U.S. patent application number 17/066259 was filed with the patent office on 2022-02-17 for cognitive computing for generating targeted offers to inactive account holders.
The applicant listed for this patent is State Farm Mutual Automobile Insurance Company. Invention is credited to Melissa Attig, Reena Batra, Puneit Dua, Elizabeth A. Flowers, Adam Mattingly, Alan Zwilling.
Application Number | 20220051292 17/066259 |
Document ID | / |
Family ID | 1000005135341 |
Filed Date | 2022-02-17 |
United States Patent
Application |
20220051292 |
Kind Code |
A1 |
Flowers; Elizabeth A. ; et
al. |
February 17, 2022 |
COGNITIVE COMPUTING FOR GENERATING TARGETED OFFERS TO INACTIVE
ACCOUNT HOLDERS
Abstract
Techniques are disclosed utilizing cognitive computing to
improve banking experiences. A user's financial account(s) and
location may be monitored to predict when a user is near a retail
store and the user has not used a particular account in a
predetermined amount of time. The techniques disclosed include
receiving a location for a user's mobile device, and determining
when the mobile device is within a predetermined threshold distance
of a retail store. The techniques include building a shopping
profile for the user based upon shopping habits for the user. The
shopping profile may be used to determine what commercial
communications should be transmitted to the user to encourage them
to utilize an inactive account to make a purchase at the retail
store when the user is within the threshold distance of the retail
store.
Inventors: |
Flowers; Elizabeth A.;
(Bloomington, IL) ; Dua; Puneit; (Bloomington,
IL) ; Zwilling; Alan; (Downs, IL) ; Mattingly;
Adam; (Normal, IL) ; Attig; Melissa;
(Bloomington, IL) ; Batra; Reena; (Alpharetta,
GA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
State Farm Mutual Automobile Insurance Company |
Bloomington |
IL |
US |
|
|
Family ID: |
1000005135341 |
Appl. No.: |
17/066259 |
Filed: |
October 8, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15498872 |
Apr 27, 2017 |
10891655 |
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17066259 |
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62436899 |
Dec 20, 2016 |
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62436883 |
Dec 20, 2016 |
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62341677 |
May 26, 2016 |
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62338749 |
May 19, 2016 |
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62338752 |
May 19, 2016 |
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62332226 |
May 5, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 30/0269 20130101;
G06Q 30/0267 20130101; G06Q 30/0261 20130101; G06Q 30/0255
20130101; G06Q 30/0226 20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02 |
Claims
1. A computer-implemented method, comprising: receiving, by one or
more processors, a set of financial data profiles associated with a
plurality of users; determining, by the one or more processors, an
inactive account time threshold, wherein the inactive account time
threshold is a particular period of time, indicative of a financial
account being inactive, determined based on one or more periods of
time separating financial transactions indicated in the set of
financial data profiles; receiving, by the one or more processors,
user financial transaction information associated with a plurality
of accounts; identifying, by the one or more processors, an
inactive account of the plurality of accounts, wherein the inactive
account is identified based on indications in the user financial
transaction information that a user has not used the inactive
account for a length of time greater than or equal to the inactive
account time threshold; receiving, by the one or more processors,
location data indicating a location of a mobile device of the user;
determining, by the one or more processors, that the location is
within a predetermined distance of a retail location of a merchant;
and based on determining that the location is within the
predetermined distance, transmitting, by the one or more
processors, a communication associated with the merchant to the
mobile device, wherein the communication indicates a reward for
using the inactive account to make a purchase with the
merchant.
2. The computer-implemented method of claim 1, wherein the
communication identifies a product offered for sale by the
merchant.
3. The computer-implemented method of claim 1, wherein the
communication indicates an amount of reward points to be awarded
for using the inactive account to make the purchase within a
specified period of time.
4. The computer-implemented method of claim 1, wherein the reward
for using the inactive account is an interest-free transaction
associated with the purchase.
5. The computer-implemented method of claim 1, wherein the one or
more processors uses a predictive modeling application to determine
the inactive account time threshold based on the one or more
periods of time.
6. The computer-implemented method of claim 1, further comprising:
generating, by the one or more processors and based on the user
financial transaction information, a shopping profile indicative of
shopping habits of the user.
7. The computer-implemented method of claim 6, further comprising:
identifying, by the one or more processors, a product offered for
sale by the merchant; and determining, by the one or more
processors and based on the shopping profile, that the product is
of interest to the user, wherein the communication is associated
with the product.
8. The computer-implemented method of claim 7, wherein the one or
more processors uses a predictive modeling application to generate
the shopping profile and to determine that the product is of
interest to the user.
9. The computer-implemented method of claim 6, further comprising:
determining, by the one or more processors and based on the
shopping profile, that the merchant is of interest to the user,
wherein the communication is transmitted based on determining that
the location is within the predetermined distance and based on
determining that the merchant is of interest to the user.
10. The computer-implemented method of claim 6, further comprising
selecting, by the one or more processors and based on the shopping
profile, the communication from a set of communications associated
with the merchant.
11. The computer-implemented method of claim 10, wherein the
shopping profile indicates at least one predicted life event
associated with the user, and the one or more processors selects
the communication based at least in part on the at least one
predicted life event.
12. A system, comprising: one or more processors; memory storing
computer-executable instructions that, when executed by the one or
more processors, cause the one or more processors to perform
operations comprising: receiving a set of financial data profiles
associated with a plurality of users; determining an inactive
account time threshold, wherein the inactive account time threshold
is a particular period of time, indicative of a financial account
being inactive, determined based on one or more periods of time
separating financial transactions indicated in the set of financial
data profiles; receiving user financial transaction information
associated with a plurality of accounts; generating, based on the
user financial transaction information, a shopping profile
indicative of shopping habits of a user; identifying an inactive
account of the plurality of accounts, wherein the inactive account
is identified based on indications in the user financial
transaction information that the user has not used the inactive
account for a length of time greater than or equal to the inactive
account time threshold; determining that a mobile device associated
with the user has entered an area defined by a geofence surrounding
a retail location of a merchant; and based on determining that the
mobile device has entered the area, transmitting a communication
associated with the merchant to the mobile device, wherein the
communication indicates a reward for using the inactive account to
make a purchase with the merchant.
13. The system of claim 12, wherein the communication identifies a
product offered for sale by the merchant and an amount of reward
points to be awarded for using the inactive account to make the
purchase of the product with the merchant.
14. The system of claim 12, wherein the operations further
comprise: identifying a product offered for sale by the merchant;
and determining, based on the shopping profile, that the product is
of interest to the user, wherein the communication is associated
with the product.
15. The system of claim 12, wherein the operations further comprise
determining, based on the shopping profile, that the merchant is of
interest to the user, wherein the communication is transmitted
based on mobile device has entered the area and based on
determining that the merchant is of interest to the user.
16. The system of claim 12, wherein the shopping profile indicates
at least one predicted life event associated with the user, and the
operations further comprise selecting the communication based at
least in part on the at least one predicted life event.
17. A non-transitory, tangible computer-readable medium storing
instructions thereon that, when executed by one or more processors,
cause the one or more processors to: receive a set of financial
data profiles associated with a plurality of users; determine an
inactive account time threshold, wherein the inactive account time
threshold is a particular period of time, indicative of a financial
account being inactive, determined based on one or more periods of
time separating financial transactions indicated in the set of
financial data profiles; receive user financial transaction
information associated with a plurality of accounts; identify an
inactive account of the plurality of accounts, wherein the inactive
account is identified based on indications in the user financial
transaction information that a user has not used the inactive
account for a length of time greater than or equal to the inactive
account time threshold; determine that a mobile device associated
with the user has entered an area defined by a geofence surrounding
a retail location of a merchant; and based on determining that the
mobile device has entered the area, transmit a communication
associated with the merchant to the mobile device, wherein the
communication indicates a reward for using the inactive account to
make a purchase with the merchant.
18. The non-transitory, tangible computer-readable medium of claim
17, wherein the communication identifies a product offered for sale
by the merchant.
19. The non-transitory, tangible computer-readable medium of claim
18, wherein the instructions further cause the one or more
processors to generate, based on the user financial transaction
information, a shopping profile indicative of shopping habits of
the user, wherein the shopping profile indicates that at least one
of the merchant or the product is of interest to the user.
20. The non-transitory, tangible computer-readable medium of claim
17, wherein the instructions further cause the one or more
processors to: predict, based on the user financial transaction
information, at least one predicted life event associated with the
user; and select, based on the at least one predicted life event,
the communication from a set of communications associated with the
merchant.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of, and claims priority
to, U.S. patent application Ser. No. 15/498,872, filed on Apr. 27,
2017, entitled "COGNITIVE COMPUTING FOR GENERATING TARGETED OFFERS
TO INACTIVE ACCOUNT HOLDERS", which claims priority to (1) U.S.
Provisional Application No. 62/338,749, entitled "Using Cognitive
Computing To Customize Loans," filed on May 19, 2016; (2) U.S.
Provisional Application No. 62/332,226, entitled "Using Cognitive
Computing To Provide a Personalized Banking Experience," filed on
May 5, 2016; (3) U.S. Provisional Application No. 62/338,752,
entitled "Using Cognitive Computing To Provide a Personalized
Banking Experience," filed on May 19, 2016; (4) U.S. Provisional
Application No. 62/341,677, entitled "Using Cognitive Computing To
Improve Relationship Pricing," filed on May 26, 2016; (5) U.S.
Provisional Application No. 62/436,899, entitled "Using Cognitive
Computing To Improve Relationship Pricing," filed on Dec. 20, 2016;
and (6) U.S. Provisional Application No. 62/436,883, entitled
"Preventing Account Overdrafts and Excessive Credit Spending,"
filed on Dec. 20, 2016, each of which is incorporated herein by
reference in its entirety.
TECHNICAL FIELD
[0002] The present disclosure relates to providing an improved
banking experience and, more particularly, to using cognitive
computing to present targeted commercial communications to inactive
account holders.
BACKGROUND
[0003] Online access to financial accounts has become commonplace,
with users now enjoying the convenience of paying bills,
transferring funds, and checking account balances from the comfort
of their own homes. Many people typically have more than one bank
account, such as a checking account and a savings account, for
example, and may move money between these accounts as needed.
[0004] Since many people have multiple financial accounts to their
name, they may not always be aware of how often they use each
account. It can be difficult for individuals to know when is the
best time to use one particular account over another account.
Similarly, companies that provide financing may wish to increase
the frequency with which their customers use their products and
services. Offering rewards is a common way in which these companies
attempt to entice customers to use their products or services.
BRIEF SUMMARY
[0005] In one aspect, a computer-implemented method for presenting
targeted commercial communications to users via their mobile
device, the method comprising: (1) generating, via one or more
processors, a shopping profile associated with shopping habits of a
user; (2) receiving, via the one or more processors, a location of
a user mobile device; (3) determining, via the one or more
processors, that the location of the user mobile device is within a
predetermined distance of a physical vendor location associated
with a vendor; (4) identifying, via the one or more processors,
that the user has not used a account for a predetermined amount of
time, wherein the account has a transaction dataset comprising data
indicative of transaction particulars; (5) selecting, via the one
or more processors, one or more relevant offerings being offered
for sale by the vendor; and/or (6) when the user has not used the
account for the predetermined amount of time, the user mobile
device is within the predetermined distance of the physical vendor
location, and the vendor has one or more relevant offerings on
sale, transmitting, via the one or more processors, a commercial
communication to the user's mobile device, wherein the commercial
communication includes identification of the one or more relevant
offerings, and a rewards communication to receive rewards if the
inactive account is used to complete a purchase of the one or more
relevant offerings at the vendor.
[0006] In another aspect, a system configured to present targeted
commercial communications to users via their mobile device, the
computer system comprising one or more local or remote processors,
servers, sensors, and/or transceivers configured to: (1) build a
shopping profile associated with shopping habits of a user; (2)
receive a location of a user mobile device; (3) determine that the
location of the user mobile device is within a predetermined
distance of a physical vendor location associated with a vendor;
(4) determine that the user has not used a account for a
predetermined amount of time; (5) determine that the vendor is
currently offering one or more relevant offerings for sale; and/or
(6) when the user has not used the account for the predetermined
amount of time, the user mobile device is within the predetermined
distance, and the vendor has one or more relevant offerings on
sale, transmit a commercial communication to the user's mobile
device, wherein the commercial communication includes
identification of the one or more relevant offerings, and a reward
communication to receive rewards if the account is used to complete
a purchase of the one or more relevant offerings at the vendor.
[0007] Advantages will become more apparent to those of ordinary
skill in the art from the following description of the preferred
aspects, which have been shown and described by way of
illustration. As will be realized, the present aspects may be
capable of other and different aspects, and their details are
capable of modification in various respects. Accordingly, the
drawings and description are to be regarded as illustrative in
nature and not as restrictive.
BRIEF DESCRIPTION OF DRAWINGS
[0008] FIG. 1 is a block diagram of an exemplary banking system 100
in accordance with one aspect of the present disclosure;
[0009] FIG. 2 illustrates exemplary financial data profiles 200 in
accordance with one aspect of the present disclosure;
[0010] FIG. 3 illustrates a graphical representation of an
exemplary tracked financial account balance 300 in accordance with
one aspect of the present disclosure;
[0011] FIG. 4 illustrates an exemplary computer-implemented method
flow 400 in accordance with one aspect of the present
disclosure;
[0012] FIG. 5 illustrates an exemplary use case 500 in accordance
with one aspect of the present disclosure;
[0013] FIG. 6 illustrates an exemplary computer-implemented method
flow 600 in accordance with one aspect of the present
disclosure;
[0014] FIG. 7 illustrates an exemplary computer-implemented method
flow 700 for generating targeted offers based upon customer
shopping profiles;
[0015] FIG. 8 illustrates an exemplary use case 800 in accordance
with one aspect of the present disclosure;
[0016] FIG. 9 illustrates an exemplary computer-implemented method
flow 900 in accordance with one aspect of the present
disclosure;
[0017] FIG. 10 illustrates an exemplary use case 1000 in accordance
with one aspect of the present disclosure;
[0018] FIG. 11 illustrates an exemplary customer interaction system
1100 in accordance with one aspect of the present disclosure;
[0019] FIG. 12 illustrates an exemplary computer-implemented method
1200 for monitoring an amount of customer interactions using
self-service communication channels, as opposed to full-service
communication channels;
[0020] FIG. 13 illustrates an exemplary computer-implemented method
flow 1300 in accordance with one aspect of the present disclosure;
and
[0021] FIG. 14 illustrates an exemplary use case 1400 in accordance
with one aspect of the present disclosure.
[0022] The Figures depict aspects of the present invention for
purposes of illustration only. One skilled in the art will readily
recognize from the following discussion that alternate aspects of
the structures and methods illustrated herein may be employed
without departing from the principles of the invention described
herein.
DETAILED DESCRIPTION
[0023] The present aspects relate to, inter alia, using cognitive
computing and/or predictive modeling to perform various actions
when a financial account overdraft is anticipated. To do so, data
may be collected and stored by one or more back-end components,
which may include a user's financial account balances, the user's
demographic data, and/or location data associated with the user's
client device. Data may also be collected for other users in a
particular region, which may include demographic data and/or the
spending habits of the other user's at various retailers. Once a
user's proximity to a particular retailer is detected, the one or
more back-end components may leverage the user's demographic data
and/or that of other users to calculate a predicted transaction
amount that the user is statistically most likely to spend at that
particular retailer. This predicted transaction amount may then be
compared to the user's current account balance to determine whether
spending the predicted transaction amount would result in an
overdraft. If so, the various actions may include transferring
money to cover the anticipated expenditure and/or sending a
notification to the client device.
[0024] Furthermore, the present aspects discussed herein may relate
to, inter alia, using cognitive computing (and/or machine learning)
and/or predictive modeling to automatically transfer funds between
accounts to optimize accrued interest. To accomplish this, data may
be collected by one or more back-end computing devices, which may
include a user's financial transactions associated with various
financial accounts and/or the balances of those accounts. Using
this data, an average daily account balance may be calculated for
one or more of the accounts, such as a checking account, for
example. A mathematical model may be implemented that identifies
funds transfer conditions, such as detecting a surplus of funds
when a user's current account balance exceeds an average daily
account balance by a threshold value. When a surplus is detected,
the one or more back-end components may then calculate a number of
days from which a statistical probability exceeds a threshold
likelihood of the balance of the user's first financial account
maintaining the surplus. The one or more back-end components may
transfer money from the first financial account to the second
financial account for the calculated number of days in an amount
equal to the difference between the threshold value and the average
daily account balance.
System Overview
[0025] FIG. 1 is a block diagram of an exemplary banking system 100
in accordance with one aspect of the present disclosure. In the
present aspect, banking system 100 may include one or more client
devices 102, a personalized banking engine 120, one or more
financial institutions 150, one or more retail stores 160, and/or a
communication network 116. Banking system 100 may include
additional, less, or alternate actions, including those discussed
elsewhere herein.
[0026] For the sake of brevity, banking system 100 is illustrated
as including a single client device 102, a single personalized
banking engine 120, two financial institutions 150, a single retail
store 160, and a single communication network 116. However, the
embodiments described herein may include any suitable number of
such components. For example, personalized banking engine 120 may
communicate with several client devices 102, each of which being
operated by a respective user, to track his or her location and/or
receive other types of information, as further discussed herein. To
provide another example, personalized banking engine 120 may
monitor data received from one or more client devices such that the
spending habits of each user may be assessed for several retail
stores.
[0027] Retail store 160 may be any suitable type of store in which
merchandise of any suitable kind is sold. Retail store 160 may
encompass physical brick-and-mortar stores as well as virtual
stores, such as those accessible via online shopping over the
Internet. Although the various aspects described herein reference a
physical location, it will be understood that this location may
refer to a particular type of location in accordance with the type
of retail store that a user is visiting. For example, in the event
that retail store 160 is a physical store, personalized banking
engine 120 may receive geographic location coordinates from client
device 102. To provide another example, in the event that retail
store 160 is an online store, personalized banking engine 120 may
receive web addresses, Internet Protocol (IP) addresses, etc., to
identify the particular retailer's "location" as a unique online
retailer.
[0028] In the present aspects, retail store 160 may be associated
with a boundary 161, which may represent any suitable boundary to
appropriately identify a physical location of retail store 160. For
example, boundary 161 may represent a geofence including a range of
geographic coordinates (e.g., latitude and longitude) such that,
when a location of client device 102 crosses boundary 161, client
device 102 and/or personalized banking engine 120 may determine
that a user associated with client device 102 has also crossed
boundary 161, and is therefore proximate to retail store 160.
[0029] Communication network 116 may be configured to facilitate
communications between one or more client devices 102, one or more
financial institutions 150, and/or personalized banking engine 120
using any suitable number of wired and/or wireless links, such as
links 117.1-117.3, for example. For example, communication network
116 may include any suitable number of nodes, additional wired
and/or wireless networks that may facilitate one or more landline
connections, Internet service provider (ISP) backbone connections,
satellite links, a public switched telephone network (PSTN),
etc.
[0030] In the present aspect, communication network 116 may be
implemented as a local area network (LAN), a metropolitan area
network (MAN), a wide area network (WAN), or any suitable
combination of local and/or external network connections. To
provide further examples, communications network 106 may include
wired telephone and cable hardware, satellite, cellular phone
communication networks, base stations, macrocells, femtocells, etc.
In the present aspect, communication network 116 may provide one or
more client devices 102 with connectivity to network services, such
as Internet services, for example, and/or support application
programming interface (API) calls between one or more client
devices 102, one or more financial institutions 150, and/or
personalized banking engine 120.
[0031] Client device 102 may be configured to communicate using any
suitable number and/or type of communication protocols, such as
Wi-Fi, cellular, BLUETOOTH, NFC, RFID, etc. For example, client
device 102 may be configured to communicate with communication
network 116 using a cellular communication protocol to send data to
and/or receive data from one or more financial institutions 150
and/or personalized banking engine 120 via communication network
116 using one or more of radio or radio frequency links 117.1-117.3
or wireless communication channels.
[0032] In various aspects, client device 102 may be implemented as
any suitable communication device. For example, client device 102
may be implemented as a user equipment (UE) and/or client device,
such as a smartphone, for example. To provide additional examples,
client device 102 may be implemented as a personal digital
assistant (PDA), a desktop computer, a tablet computer, a laptop
computer, a wearable electronic device, etc.
[0033] As further discussed below, data transmitted by client
device 102 to one or more financial institutions 150 and/or
personalized banking engine 120 may include, for example, any
suitable information used by personalized banking engine 120 to
track the location of client device 102, to track account balances
of one or more users associated with client device 102, to
anticipate an overdraft of one or more financial accounts
associated with one or more financial institutions 150, to
calculate if and when to instruct one or more financial
institutions 150 to transfer money between a user's various
financial accounts and how much money to transfer, etc.
[0034] Furthermore, data received by client device 102 from one or
more financial institutions 150 and/or personalized banking engine
120 may include any suitable information used to notify the user of
a suggested course of action and/or that an automated course of
action has been taken regarding the user's financial accounts. For
example, if an anticipated overdraft of one or more accounts is
detected, then client device 102 may display a notification
transmitted via personalized banking engine 120 of the account's
current balance, a calculated transaction amount that is predicted
to be spent at retailer 160, a calculated overdraft amount, etc. To
provide another example, in the event that a surplus of funds is
detected in one or more of the user's financial accounts at one or
more financial institutions 150, the user may receive an indication
that a transfer is pending, a transfer has been made, the details
of the transfer such as how long until funds are transferred back
and/or the amount of the transfer, etc.
[0035] Personalized banking engine 120 may be configured to
communicate using any suitable number and/or type of communication
protocols, such as Wi-Fi, cellular, BLUETOOTH, NFC, RFID, etc. For
example, personalized banking engine 120 may be configured to
communicate with client device 102 using a cellular communication
protocol to send data to and/or receive data from client device 102
via communication network 116 using wireless communication or data
transmission over one or more of radio links 117.1-117.3 (or other
wireless communication channels). For example, as further discussed
herein, personalized banking engine 120 may receive location data
from client device 102 indicative of a current geographic location
of client device 102 (e.g., geographic coordinates), a retail
website address that client device 102 is currently navigating,
demographic information associated with client device 102, etc. To
provide another example, personalized banking engine 120 may
transmit notifications, messages, etc., to client device 102, which
may in turn be displayed via client device 102.
[0036] To provide yet another example, personalized banking engine
120 may be configured to communicate with one or more financial
institutions 150 to send data to, and/or receive data from, one or
more financial institutions 150. For example, a user may set up
account permissions to authorize personalized banking engine 150 to
access financial data from one or more user accounts held at one or
more financial institutions and/or to make withdrawals, deposits,
and transfers on the user's behalf. Once such authorizations are
set up, personalized banking engine 120 may be configured to
actively request funds transfers to one or more financial
institutions 150, and/or to track and/or manage funds held in one
or more user accounts at one or more financial institutions
150.
Detailed Operation of Banking System
[0037] In the present aspect, client device 102 may include one or
more processors 104, a communication unit 106, a user interface
108, a display 110, a location acquisition unit 112, and a memory
unit 114.
[0038] Communication unit 106 may be configured to facilitate data
communications between client device 102 and one or more of
communication network 116, financial institutions 150, and/or
personalized banking engine 120 in accordance with any suitable
number and/or type of communication protocols. Communication unit
106 may be configured to facilitate data communications based upon
the particular component and/or network with which client device
102 is communicating.
[0039] Such communications may facilitate the transmission of
location data and/or other data from client device 102 that is
utilized by personalized banking engine 120 to provide enhanced
banking or other financial services for a user associated with
client device 102, as further discussed herein. In the present
aspects, communication unit 106 may be implemented with any
suitable combination of hardware and/or software to facilitate this
functionality. For example, communication unit 106 may be
implemented with any suitable number of wired and/or wireless
transceivers, network interfaces, physical layers (PHY), ports,
antennas, etc.
[0040] User interface 108 may be configured to facilitate user
interaction with client device 102. For example, user interface 108
may include a user-input device such as an interactive portion of
display 110 (e.g., a "soft" keyboard displayed on display 110), an
external hardware keyboard configured to communicate with client
device 102 via a wired or a wireless connection (e.g., a BLUETOOTH
keyboard), an external mouse, or any other suitable user-input
device.
[0041] Display 110 may be implemented as any suitable type of
display that may facilitate user interaction, such as a capacitive
touch screen display, a resistive touch screen display, etc. In
various aspects, display 110 may be configured to work in
conjunction with user-interface 108 and/or one or more processors
104 to detect user inputs upon a user selecting a displayed
interactive icon or other graphic, to identify user selections of
objects displayed via display 110, to display notifications of
suggested actions to take and/or actions that have already been
taken regarding a user's financial accounts at one or more
financial institutions 150, etc.
[0042] Location acquisition unit 112 may be implemented as any
suitable device configured to generate location data indicative of
a current geographic location of client device 102. In one aspect,
location acquisition unit 102 may be implemented as a satellite
navigation receiver that works with a global navigation satellite
system (GNSS), such as the global positioning system (GPS)
primarily used in the United States, the GLONASS system primarily
used in the Soviet Union, the BeiDou system primarily used in
China, and/or the Galileo system primarily used in Europe.
[0043] Location acquisition unit 112 and/or one or more processors
104 may be configured to receive navigational signals from one or
more satellites and to calculate a geographic location of client
device 102 using these signals. Location acquisition unit 112 may
include one or more processors, controllers, or other computing
devices and memory to calculate the geographic location of client
device 102 without one or more processors 104. Alternatively,
location acquisition unit 112 may utilize components of one or more
processors 104. Thus, one or more processors 104 and location
acquisition unit 112 may be combined or be separate or otherwise
discrete elements.
[0044] One or more processors 104 may be implemented as any
suitable type and/or number of processors, such as a host processor
for the relevant device in which client device 102 is implemented,
for example. One or more processors 104 may be configured to
communicate with one or more of communication unit 106, user
interface 108, display 110, location acquisition unit 112, and/or
memory unit 114 to send data to and/or to receive data from one or
more of these components.
[0045] For example, one or more processors 104 may be configured to
communicate with memory unit 114 to store data to and/or to read
data from memory unit 114. In accordance with various embodiments,
memory unit 114 may be a computer-readable non-transitory storage
device, and may include any combination of volatile memory (e.g., a
random access memory (RAM)) and/or non-volatile memory (e.g.,
battery-backed RAM, FLASH, etc.). In one embodiment, memory unit
114 may be configured to store instructions executable by one or
more processors 104. These instructions may include
machine-readable instructions that, when executed by one or more
processors 104, cause one or more processors 104 to perform various
acts.
[0046] In one aspect, virtual personalized banker application 115
is a portion of memory unit 114 configured to store instructions,
that when executed by one or more processors 104, cause one or more
processors 104 to perform various acts in accordance with
applicable embodiments as described herein. For example,
instructions stored in virtual personalized banker application 115
may facilitate one or more processors 104 performing functions such
as determining when client device 102 has crossed boundary 161 or
is otherwise proximate to retail store 160, periodically
transmitting the location of client device 102 (or causing location
acquisition unit 112 to do so) as part of a running background
process, sending information to one or more financial institutions
150 and/or personalized banking engine 120, receiving information
from one or more financial institutions 150 and/or personalized
banking engine 120, displaying notifications and/or other
information using data received via one or more financial
institutions 150 and/or personalized banking engine 120, etc.
[0047] In some aspects, virtual personalized banker application 115
may reside in memory unit 114 as a default application bundle that
may be included as part of the operating system (OS) utilized by
client device 102. But in other aspects, virtual personalized
banker application 115 may be installed on client device 102 as one
or more downloads, such as an executable package installation file
downloaded from a suitable application store via a connection to
the Internet.
[0048] For example, virtual personalized banker application 115 may
be stored in any suitable portions of memory unit 114 upon
installation of a package file downloaded in such a manner.
Examples of package download files may include downloads via the
iTunes Store, the Google Play Store, the Windows Phone Store, a
package installation file downloaded from another computing device,
etc. Once downloaded, virtual personalized banker application 115
may be installed on client device 102 as part of an installation
package. Upon installation of virtual personalized banker
application 115, memory unit 114 may store executable instructions
that, when executed by one or more processors 104, cause client
device 102 to implement the various functions of the aspects as
described herein.
[0049] For example, upon installing and launching virtual
personalized banker application 115 on client device 102, a user
may be prompted to enter login information and/or complete an
initial registration process to create a user profile with a party
associated or otherwise affiliated with personalized banking engine
120, which may be the same party or a different party than that
affiliated with one or more financial institutions 150, as further
discussed below. The user may initially create a user profile upon
first launching the application, through a registration process via
a website, over the phone, etc. This user profile may include, for
example, the customer's demographic information or any suitable
information that may be useful in facilitating various portions of
the aspects as described herein.
[0050] This registration process may additionally or alternatively
include, for example, obtaining the user's affirmative consent or
permission to track his or her location, to track and/or receive
the user's financial account data including financial transactions
and/or account balances associated with the user's financial
accounts at one or more financial institutions 150, to opt-in to a
system whereby the user's spending habits are tracked and/or
spending data at various retailers is collected, to provide
authorization (e.g., online login credentials) to access the user's
accounts held at one or more financial institutions 150, etc.
[0051] In the present aspects, virtual personalized banker
application 115 may provide different levels of functionality based
upon options selected by a user and/or different implementations of
virtual personalized banker application 115. For example, in some
aspects, virtual personalized banker application 115 may facilitate
client device 102 working in conjunction with one or more financial
institutions 150 and/or personalized banking engine 120 to predict
account overdrafts and perform various actions accordingly. To
provide another example, other aspects include virtual personalized
banker application 115 facilitating client device 102 working in
conjunction with one or more financial institutions 150 and/or
personalized banking engine 120 to transfer funds between various
financial accounts held at one or more financial institutions 150
to optimize interest accrual. To provide yet another example, other
aspects may include virtual personalized banker application 115
facilitating both of the aforementioned functionalities and/or
other functionalities as further discussed herein.
[0052] One or more financial institutions 150 may include any
suitable number and/or type of financial institutions that hold
and/or are associated with various financial accounts. For example,
one or more financial institutions 150 may include banks,
creditors, and/or brokers. One or more users (e.g., a user
associated with client device 102) may hold one or more accounts
with the various institutions such as checking accounts, savings
accounts, credit accounts, charge accounts, money market accounts,
brokerage accounts, etc. These accounts may be held at a single
institution or spread out across several different financial
institutions.
[0053] In one aspect, financial accounts held at one or more
financial institutions 150 may be accessible via a secure
connection to communication network 116, for example, by client
device 102 and/or personalized banking engine 120. For example, one
or more financial institutions 150 may provide online banking
services that allow a user to access his accounts using client
device 102 and/or another suitable computing device. Upon receipt
of a valid authenticated request for financial data, one or more
financial institutions 150 may transmit financial data to client
device 102 and/or personalized banking engine 120, which is further
discussed below. Examples of the financial data transmitted by one
or more financial institutions 150 may include financial
transaction data indicating previous credits and debits to a user's
accounts, a current account balance, spending data such as the
time, amount, and specific merchant for which previous account
debits and/or charges were made, etc.
[0054] Personalized banking engine 120 may include any suitable
number of components configured to receive data from and/or send
data to one or more of client devices 102 and/or one or more
financial institutions 150 via communication network 116 using any
suitable number of wired and/or wireless links. In various
embodiments, personalized banking engine 120 may constitute a
portion of (or the entirety of) one or more back-end components,
and may be configured (alone or in conjunction with other back-end
components) to execute one or more applications to perform one or
more functions associated with the various aspects as discussed
herein.
[0055] For example, as shown in FIG. 1, personalized banking engine
120 may communicate with one or more external computing devices,
such as servers, databases, database servers, web servers, etc. The
present aspects include personalized banking engine 120 working in
conjunction with any suitable number and/or type of back-end
components to facilitate the appropriate functions of the aspects
as described herein.
[0056] For example, personalized banking engine 120 may be
implemented as any suitable number of servers that are configured
to access data from one or more additional data sources and/or
store data to one or more storage devices. For example, as shown in
FIG. 1, additional data sources 170 may represent data that is made
available to personalized banking engine 120, such as third-party
data providers or other data sources in addition to and including
one or more financial institutions 150. This additional data may
facilitate the execution of one or more cognitive computing and/or
predictive modeling algorithms via personalized banking engine 120
to calculate the statistical probability that an overdraft is about
to occur, whether a particular user account has a surplus in funds,
if and when to transfer some of an account surplus to another
account with a better interest rate, the amount of such transfers,
etc.
[0057] To provide some illustrative examples, additional data
sources 170 may include data mined from social media and/or a
user's web browsing habits, data provided by various retailers,
demographic data associated with other users in a particular region
and/or associated with a particular retailer, income levels of
various users, where different users commonly shop, assets
associated with various users (such as the cars each user owns,
etc.), mortgage loan information associated with various users, how
much users typically spend at various retailers, etc. Any portion
of this data may be used as input to one or more cognitive
computing and/or predictive modeling algorithms to perform such
calculations, some examples of which are further discussed
below.
[0058] To provide another example, personalized banking engine 120
may be implemented as any suitable number of servers that are
configured to generate and/or store financial data profiles 180.
Financial data profiles 180 may include, for example, an
aggregation of spending data, financial data, demographic data,
etc., correlated to one or more users and accessed via personalized
banking engine 120 data. This data may include the aforementioned
data received via one or more financial institutions 150 and/or
other data including additional data sources 170, for example.
[0059] To provide an illustrative example, financial data profiles
180 may include a number of user profiles organized in accordance
with suitable types of information to uniquely identify each
particular user, so that each user may later be matched to his or
her financial data profile stored in financial data profiles 180.
For example, financial data profiles 180 may store a username that
is used by one or more users in accordance with virtual
personalized banker application 115, a first and last name of each
user, etc. These financial data profiles are discussed in further
detail below.
[0060] In the present aspect, personalized banking engine 120 may
include one or more processors 122, a communication unit 124,
and/or a memory unit 126. One or more processors 122, communication
unit 124, and memory unit 126 may be substantially similar
implementations of, and perform substantially similar functions as,
one or more processors 104, communication unit 106, and memory unit
114, respectively, of client device 102. Therefore, only the
differences between these components will be further discussed
herein.
[0061] Of course, differences between components of personalized
banking engine 120 and client device 102 may be owed to differences
in device implementation rather than the functions performed by
each individual component. For example, if personalized banking
engine 120 is implemented as a server whereas client device 102 is
implemented as a personal computing device, one or more processors
122 may have more processing power (e.g., a faster processor, more
cores, etc.) than one or more processors 104, although one or more
processors 122 may perform similar functions as one or more
processors 104 (e.g., executing instructions stored in memory to
perform various acts, processing data, etc.).
[0062] Again, in various aspects, personalized banking engine 120
may acquire data from various sources. Some of these sources may
include data from secure connections or may otherwise require
secured or authorized access. For example, one or more financial
institutions 150 may provide access to one or more users' financial
account data via any suitable authentication technique, such as via
a secure connection, password authentication, public and/or private
key exchanges, biometric identification, etc.
[0063] In the present aspects, personalized banking engine 120 may,
when appropriate, implement any suitable techniques to obtain
information in a legal and technically feasible manner. For
example, a user may setup an account and/or profile with a third
party associated with personalized banking engine 120. The user may
then opt in to data collection via the various data sources that
are to be collected via personalized banking engine 120, such as
browsing habits, social media, etc. The user may additionally or
alternatively provide authentication information for each account
and/or data source for which data is to be accessed, collected,
tracked, monitored, etc.
Cognitive Computing
[0064] Cognitive computing may refer to systems that learn at
scale, reason with purpose, and/or interact with humans naturally.
Rather than being explicitly programmed, cognitive computing
systems may learn and reason from their interactions and from their
experiences with their environment. As opposed to traditional
computing systems, which are deterministic, cognitive computing
systems are probabilistic. In other words, cognitive computing
systems generate not just answers to numerical problems, but
hypotheses, reasoned arguments, and recommendations about more
complex and meaningful bodies of data. Cognitive computing systems
may advantageously interpret and utilize data that is typically
referred to as being unstructured in nature. This allows such
systems to keep pace with the volume, complexity, and
unpredictability of information and systems in the modern world. To
do so, cognitive computing systems attempt to augment the reasoning
and thought processes of the human brain.
[0065] Therefore, in various aspects, personalized banking engine
120 may be implemented as a computing device, or a constituent part
of one or more computing devices, that is/are configured to process
data in accordance with one or more cognitive computing techniques
and/or machine learning techniques. For example, personalized
banking engine 120 may be implemented as one or more nets or nodes
of an artificial neural network system and/or other suitable system
that models and/or mimics the reasoning and processing of the human
brain. Thus, cognitive computing and predictive modeling
application 127 may include one or more machine learning
algorithms, pattern recognition techniques, code, logic, and/or
instructions to facilitate the behavior, functionality, and/or
processing of a cognitive computing system.
[0066] In certain embodiments, personalized banking engine 120 may
utilize machine learning techniques. The machine learning
techniques may be cognitive learning, deep learning, combined
learning, heuristic engines and algorithms, and/or pattern
recognition techniques. For instance, a processor (such as the
processor 104 of client device 102, or the processor 122 of the
personalized banking engine 120) or a processing element may be
trained using supervised or unsupervised machine learning, and the
machine learning program may employ a neural network, which may be
a convolutional neural network, a deep learning neural network, or
a combined learning module or program that learns in two or more
fields or areas of interest. Machine learning may involve
identifying and recognizing patterns in existing data in order to
facilitate making predictions for subsequent data. Models may be
created based upon example inputs in order to make valid and
reliable predictions for novel inputs.
[0067] Additionally or alternatively, the machine learning programs
may be trained by inputting sample data sets or certain data into
the programs, such as image, client device, insurer database,
and/or third-party database data, including data from a user's
financial data profile 180. For example, the data from a user's
financial data profile 180 may be provided by a service provider
the user has an agreement with such as one of the financial
institutions 150. The machine learning programs may utilize deep
learning algorithms that may be primarily focused on pattern
recognition, and may be trained after processing multiple examples.
The machine learning programs may include Bayesian program learning
(BPL), voice recognition and synthesis, image or object
recognition, optical character recognition, and/or natural language
processing--either individually or in combination. The machine
learning programs may also include natural language processing,
semantic analysis, automatic reasoning, and/or machine
learning.
[0068] In supervised machine learning, a processing element may be
provided with example inputs and their associated outputs, and may
seek to discover a general rule that maps inputs to outputs, so
that when subsequent novel inputs are provided the processing
element may, based upon the discovered rule, accurately predict the
correct output. In unsupervised machine learning, the processing
element may be required to find its own structure in unlabeled
example inputs. In one embodiment, machine learning techniques may
be used to extract the relevant data for one or more client device
details, user request or login details, user device sensors,
geolocation information, image data, an insurer database, a
third-party database, and/or other data.
[0069] In one embodiment, a processing element (and/or machine
learning or heuristic engine or algorithm discussed herein) may be
trained by providing it with a large sample of financial data with
known characteristics or features, such as historical financial
data profiles. Based upon these analyses, the processing element
may learn how to identify characteristics and patterns that may
then be applied to analyzing data relevant to the operation of the
personalized banking engine 120, device sensors, geolocation
information, image data, an insurer database, a third-party
database, and/or other data. For example, the processing element
may learn, with the user's permission or affirmative consent, to
identify a user's spending habits, or spending habits for a
population of users, and provide to the personalized banking engine
120 any learned insights.
[0070] In the present aspects, cognitive computing and predictive
modeling application 127 may include any suitable combination of
functions as discussed herein. For example, as shown in FIG. 1,
cognitive computing and predictive modeling application 127
includes a data aggregation module 129, a spending prediction
module 131, and an interest optimization module 133. These modules
are for illustrative purposes, and represent some of the example
functionality that may be performed by personalized banking engine
120 in accordance with a cognitive computing-based system. However,
some aspects include cognitive computing and predictive modeling
application 127, including additional, less, or alternate actions,
such as those discussed elsewhere herein. Furthermore, some aspects
include cognitive computing and predictive modeling application 127
implementing traditional, non-cognitive computing processes.
[0071] In one aspect, data aggregation module 129 is a portion of
memory unit 126 configured to store instructions, that when
executed by one or more processors 122, cause one or more
processors 122 to perform various acts in accordance with
applicable embodiments as described herein. In the present aspects,
instructions stored in data aggregation module 129 may facilitate
one or more processors 122 performing functions such as mining data
for any suitable number of users. For example, instructions stored
in data aggregation module 129 may facilitate personalized banking
engine 120 providing the requested authorization to one or more
financial institutions 150 to receive financial data from these
institutions, such as a user's current and/or past account
balances, financial transactions that impact the user's account
balances, etc. To provide another example, instructions stored in
data aggregation module 129 may facilitate personalized banking
engine 120 accessing one or more additional data sources (e.g., via
database 170) to receive data from one or more third party data
mining providers, to receive browsing history data associated with
one or more users, to receive demographic data and/or household
income data for one or more users, etc.
[0072] To provide yet another example, instructions stored in data
aggregation module 129 may facilitate personalized banking engine
120 receiving spending data from one or more financial institutions
150 (e.g., a credit card company) and/or participating retailers
(e.g., retailer 160, online retailers, etc.), where the spending
data may indicate how much money one or more users spent at various
retailers, where each retailer was located, and/or what was
purchased. The spending data may come from any suitable source in
addition to, or as an alternative to, one or more financial
institutions 150 and/or participating retailers. For example, the
spending data may be received from client device 102 when a user
uses client device 102 to make purchases (e.g., via a mobile device
payment).
[0073] In the present aspects, instructions stored in data
aggregation module 129 may facilitate personalized banking engine
120 to aggregate received data and to organize this data into one
or more financial profiles. These financial profiles may be
associated with each user for which data is received, and organized
such that data contained as part of each profile may be associated
with each unique user. For example, each user's username and/or
other suitable identifying information may be stored as part of a
financial profile that includes an aggregation of all data received
for that particular user from one or more data sources, as
previously discussed. The details of the financial profiles are
further discussed herein with reference to FIG. 2.
[0074] In one aspect, spending prediction module 131 is a portion
of memory unit 126 configured to store instructions that, when
executed by one or more processors 122, cause one or more
processors 122 to perform various acts in accordance with
applicable aspects as described herein. In the present aspects,
instructions stored in spending prediction module 131 may
facilitate one or more processors 122 performing functions such as
calculating a statistical likelihood that a user will spend money
at a particular retailer in an amount that may result in an account
overdraft. To do so, aspects include personalized banking engine
120 accessing and/or receiving one or more inputs, weighting these
inputs as part of a weighting function, and calculating, as an
output of the weighting function, a statistical likelihood that the
user will spend a certain amount of money resulting in an
overdraft. The details of these calculations are further discussed
below.
[0075] For example, instructions stored in spending prediction
module 131 may facilitate one or more processors 122 tracking a
current account balance for a user's financial account utilizing
financial account data that is received via one or more financial
institutions 150 and/or stored as part of the user's profile in
financial data profiles 180. Furthermore, personalized banking
engine 120 may determine the location of client device 102 and/or
one or more websites visited by a user via client device 102 to
attempt to anticipate whether a user is about to spend money that
would result in a reduction of funds.
[0076] For example, personalized banking engine 120 may
periodically compare location data received via client device 102
to determine whether client device 102 (and thus a user associated
with client device 102) is within a threshold distance or otherwise
proximate to a particular retailer (e.g., client device 102 has
crossed a geofence associated with boundary 161 and therefore
proximate to retailer 160). If so, personalized banking engine 120
may use this as a trigger condition to begin calculating a
statistical likelihood of what amount, if any, the user is likely
to spend at that retailer.
[0077] To provide another example, personalized banking engine 120
may periodically compare IP addresses and/or websites received via
client device 102 to determine whether client device 102 (and thus
a user associated with client device 102) is currently visiting a
particular online retailer. This may be determined, for example, by
accessing database 170, which may contain a list of known online
retailers and their associated IP addresses or other uniquely
identifiable information. If so, personalized banking engine 120
may use this as a trigger condition to begin calculating a
statistical likelihood of what amount, if any, the user is likely
to spend at that online retailer.
[0078] Furthermore, personalized banking engine 120 may not only
calculate a predicted transaction amount, but the most likely
account that the user will use to do so. Because personalized
banking engine 120 may have access to the user's financial account
data, such as current balances of checking accounts and/or credit
card accounts, some aspects may include instructions stored in
spending prediction module 131 that facilitate personalized banking
engine 120 determining whether this amount, if actually spent,
would result in one or more negative financial outcomes for the
user.
[0079] To provide an illustrative example, a statistical likelihood
may be calculated that exceeds a threshold likelihood (e.g., 75%)
that the user will spend a particular amount of money at the
retailer, drawing funds from his checking account to do so. In this
scenario, personalized banking engine 120 may calculate the effect
of such a potential transaction on the user's current checking
account balance. In the event that a potential transaction of the
calculated predicted spending amount would cause an overdraft, then
personalized banking engine 120 may transmit a notification to
client device 102 indicating that a potential overdraft may
occur.
[0080] To provide another illustrative example, personalized
banking engine 120 may calculate the effect of a potential
transaction on the user's credit card balance. In the event that a
potential transaction of the calculated predicted spending amount
would cause the user's credit card balance to exceed a maximum
limit, then personalized banking engine 120 may transmit a
notification to client device 102 indicating this.
[0081] In various aspects, spending prediction module 131 may
facilitate one or more processors 122 performing active and/or
passive techniques to attempt to rectify one or more potential
negative financial outcomes for the user. For example, the
aforementioned notifications may act as passive techniques to
inform the user, via client device 102, that a potential overdraft
may occur, that a maximum credit card limit may be exceeded, etc.
These notification techniques may include, for example, pushing a
notification to client device 102, sending an email message to
client device 102, sending a text message notification to client
device 102, and/or any other suitable technique(s).
[0082] Additionally or alternatively, spending prediction module
131 may facilitate one or more processors 122 actively avoiding one
or more potential negative financial outcomes for the user. For
example, in the event that a potential credit card overdraft is
detected, personalized banking engine 120 may communicate with one
or more financial institutions 150 to request a funds transfer from
one of the user's other financial accounts to cover the calculated
predicted transaction amount. For example, personalized banking
engine 120 may request a transfer of user funds from a savings
account to a checking account in an amount equal to the calculated
predicted transaction amount (or within a threshold, e.g., 10%
greater than the calculated predicted transaction amount). In this
way, personalized banking engine 120 may actively monitor user
activity and behavior and attempt to predict and rectify potential
issues before they actually occur.
[0083] In one aspect, interest optimization module 133 is a portion
of memory unit 126 configured to store instructions, that when
executed by one or more processors 122, cause one or more
processors 122 to perform various acts in accordance with
applicable aspects as described herein. In the present aspects,
instructions stored in interest optimization module 133 may
facilitate one or more processors 122 performing functions such as
attempting to optimize accrued interest among several of a user's
financial accounts. For example, personalized banking engine 120
may request a transfer of funds between a user's financial accounts
when a surplus of funds is detected in a lower interest-bearing
account.
[0084] The details of the calculations that are performed to
determine when funds should be transferred, the amount, and for how
long, are further discussed below with reference to FIG. 3. As a
general example, however, the present aspects include instructions
stored in interest optimization module 133 facilitating one or more
processors 122 calculating an average daily account balance
associated with one of a user's financial accounts. This data may
be accessed, for example, from the financial account data that is
associated with the user's financial data profile and/or received
via communications with one or more financial institutions 150.
Based upon the previous account activity, such as deposit
frequencies and previous day-to-day account balances, an average
daily account balance may be calculated for one or more user
accounts.
[0085] In the present aspects, a surplus of funds may be detected
when a current account balance exceeds the calculated average daily
account balance by a threshold value (e.g., by 10%, 25%, 50%,
etc.). Once a surplus is detected, one or more processors 122 may
calculate a number of days from the time the surplus is detected
for which a statistical probability exceeds a threshold likelihood
of the balance of the user's financial account remaining in excess
of the average daily account balance. In other words, a likelihood
may be calculated that the surplus may remain for some period of
time in excess of the average daily account balance. If this
likelihood exceeds a threshold likelihood (e.g., 80%), then one or
more processors 122 may request a transfer of funds from the
financial account with the surplus to another, higher
interest-bearing financial account for the calculated number of
days in an amount equal to a difference between the threshold value
and the average daily account balance. In this way, personalized
banking engine 120 may actively monitor user account activity and
attempt to optimize accrued interest by automatically moving funds
between accounts when a surplus of funds held in a low
interest-bearing fund is identified.
[0086] As discussed above with reference to spending prediction
module 131, interest optimization module 133 may also cause
personalized banking engine 120 to transmit appropriate
notifications (e.g., emails, text messages, push notifications,
etc.) to client device 102. These notifications may, for example,
indicate that a transfer has occurred or is about to occur, thereby
keeping the user up-to-date on the account activity and/or allowing
the user to intervene if the account transfer is unwanted or
unneeded.
Exemplary User Financial Data for Cognitive Computing
Calculations
[0087] FIG. 2 illustrates exemplary financial data profiles 200 in
accordance with an aspect of the present disclosure. As shown in
FIG. 2, financial data profiles 200 are an example of the various
types of data that may be stored in any suitable number of storage
units or databases (e.g., financial data profiles 180, as shown in
FIG. 1) and organized, modified, and/or accessed via a personalized
banking engine, such as personalized banking engine 120, for
example, as shown in FIG. 1.
[0088] As shown in FIG. 2, financial data profiles 200 include a
number of financial data profiles associated with a number of
individual users A-D. Although only four example financial data
profiles are illustrated in FIG. 2, aspects include financial data
profiles 200 including any suitable number of financial data
profiles for any suitable number of different users.
[0089] Financial data profiles 200 illustrate a number of different
types of data associated with each user. Again, this data may be
used in accordance with the present aspects to perform calculations
and predictions regarding a user's financial accounts. FIG. 2
illustrates each of financial data profiles 200 as having a
specific number and amount of data associated with each user's
financial data profile; however, aspects include financial data
profiles 200 storing any suitable number and/or type of data to
facilitate the calculations and/or predictions as discussed
herein.
[0090] For example, as shown in FIG. 2, each user's financial data
profile contains three different types of inputs. Input (a) may
represent each user's credit card utilization as a percentage of
each user's maximum credit card limits. Credit card utilization may
be obtained, for example, via data received from third party credit
reports and/or data obtained via access to a user's credit card
accounts. Input (b) may represent a history of retailers that each
user may have visited over some period of time (e.g., within the
last 30 days, 60 days, etc.), the amount of money that each user
spent at each retailer, and/or what each user purchased, while
inputs (c1-c7) may represent a number of demographic factors.
[0091] In addition to the aforementioned inputs (a), (b), and
(c1-c7), each user's financial data profile may contain other
information such as financial account data. The details of each
user's financial data are not shown in FIG. 2 for purposes of
brevity, but may include any suitable data used to calculate a
daily account balance, an average account balance over some period
of time, spending data discussed above, etc. In other words, each
user's financial data profile may include all necessary information
required to perform the aspects described herein.
[0092] In the present aspects, upon detecting a user's proximity to
a physical retailer or a user navigating an online retailer's
website, personalized banking engine 120 may utilize data stored in
one or more financial data profiles to calculate a predicted
transaction amount that the user is statistically most likely to
spend at that particular retailer. Aspects include personalized
banking engine 120 utilizing this data in accordance with any
suitable predictive modeling and/or cognitive computing techniques
to calculate a predicted transaction amount.
[0093] For example, personalized banking engine 120 may calculate
the predicted transaction amount as an output of a weighting
function that weights a plurality of inputs extracted from any
combination of data stored in one or more financial data profiles.
The weighting function may, for example, place weights upon various
inputs that tend to contribute or correlate more to the
determination of how much a certain user typically spends at a
particular retailer.
[0094] This may be analyzed, for example, in light of previous data
stored in the user's financial data profile and/or a history of
data stored in other user's financial profiles. To provide an
illustrative example, an analysis of data stored across one or more
financial data profiles may indicate a strong correlation between
an amount of money spent at certain retailers and certain elements
of demographic data, such as age (c1) and income (c7). Furthermore,
such an analysis may also indicate a weaker correlation between an
amount of money spent at certain retailers and credit card
utilization and other elements of demographic data, such as gender
(c2) and household size (c4).
[0095] Continuing the previous example, aspects include calculating
a predicted transaction amount by applying greater weight to inputs
from financial data profiles having a stronger correlation to the
amount spent at a particular retailer, and applying lesser weight
to inputs from financial data profiles having a weaker correlation
to the amount spent at a particular retailer. In this way,
personalized banking engine 120 may determine the statistically
most likely transaction amount that a user will spend at a
particular retailer based upon that user's previous behavior and/or
similar user's previous behavior at the same retailer or similar
retailers (e.g., retailers selling similar merchandise, located in
a similar region, etc.).
[0096] To provide another illustrative example, each of financial
data profiles 200 may store spending data (e.g., as part of the
financial data) that indicates an average amount spent by each of
the plurality of users at various retailers over a period of time.
Continuing this example, personalized banking engine 120 may
determine the statistically most likely transaction amount that a
user will spend at a particular retailer by identifying a subset of
the users that have demographic information that best matches that
of the user. With reference to FIG. 2, for example, assume that
users A, B, and D have similar demographic data inputs (e.g., a
threshold number of inputs c1-c7 match or match within a certain
threshold).
[0097] Further assume that personalized banking engine 120 has
detected that user A is nearby retailer A. In such a scenario, the
present aspects may include personalized banking engine 120
calculating a predicted transaction amount for user A at retailer A
by averaging the amount spent by each of users B and D over the
last 60 days at the same retailer (a total of three transactions),
which results in a calculation of ($128+$135+$310)/3=$191. If an
adequate amount of data exists for user A's previous visits to the
same retailer, this data may also be included in the average
calculation. For example, user A may have purchased two $200 items
at retailer A over the last 60 days. As such, there would be five
transactions of $128, $135, $200, $200, and $310. In this case, the
average amount spent by users A, B, and D at retailer A would be
$194.60. In another embodiment, personalized banking engine 120 may
calculate a predicted transaction amount for user A at retailer A
by determining the median amount spent by each of users A, B, and D
over the last 60 days at retailer A, which results in the following
calculation: median ($128, $135, $200, $200, $310)=$200. Of course,
various modifications may be made to such calculations, such as not
including certain data points outside a certain range to prevent
skewing of calculations, weighing the user's own spending data more
heavily than other user's, etc.
[0098] To provide yet another illustrative example, aspects include
personalized banking engine 120 determining the statistically most
likely transaction amount that a user will spend at a particular
retailer by combining spending data with a weighing function. In
other words, as in the previous example, personalized banking
engine 120 may identify a subset of the users that have demographic
information that best matches that of a user for which the
calculation is being performed.
[0099] However, instead of averaging the spending amounts among the
identified users for the same retailer, aspects include
personalized banking engine 120 calculating dominant portions of
the demographic information that cause the greatest variance in the
average amount spent by each of the subset of the plurality of
other users. Therefore, these dominant portions may constitute
demographic factors with the strongest correlation to the average
amount spent across multiple users.
[0100] For example, assume that all users A-D have similar
demographic data. User's A, B, and D are shown to have recently
spent similar amounts at the same retailer A, while user C is shown
to have spent significantly more. An analysis of the differences
between their demographic data may indicate, however, that although
several portions of demographic data match among users A-D, user C
has an annual income approximately twice that of user's A, B, and
D. Therefore, annual income input (c7) may be considered the
dominant portion of demographic data in this example. Thus, the
statistically most likely transaction amount may be calculated by
weighting this input (c7) differently than the others, such as by
increasing its weight compared to the other demographic inputs
(c1-c6), for example.
Exemplary Financial Account Balance Tracking for Cognitive
Computing Calculations
[0101] FIG. 3 illustrates a graphical representation of an
exemplary tracked financial account balance 300 in accordance with
an aspect of the present disclosure. In the present aspect, tracked
financial account balance 300 may represent data in a time-wise
format that is monitored by a personalized banking engine, such as
personalized banking engine 120, for example, as shown in FIG.
1.
[0102] For example, tracked financial account balance 300 may
constitute an account balance of one or more user accounts that is
accessed, monitored, or otherwise tracked, which may be stored as
part of financial data profiles 180, as shown in FIG. 1. Thus, the
data used to construct the tracked financial account balance 300
may be part of a user's financial profile. In various aspects, the
user's financial data, including the account balances, may be
updated in accordance with any suitable schedule (e.g., once per
day, once every hour, once every 30 minutes, etc.). Therefore,
tracked financial account balance 300 may represent a log of
historical daily closing balances for a user's financial account,
such as a checking account, for example.
[0103] As shown in FIG. 3, financial account balance 300 shows
variations of a user's account balance over a period of time as
line 302.1. In accordance with the present aspects, personalized
banking engine 120 may average variations in a user's account
balance over any suitable time window, such as the previous 30
days, the previous 60 days, etc., to calculate an average daily
account balance 304 within this window. Furthermore, personalized
banking engine 120 may calculate average daily account balance 304
as a rolling average, updating the value each day as the window
moves forward in time, such that average daily account balance 304
represents an average account balance from the most recent 30 days,
the most recent 60 days, etc.
[0104] Although referred to as an average daily account balance,
average daily account balance 304 may be calculated using any
suitable techniques including traditional, or "straight" averaging
or weighted averaging, such as giving more weight to monetary
amounts that are more common or appear more often within a time
period. Furthermore, the present aspects include personalized
banking engine 120 calculating average daily account balance 304 by
truncating or not including more sporadic changes in the account
balance within a time period.
[0105] In still other aspects, personalized banking engine 120 may
calculate average daily account balance 304 via one or more
cognitive computing techniques and/or predictive modeling
techniques. Personalized banking engine 120 may attempt, for
example, to predict average daily account balance 304 based upon
spending history, financial transactions associated with the user's
accounts, the frequency and/or amount of deposits to the user's
financial account (e.g., regular recurring deposits from the user's
paycheck or direct deposit), etc.
[0106] Thus, personalized banking engine 120 may track or otherwise
calculate average daily account balance 304 using any suitable
number and/or sources of data, such as third party data sources
and/or one or more financial account data received or otherwise
accessed via the user's one or more financial institutions (e.g.,
one or more financial institutions 150, as shown in FIG. 1). Once
calculated, personalized banking engine 120 may then utilize this
data to detect a surplus of funds in one or more financial
accounts.
[0107] This surplus may be calculated in accordance with any
suitable techniques. For example, as shown in FIG. 3, a threshold
monetary value is represented by line 306, which exceeds the
average daily account balance 304 by some amount. This amount may
be calculated or otherwise selected to function as a buffer such
that even if the current balance is reduced by this amount, as
shown by line 308, the current account balance will not be negative
or otherwise be overdrawn. For example, the calculated threshold
value may be some percentage (e.g., 10%, 20%, etc.) of average
daily account balance 304.
[0108] Upon detecting a surplus condition, the present aspects
include personalized banking engine 120 calculating, projecting, or
otherwise forecasting an account balance for a time period in the
future. These calculations may be based upon, for example, one or
more cognitive computing techniques and/or predictive modeling
techniques, which may attempt, for example, to forecast a user's
future account balance based upon spending history, financial
transactions associated with the user's accounts, the frequency
and/or amount of deposits to the user's financial, other user's
account history and/or spending habits with similar demographics,
etc. An example of a forecasted account balance is shown in FIG. 3
as broken line 302.2, while the user's previous and current account
balances are shown in FIG. 3 as solid line 301.1.
[0109] To provide an illustrative example referring to FIG. 3,
personalized banking engine 120 may detect an initial surplus at
time 310. In this example, time periods are measured in days, but
aspects include the calculation of time periods in accordance with
any suitable unit of time measurement (e.g., weeks, hours, minutes,
seconds, etc.). Personalized banking engine 120 may then calculate
a set of time periods (e.g., from one to 10 days) and, for each
one, an associated statistical probability that represents a
likelihood of the user's account balance remaining above in excess
of the average daily account balance by the threshold value. An
example of a set of calculations is provided below in Table 1.
TABLE-US-00001 TABLE 1 Statistical probability of user's account
Number of days balance retaining a surplus 0 (Current) 100% 1 97% 2
90% 3 82% 4 78% 5 68% 6 62% 7 58% 8 55% 9 72% 10 85%
[0110] As shown in Table 1, personalized banking engine 120 may
calculate various degrees of statistical probabilities based upon
the forecasted future account balance. As shown in FIG. 3, it
becomes less likely that the forecasted account balance will
include a surplus of funds until day 9, which may represent, for
example, a date associated with a regularly recurring deposit.
Therefore, aspects include personalized banking engine 120
selecting a number of days from when the surplus is detected for
which a statistical probability exceeds a threshold likelihood of
the balance of the user's first financial account remaining above
the threshold value.
[0111] This may include, for example, analyzing the results similar
to those shown above in Table 1 to determine a number of
consecutive days for which a threshold likelihood is continuously
exceeded. Using the example values shown in Table 1, assume that
the threshold likelihood is selected as 75%. In such a case,
personalized banking engine 120 may calculate the number of days as
4. Aspects include personalized banking engine 120 then
transferring funds from the user's account to another financial
account that bears a higher interest rate for four days, after
which time personalized banking engine 120 may transfer the funds
back to the original account. The amount of these funds may be, for
example, a difference between the threshold value and the average
daily account balance, as shown in FIG. 3 by the reduction in the
forecasted account balance represented by line 308. In this way,
personalized banking engine 120 facilitates automatic optimization
of accrued interest among a user's accounts with a minimal
likelihood of the user having to intervene and/or the transfer
resulting in an overdraft or negative account balance.
[0112] In various aspects, personalized banking engine 120 may
transfer funds back to the user's account in varying amounts to
further maximize accrued interest. For example, the entire amount
of the funds may be transferred back, or a lesser amount may be
transferred to bring the balance of the user's first financial
account back to the average daily account balance.
[0113] Additionally or alternatively, personalized banking engine
120 may calculate the number of days for which to keep the
transferred funds in the higher interest-bearing account by taking
into consideration the amount of time required for funds to clear
or be available for withdrawal. For example, personalized banking
engine 120 may determine that it takes one business day for funds
to be available for withdrawal by the user after a transfer is
made. Using the above example, personalized banking engine 120 may
compensate for this business day and transfer funds at the end of
the third day instead of the fourth day.
[0114] Furthermore, various aspects include personalized banking
engine 120 continuing to monitor the user's current account balance
once funds have been transferred out of the lower interest-bearing
account. Thus, in the event that the forecasted account balance is
inaccurate, personalized banking engine 120 may transfer funds back
to the user's lower interest-bearing account even when the number
of calculated days has not yet expired.
[0115] For example, personalized banking engine 120 may determine
when one or more transactions are pending but have not yet cleared
and therefore have not yet affected the user's current account
balance. Personalized banking engine 120 may, however, use the
amount of one or more pending transactions to determine whether,
upon being cleared, the user's account would be overdrawn or
otherwise negatively impacted. In such a scenario, personalized
banking engine 120 may transfer funds back into the lower
interest-bearing account prior to the expiration of the number of
days discussed above.
[0116] Additionally or alternatively, personalized banking engine
120 may use the amount of pending transactions as part of a
condition to determine whether to initially transfer money out of
the user's lower interest-bearing account to the user's higher
interest-bearing account, thereby compensating for pending
transactions when determining whether a surplus exists. In this
way, personalized banking engine 120 may actively track and monitor
a user's account while transfers are pending to ensure that a
user's financial account is not negatively impacted.
[0117] In any event, the current aspects include personalized
banking engine 120, upon transferring a user's funds between
accounts and/or determining that a transfer may be preferable or
necessary, notify client device 102 in accordance with any suitable
techniques. For example, personalized banking engine 120 may
transmit a push notification, a text message, an email, etc., to
client device 102 to keep the user up-to-date regarding the status
of account transfers and other recommendations.
Exemplary Computer-Implemented Method of Preventing Account
Overdrafts and Excessive Credit Spending
[0118] FIG. 4 illustrates an exemplary computer-implemented method
flow 400 in accordance with one aspect of the present disclosure.
In the present aspects, one or more portions of method 400 (or the
entire method 400) may be implemented by any suitable device, and
one or more portions of method 400 may be performed by more than
one suitable device in combination with one another. For example,
one or more portions of method 400 may be performed by client
device 102 and/or personalized banking engine 120, as shown in FIG.
1. In one embodiment, method 400 may be performed by any suitable
combination of one or more processors, applications, algorithms,
and/or routines. For example, method 400 may be performed via one
or more processors 122 executing instructions stored in cognitive
computing and predictive modeling application 127 in conjunction
with data collected, received, and/or generated via personalized
banking engine 120.
[0119] Method 400 may start when one or more processors track a
user's financial account balance (block 402). In the present
aspects, the user's financial account balance may be tracked via a
personalized banking engine receiving financial account data from
one or more financial institutions for which the user has provided
authorization. For example, as discussed herein, a personalized
banking engine may access the user's account information via a
secure connection, using the user's provided password and logon
information.
[0120] Method 400 may include one or more processors determining
whether a user is close to a retailer and/or whether a user is
visiting a retailer's website (block 404). This may include, for
example, a personalized banking engine monitoring a user's physical
location and/or websites the user is visiting to determine that a
user is close to a particular physical retail location and/or
currently navigating a retailer's website, as further discussed
herein (block 404). If so, method 400 may continue (block 406). If
not, method 400 may revert back to continuing to monitor the user's
financial account balance (block 402) and the user's physical
location and/or websites the user is visiting (block 404).
[0121] Method 400 may include one or more processors calculating a
predicted transaction amount that the user will likely spend at the
particular retailer (block 406) previously identified (block 404).
Method 400 may include one or more processors, upon detecting that
a user is close to a retailer or visiting a retail website,
calculating a predicted transaction amount that the user is likely
to spend at that retailer with a statistical probability exceeding
a threshold probability (block 406). Again, this calculation may be
based upon any suitable cognitive computing and/or predictive
modeling techniques, such as those leveraging demographic data or
other data as discussed herein (block 406).
[0122] Method 400 may include one or more processors determining
whether the calculated predicted transaction amount (block 404)
exceeds the user's current account balance (block 408). If so,
method 400 may continue (block 410). If not, method 400 may revert
back to continuing to monitor the user's financial account balance
(block 402) and the user's physical location and/or websites the
user is visiting (block 404).
[0123] Method 400 may include one or more processors transmitting a
notification to a user's client device indicating a potential
overdraft of the user's credit limit being potentially exceeded
(block 410). Again, these notifications may include, for example,
push notifications, email messages, text messages, etc. (block
410).
Exemplary Use Case for Preventing Account Overdrafts and Excessive
Credit Spending
[0124] FIG. 5 illustrates an exemplary use case 500 for preventing
account overdrafts and excessive credit spending. FIG. 5 includes a
first time period 502, a second time period 504, a client device
102, a retailer 160, and a boundary 161. The use case 500
illustrated in FIG. 5 may be an example operation of the disclosed
systems and methods for preventing account overdrafts and excessive
credit spending. During time period 502, a user (not shown) may be
in possession of client device 102. Client device 102 is in
relative proximity to the retailer 160, however, the client device
has not crossed boundary 161. Boundary 161 may be the geofence
described above as determined by client device 102, personalized
banking engine 120, retailer 160, or some combination thereof.
During this time period, personalized banking engine 120, or client
device 102, may track a current account balance for the user's
financial account utilizing financial account data.
[0125] At time period 504, the user and client device 102 have
crossed boundary 161 and are located within the proximity of the
retailer 160. Personalized banking engine 120, or client device
102, may determine that the user is located within a threshold
distance of the retailer 160. Personalized banking engine 120, or
client device 102, may calculate a predicted transaction amount
that the user is likely to spend at the retailer 160. Personalized
banking engine 120, or client device 102, may use any of the
different types of methods described above to calculate the
predicted transaction amount. In the event that the predicted
transaction amount exceeds the current account balance for the
user's financial account and the user is located within the
threshold distance of the retailer 160, personalized banking engine
120, or client device 102, may selectively transmit a notification
to client device 102 associated with the user. If the predicted
transaction amount does not exceed the current account balance for
the user's financial account, or the user is not located within the
threshold distance of retailer 160, a notification does not need to
be sent. In some embodiments, if the predicted transaction amount
does not exceed the current account balance for the user's
financial account personalized banking engine 120 may selectively
transmit a notification to client device 102 alerting the user to
the fact that he or she has sufficient funds in his or her
financial account to cover a purchase totaling the predicted
transaction amount.
Exemplary Computer-Implemented Method for Notifying a User of a
Potential Overdraft
[0126] In one aspect, a computer-implemented method for notifying a
user of a potential overdraft may be provided. The method may
include: (1) tracking a current account balance for a user's
financial account utilizing financial account data; (2) determining
when the user is located within a threshold distance of a retailer;
(3) calculating a predicted transaction amount that the user is
statistically most likely to spend at the retailer; and/or (4)
selectively transmitting, by the one or more processors, a
notification to a client device associated with the user indicating
that a potential overdraft may occur when (i) the predicted
transaction amount exceeds the current account balance for the
user's financial account, and (ii) the user is located within the
threshold distance of the retailer. The method may include
additional, less, or alternate actions, including those discussed
elsewhere herein.
[0127] For instance, in various aspects, the predicted transaction
amount may be calculated as an output of a weighting function that
weights a plurality of inputs that include (i) the retailer, and
(ii) the user's demographic information. The user's demographic
information may include, for example, the user's age, income level,
and geographic region.
[0128] Additionally or alternatively, the method may include
storing spending data in a database, which may be associated with
each of a plurality of other users. The spending data may indicate
an average amount spent by each of the plurality of users at the
retailer over a period of time and/or demographic information
associated with each of the plurality of other users. In such a
case, the method may include calculating the predicted transaction
amount by (i) identifying a subset of a plurality of other users
having demographic information matching that of the user; and (ii)
averaging, over each of the identified subset of the plurality of
users, the average amount spent by each of the subset of the
plurality of users.
[0129] Additionally or alternatively, the predicted transaction
amount may be calculated by first calculating dominant portions of
the demographic information, for each of the subset of the
plurality of other users, which causes the greatest variance in the
average amount spent by each of the subset of the plurality of
other users. The method may include calculating the predicted
transaction amount as an output of a weighting function that
weights a plurality of inputs including the user's demographic
information based upon the dominant portions of the demographic
information from each of the subset of the plurality of other
users.
[0130] Additionally or alternatively, the method may include
selectively transmitting the notification to the client device by
sending a push notification to the client device, sending an email
message to the client device, and/or sending a text message to the
client device.
Exemplary System for Notifying a User of a Potential Overdraft
[0131] In yet another aspect, a system for notifying a user of a
potential overdraft may be provided. The system may include: (1) a
client device configured to periodically transmit location data
indicative of a current location of the client device and to
receive and display notifications associated with a user's
financial account; and/or (2) one or more back-end components
configured to: (a) track a current account balance for the user's
financial account utilizing financial account data; (b) determine
when the user is located within a threshold distance of a retailer
based upon the periodically transmitted location data; (c)
calculate a predicted transaction amount that the user is
statistically most likely to spend at the retailer; and/or (d)
selectively transmit a notification to the client device associated
with the user indicating that a potential overdraft may occur when
(i) the predicted transaction amount exceeds the current account
balance for the user's financial account, and (ii) the user is
located within the threshold distance of the retailer The system
may include additional, less, or alternate components, including
those discussed elsewhere herein.
[0132] For instance, the one or more back-end components may be
further configured to calculate the predicted transaction amount as
an output of a weighting function that weights a plurality of
inputs that include (i) the retailer, and (ii) the user's
demographic information. The user's demographic information may
include, for example, the user's age, income level, and geographic
region.
[0133] Additionally or alternatively, the one or more back-end
components may be further configured to: (e) store spending data in
a database associated with each of a plurality of other users, the
spending data indicating an average amount spent by each of the
plurality of users at the retailer over a period of time; (f)
identify a subset of a plurality of other users having demographic
information that matches that of the user; and/or (g) calculate the
predicted transaction amount by averaging, over each of the
identified subset of the plurality of users, the average amount
spent by each of the subset of the plurality of users.
[0134] Additionally or alternatively, the system may include the
one or more back-end components storing spending data in a
database, which may be associated with each of a plurality of other
users. The spending data may indicate an average amount spent by
each of the plurality of users at the retailer over a period of
time and/or demographic information associated with each of the
plurality of other users. In such a case, the system may include
the one or more back-end components calculating the predicted
transaction amount by (i) identifying a subset of a plurality of
other users having demographic information matching that of the
user; and (ii) averaging, over each of the identified subset of the
plurality of users, the average amount spent by each of the subset
of the plurality of users.
[0135] Additionally or alternatively, the predicted transaction
amount may be calculated by the one or more back-end components
first calculating dominant portions of the demographic information,
for each of the subset of the plurality of other users, which
causes the greatest variance in the average amount spent by each of
the subset of the plurality of other users. The system may include
the one or more back-end components calculating the predicted
transaction amount as an output of a weighting function that
weights a plurality of inputs including the user's demographic
information based upon the dominant portions of the demographic
information from each of the subset of the plurality of other
users.
[0136] Additionally or alternatively, the system may include the
one or more back-end components selectively transmitting the
notification to the client device by sending a push notification to
the client device, sending an email message to the client device,
and/or sending a text message to the client device.
Exemplary System for Moving Excess Monies Between Financial
Accounts
[0137] In yet another aspect, a system to sweep excess monies into
another financial account may be provided. The system may include
one or more processors and/or transceivers configured to: (1) track
financial account data including (i) financial transactions
associated with a user's first and second financial account, and
(ii) a current account balance associated with the user's first and
second financial account; (2) calculate an average daily account
balance associated with the user's first financial account based
upon the financial account data; (3) detect a surplus when the
current account balance associated with the user's first financial
account exceeds the average daily account balance by a threshold
value; (4) calculate a number of days from when the surplus is
detected for which a statistical probability exceeds a threshold
likelihood of the balance of the user's first financial account
remaining in excess of the average daily account balance; and/or
(5) transfer money from the first financial account to the second
financial account for the calculated number of days in an amount
equal to a difference between the threshold value and the average
daily account balance.
[0138] In some embodiments of the system, the first financial
account accrues interest at a rate that is less than that of the
second financial account. In other embodiments, the system further
includes at the expiration of the calculated number of days,
transferring, by the one or more processors, money from the second
financial account to the first financial account. Additionally, in
some embodiments the money is transferred from the second financial
account to the first financial account in an amount required to
bring the balance of the user's first financial account back to the
average daily account balance. In another aspect of the system, the
act of calculating the number of days includes compensating for
time required for the money to be available in the first financial
account upon being transferred from the second financial account to
the first financial account.
[0139] In yet another aspect of the system, the system is further
configured to: (6) determine whether a pending transaction, upon
being cleared, would result in the user's first financial account
being overdrawn, and wherein the act of transferring money from the
first financial account to the second financial account further
comprises selectively transferring the money from the first
financial account to the second financial account only when the
pending transaction, upon being cleared, would not result in the
user's first financial account being overdrawn. The system may also
be further configured to: (7) transmit a notification to a client
device associated with the user indicating that money has been
transferred from the first financial account to the second
financial account.
Exemplary Computer-Implemented Method of Optimizing Interest
Accrual Between a User's Financial Accounts
[0140] FIG. 6 illustrates an exemplary computer-implemented method
flow 600 in accordance with an aspect of the present disclosure. In
the present aspects, one or more portions of method 600 (or the
entire method 600) may be implemented by any suitable device, and
one or more portions of method 600 may be performed by more than
one suitable device in combination with one another. For example,
one or more portions of method 600 may be performed by client
device 102 and/or personalized banking engine 120, as shown in FIG.
1. In an embodiment, method 600 may be performed by any suitable
combination of one or more processors, applications, algorithms,
and/or routines. For example, method 600 may be performed via one
or more processors 122 executing instructions stored in cognitive
computing and predictive modeling application 127 in conjunction
with data collected, received, and/or generated via personalized
banking engine 120.
[0141] Method 600 may start when one or more processors track a
user's financial account balance (block 602). In the present
aspects, the user's financial account balance may be tracked via a
personalized banking engine receiving financial account data from
one or more financial institutions that the user has provided
authorization. For example, as discussed herein, a personalized
banking engine may access the user's account information via a
secure connection, using the user's provided password and logon
information, etc.
[0142] Method 600 may include one or more processors calculating an
average daily account balance (block 604) using the tracked
financial account balance data (block 602). This may include, for
example, calculating an average daily account balance from any
suitable window and in accordance with any suitable techniques, as
further discussed herein (block 604).
[0143] Method 600 may include one or more processors determining
whether the user's tracked account has a surplus of funds (block
606). This may be determined, for example, when the user's account
balance exceeds a threshold value (block 606). If so, then method
600 may continue (block 606). If not, method 600 may revert back to
continuing to track the user's financial account balance (block
602).
[0144] Method 600 may include one or more processors calculating a
number of days for which the user's account balance will remain in
excess of the threshold value (block 608). This threshold value may
include, for example, the same threshold value used for the
determination of whether the user's account has a surplus of funds
(block 608). This calculation may be performed, for example, by
determining a consecutive number of days in which a threshold
statistical probability threshold is exceeded, as discussed further
herein (block 608).
[0145] Method 600 may include one or more processors transferring
funds from the user's tracked account to another account that has a
higher interest rate (block 610). This may include, for example, a
personalized banking engine communicating with the user's financial
institutions to request a funds transfer, such as an electronic
funds transfer (EFT), an automated clearing house transfer (ACH),
etc. (block 410).
Targeted Offers Based Upon Shopping Profile
[0146] FIG. 7 illustrates an exemplary computer-implemented method
700 in accordance with one aspect of the present disclosure. In the
present aspects, one or more portions of method 700 (or the entire
method 700) may be implemented by any suitable device, and one or
more portions of method 700 may be performed by more than one
suitable device in combination with one another. For example, one
or more portions of method 700 may be performed by client device
102 and/or personalized banking engine 120, as shown in FIG. 1. In
one embodiment, method 700 may be performed by any suitable
combination of one or more processors, applications, algorithms,
and/or routines. For example, method 700 may be performed via one
or more processors 122 executing instructions stored in cognitive
computing and predictive modeling application 127, and/or
instructions stored in commercial communication module 133, in
conjunction with data collected, received, and/or generated via
personalized banking engine 120.
[0147] The method 700 may include generating, via one or more
processors, a shopping profile of the user wherein the shopping
profile includes a dataset indicative of shopping habits of the
user (block 702). The shopping profile for the user may be built
(with user permission) using financial transaction data, such as
the financial transaction data stored in the financial data
profiles 180. The method 700 may include receiving, via the one or
more processors, a location of the user's mobile device (block
704). The location of the user's device may be determined by the
location acquisition unit 112 of the client device 102. The method
700 may include identifying, via the one or more processors, that
the location of the mobile device is within a threshold distance of
a physical vendor location associated with a vendor (block 706).
The method 700 may include determining, via the one or more
processors, that the vendor is a preferred vendor of the user based
upon the dataset included in the shopping profile (block 708).
[0148] If the vendor is a preferred vendor, the method may include,
identifying, via the one or more processors, commercial
communications for products that are currently being offered by the
preferred vendor at abated value (block 710). Accordingly, the
method may include determining, via the one or more processors,
which commercial communications are for preferred products for the
user based upon the dataset included in the shopping profile (block
712). Further, the method may include, when there are any preferred
products currently offered by the preferred vendor and the mobile
device is within the threshold vicinity of the physical vendor
location, transmitting, via the one or more processors, the
commercial communications for the preferred products to the user's
mobile device (block 714). The method may include additional, less,
or alternate actions, including those discussed elsewhere
herein.
[0149] In some embodiments of the method, generating, via the one
or more processors, the shopping profile of the user (block 702)
includes inputting, via the one or more processors, financial
transaction data associated with the user into a machine learning
program that is trained to generate the shopping profile for the
user based upon the financial transaction data.
[0150] In some embodiments of the method, the shopping profile for
the customer includes at least one or more of (i) the names of
merchants at which the user shops; (ii) the frequency the user
shops at each merchant; (iii) an average of how much the user
spends at each merchant; (iv) the types of products purchased at
each merchant, (v) the brand names of products purchased at each
vendor by the user, or some combination thereof. The shopping
profile may be customizable by the customer.
[0151] In some embodiments of the method, the commercial
communications include an offer of reward points if a purchase of
the preferred product is completed within a given amount of time.
Similarly, the commercial communications may include an offer of
reward points if a purchase of the preferred product is completed
using a designated payment type. The designated payment type may be
a particular credit account, a particular financial account, or any
other type of payment account associated with the user. In some
embodiments of the method, the commercial communications include
bundled pricing for a bundle of products offered by the preferred
merchant. For example, the bundled products may be products and/or
services that are commonly bought together such as a mortgage loan
and homeowners insurance, or an automobile and automobile
insurance, etc. In yet other embodiments, the commercial
communications may include an offer of reward points if a purchase
of the preferred product is completed via a loan product.
[0152] In some embodiments, the shopping profile for the user
includes a predicted life event, and the preferred offer is based
upon the predicted life event. The life event may be predicted as
part of generating the shopping profile (block 702), or may be
predicted independent of the method 700. The predicted life event
may include at least one of an educational event, an age-related
event, a birth of a child, a marriage, or some combination thereof.
The educational event may be the user graduating from an education
institution, or a child or other member of the user's family
graduating from an educational institution. The age-related event
may be the user turning a particular age, and/or a member of the
user's family (e.g., a spouse or child) turning a particular age.
The birth of the child may be the birth of a child of the user, the
birth of a child for a member of the user's family, or the birth of
a child for another individual connected to the user. Similarly,
the marriage may be the user's marriage, or the marriage of any
member of the user's family, any friend of the user, or any other
individual associated with the user.
Exemplary Use Case for Generating Targeted Offers Based Upon
Shopping Profile
[0153] FIG. 8 illustrates an exemplary use case 800 for presenting
targeted offers for preferred products to a user via a mobile
device. FIG. 8 includes a first time period 802, a second time
period 804, a client device 102, a retailer 160, and a boundary
161. The use case 800 illustrated in FIG. 8 may be an example
operation of the disclosed systems and methods for presenting
targeted offers for preferred products to a user via a mobile
device. During time period 802, a user (not shown) may be in
possession of client device 102. Client device 102 is in relative
proximity to the retailer 160. However, client device 102 has not
crossed boundary 161. Boundary 161 may be the geofence described
above as determined by client device 102, personalized banking
engine 120, retailer 160, or some combination thereof. During this
time period personalized banking engine 120, or client device 102,
may generate a shopping profile for the user. Personalized banking
engine 120, or client device 102, may use any of the different
types of methods described above to generate the shopping profile.
In some embodiments, the shopping profile is not built until after
the user has crossed boundary 161.
[0154] At time period 804, the user and client device 102 have
crossed boundary 161 and are located within the proximity of
retailer 160. Personalized banking engine 120, or client device
102, may determine that the user is located within the threshold
distance of retailer 160. Personalized banking engine 120, or
client device 102, may determine that the retailer 160 is a
preferred vendor for the user. Personalized banking engine 120, or
client device 102, may identify relevant commercial communications
for the user associated with the preferred merchant. For example,
the commercial communications may be targeted offers for goods or
services of interest to the user. The goods or services that are of
interest to the user may be determined by personalized banking
engine 120, or client device 102, analyzing the shopping profile of
the user. These commercial communications may then be transmitted
to client device 102 allowing the user to take action on the
commercial communications. The user may purchase the goods or
services identified in the commercial communications, or in some
cases the user may decide based upon the notification to enter
retailer 160 to investigate the promotional offers.
Targeted Offers to Inactive Account Holders
[0155] FIG. 9 illustrates an exemplary computer-implemented method
900 in accordance with one aspect of the present disclosure. In the
present aspects, one or more portions of method 900 (or the entire
method 900) may be implemented by any suitable device, and one or
more portions of method 900 may be performed by more than one
suitable device in combination with one another. For example, one
or more portions of method 900 may be performed by client device
102 and/or personalized banking engine 120, as shown in FIG. 1. In
one embodiment, method 900 may be performed by any suitable
combination of one or more processors, applications, algorithms,
and/or routines. For example, method 900 may be performed via one
or more processors 122 executing instructions stored in cognitive
computing and predictive modeling application 127, and/or
instructions stored in commercial communication module 133, in
conjunction with data collected, received, and/or generated via
personalized banking engine 120.
[0156] The method 900 may include receiving financial transaction
data associated with financial transactions initiated by a user,
and generating, via one or more processors, a shopping profile
associated with shopping habits of a user (block 902). The shopping
profile may include a dataset including the received financial
transaction data, and/or other data indicative of a user's shopping
preferences (e.g., favorite stores, favorite products), or
transaction history. The method 900 may include receiving, via the
one or more processors, location data from a user's mobile device
in real-time or near real-time (block 904), and using the mobile
device location to determine, via the one or more processors, that
the user is at, or near, a specific vendor location (block 906).
Whether a user is at, or near, a specific vendor location may be
determined by the user's mobile device crossing a predetermined
threshold distance of the physical vendor location. The threshold
distance may be decided by the user, the vendor, or by personalized
banking engine 120.
[0157] The method 900 may include identifying, via the one or more
processors, that the user has not used an account for a
predetermined amount of time (block 908). The account may be an
inactive credit card account, an inactive checking account, an
inactive debit account, an inactive mobile account, an inactive
electronic money account, or an inactive prepaid account. The
account may be identified by determining which accounts in the
financial profile 180 for the user are related, or relevant, to the
vendor at the physical vendor location. The predetermined amount of
time may be a time set by personalized banking engine 120 or a time
set by the user. The predetermined amount of time may be a number
of days, weeks, months, or even years. In some embodiments, the
cognitive computing predictive modeling application 127 may
determine the predetermined amount of time based upon an analysis
of a set of financial data profiles for other users and the amount
of time that separates transactions in the corresponding set of
financial data profiles for those users.
[0158] The method may include determining, via the one or more
processors, relevant products being offered for sale by the vendor
(block 910). The relevant products may be products that are being
offered at abated value, or discounted pricing, or the relevant
products may be products that are denoted as preferred products for
the user in their shopping profile, or some combination thereof.
The cognitive computing predictive modeling application 127 may
determine which products are relevant to the user based upon the
user's financial transactions, the financial profiles of users with
similar demographics, predicted shopping habits for the user based
upon predicted events, or some combination thereof.
[0159] If the account has not been used in the predetermined amount
of time, and the merchant is offering relevant products, and the
user, or their mobile device, has crossed a predetermined threshold
(block 912), the method 900 may include transmitting a commercial
communication to the user's mobile device, wherein the commercial
communication identifies the one or more relevant products, and a
rewards communication to receive rewards if the inactive account is
used to complete a purchase of the one or more relevant products at
the vendor (block 914). The rewards may be for bonus or additional
points if the inactive account is used to conduct a financial
transaction that day or within another a time period to facilitate
making online "point of sale" offers in real-time or near real-time
to holders of inactive accounts. The method may include additional,
less, or alternate actions, including those discussed elsewhere
herein.
[0160] For example, the method may include determining, via the one
or more processors, that the vendor is a preferred vendor of the
user based upon information within the user's shopping profile
before determining offers for products on sale that are currently
being offered by the preferred vendor. Similarly, the method may
include determining, via the one or more processors, which offers
are preferred offers for the user based upon information within the
user's shopping profile, and only transmitting notifications to the
user's mobile device associated with preferred offers.
[0161] In some embodiments, the shopping profile for the user
includes a predicted life event, and the preferred offer is based
upon the predicted life event. The predicted life event may be
predicted as part of generating the shopping profile (block 902),
or may be predicted independent of the method 900. The predicted
life event may include children of the user, or the user,
graduating from high school or college. The predicted life event
may include an age related event (e.g., the user or their children
turning 16, 18, or 22), and the offer may be an offer for a vehicle
loan. Similarly, the predicted life event may include a birth of a
child, or a marriage. The birth or marriage may be expected, or not
expected, by cognitive computing predictive modeling application
127. Additionally, the predicted life event may include an increase
in income, or a move to a new residence or home.
[0162] In some embodiments, the offer to the user included in the
commercial communication includes an interest-free transaction
offered to the user if the transaction is completed with the
inactive account. The interest-free transaction may be a
transaction executed with the inactive account (e.g., inactive
credit card) where after the transaction is completed the user is
not obligated to incur any interest on his or her account as a
result of the transaction. The interest-free transaction may run
for a period of time (e.g., a month, a year, etc.) before the user
is obligated to pay interest on the transaction.
[0163] In some embodiments of the method, generating, via the one
or more processors, the shopping profile of the user (block 902)
includes inputting, via the one or more processors, financial
transaction data associated with the user into a machine learning
program that is trained to generate the shopping profile for the
user based upon the financial transaction data, with the user's
permission, using the financial transaction data. In some
embodiments of the method, the shopping profile for the customer
includes at least one or more of: (i) the names of merchants at
which the user shops; (ii) the frequency the user shops at each
merchant; (iii) an average of how much the user spends at each
merchant; (iv) the types of products purchased at each merchant,
(v) the brand names of products purchased at each vendor by the
user; or some combination thereof. The shopping profile may be
customizable by the customer.
Exemplary Use Case for Targeted Offers to Inactive Account
Holders
[0164] FIG. 10 illustrates an exemplary use case 1000 for
presenting targeted offers to inactive account holders via a mobile
device. FIG. 10 includes a first time period 1002, a second time
period 1004, a client device 102, a retailer 160, and a boundary
161. The use case 1000 illustrated in FIG. 10 may be an example
operation of the disclosed systems and methods for presenting
targeted offers to inactive account holders to a user via a mobile
device. During time period 1002, a user (not shown) may be in
possession of client device 102. Client device 102 is in relative
proximity to retailer 160, however, the client device has not
crossed boundary 161. Boundary 161 may be the geofence described
above as determined by client device 102, personalized banking
engine 120, retailer 160, or some combination thereof. During this
time period, personalized banking engine 120, or client device 102,
may generate a shopping profile for the user. Personalized banking
engine 120, or client device 102, may use any of the different
types of methods described above to generate the shopping profile.
In some embodiments, the shopping profile is not built until after
the user has crossed boundary 161.
[0165] At time period 1004, the user and client device 102 have
crossed boundary 161 and are located within proximity of retailer
160. Personalized banking engine 120, or client device 102, may
determine that the user is located within the threshold distance of
retailer 160. Personalized banking engine 120, or client device
102, may determine that the user has not used a particular account
in a predetermined amount of time. Personalized banking engine 120,
or client device 102, may identify relevant commercial
communications for the user associated with the merchant. For
example, the commercial communications may be targeted offers to
use the inactive account to purchase goods or services of interest
to the user. The goods or services that are of interest to the user
may be determined by personalized banking engine 120, or client
device 102, analyzing the shopping profile of the user. These
commercial communications may then be transmitted to client device
102 allowing the user to take action on the commercial
communications. The user may purchase the goods or services
identified in the commercial communications, or in some cases the
user may decide based upon the notification to enter retailer 160
to investigate the promotional offers.
Exemplary Customer Interaction System
[0166] FIG. 11 illustrates an exemplary customer interaction system
1100 in accordance with one aspect of the present disclosure. FIG.
11 includes a user 1102, a vendor 1104, a self-service
communication channel 1106, a full-service communication channel
1108, a network 116, network connections 117.1, 117.2, and 117.3,
and a personalized banking engine 120. FIG. 11 also optionally
includes a client device 102, financial institutions 150, and
retail stores 160. In some implementations client device 102 is on
the person of user 1102, in other implementations client device 102
is independent of user 1102. Similarly, financial institutions 150,
or retail stores 160, may be inclusive of vendor 1104, or
independent of vendor 1104.
[0167] User 1102 may be a user, or customer, of vendor 1104.
Accordingly, the interactions between user 1102 and vendor 1104 may
be monitored by the overall system 1100, and more particularly,
monitored by personalized banking engine 120. User 1102 may have a
financial data profile associated with user 1102, such as the
example financial data profiles illustrated in FIG. 2. The data
collected on user 1102, including the interactions user 1102 has
with vendor 1104, may be used to determine rewards to award the
user. Personalized banking engine 120 may use machine learning, or
cognitive computing techniques, to determine the amount or type of
reward that the user may receive for using a self-service
communication channel 1106 instead of a full-service communication
channel 1108.
[0168] Customer interactions may occur over self-service
communication channel 1106, or a full-service communication channel
1108. The customer interactions may be reporting issues with an
account, or product or service, to vendor 1104, paying a bill to
vendor 1104, or some similar type of interaction. Vendor 1104 may
be a merchant that user 1102 regularly frequents, or has frequented
only once in the past. User 1102 may have an ongoing contractual
relationship with vendor 1104. For example, user 1102 may have an
insurance policy with vendor 1104. Vendor 1104 in some cases may be
a financial institution 150, or retail store 160, with which user
1102 has an ongoing relationship.
[0169] Self-service communication channel 1106 may be a
communication channel that user 1102 uses to interact with vendor
1104. "Self-service" may mean that user 1102 is responsible for
taking the necessary actions to resolve his or her issue or
complete his or her transaction. In some embodiments, self-service
communication channel 1106 may include a communication tool
embedded into a public facing website maintained by vendor 1104,
such as a chat client. In other cases, self-service communication
channel 1106 may be used by user 1102 to enroll into a program
(with vendor 1104) to receive electronic statements for his or her
account with vendor 1104, as opposed to paper statements sent
through the US Postal Service. In other embodiments, self-service
communication channel 1106 may be used by user 1102 to enroll in an
automatic payment program with vendor 1104.
[0170] Conversely, full-service communication channel 1108 may be
any sort of communication channel that requires vendor 1104, or one
of its agents, to engage with user 1102. "Full-service" may mean
that user 1102 and vendor 1104 must proactively interact with each
other to resolve the user's issue or transaction. Some examples may
include user 1102 calling a phone number to speak with an operator
employed by vendor 1104, user 1102 interacting with a human agent
of vendor 1104 via text message, or other means of
communication.
[0171] In a typical operation of the system 1100, user 1102 may
wish to interact with vendor 1104. For example, user 1102 may need
to pay an outstanding balance on a bill for an account user 1102
has with vendor 1104. User 1102 may then communicate with vendor
1104 by using self-service communications channel 1106, such as via
a web site associated with vendor 1104. The user's interaction with
vendor 1104 may be logged into a profile for user 1102, such as one
of the financial profiles detailed in FIG. 2. At a later time, user
1102 may need to report an issue with a product or service
purchased from vendor 1104. User 1102 may then elect to use
full-service communication channel 1108 to communicate with vendor
1104, by calling a helpline phone number for vendor 1104, for
example. The call from user 1102 to vendor 1104 may be logged on
the user's profile with vendor 1104.
[0172] These types of interactions may occur over a period of time
(e.g., a day, a week, months) between user 1102 and vendor 1104.
Personalized banking engine 120, or vendor 1104, may conduct an
analysis of the user's interactions with vendor 1104 and determine
that user 1102 is utilizing full-service communication channels
1108 with vendor 1104 more than user 1102 is utilizing self-service
communication channels 1106 with vendor 1104. Accordingly,
personalized banking engine 120 may notify user 1102 that if user
1102 were to increase his or her usage of self-service
communication channels 1106, user 1102 would be eligible to receive
a reward from vendor 1104. The notification of the reward to user
1102 may include the system 1100, or more particularly personalized
banking engine 120, transmitting the reward to the user's mobile
device for user 1102 to review.
[0173] Example rewards may be a discount on products or services
offered by vendor 1104, exclusive purchasing opportunities for
products or services offered by vendor 1104 that are not offered to
other users, promotional opportunities for purchasing goods or
services from other vendors, or in some cases depositing reward
points into an account associated with user 1102. The reward points
may be used by user 1102 to purchase products or services with
vendor 1104 or other vendors. The rewards may be determined based
in part upon the user's financial habits, or patterns of spending
at retailers. The user's habits and patterns may be determined by
tracking the user's financial information as detailed in the
description for FIGS. 1 and 2 above.
Monitoring Amount of Customer Interaction Self-Service
[0174] FIG. 12 illustrates an exemplary computer-implemented method
1200 for monitoring an amount of customer interactions using
self-service communication channels, as opposed to full-service
communication channels. The method 1200 may include: monitoring,
via one or more processors, an amount of interactions between a
customer and a vendor in at least one full-service channel and in
at least one self-service channel (block 1202); determining, via
the one or more processors, that the amount of customer
interactions for the customer through the at least one self-service
channel is less than the amount of customer interactions for the
customer through the at least one full-service channel (block
1204); when the amount of customer interactions for the customer
through the at least one self-service channel is less than the
amount of customer interactions through the at least one
full-service channel (block 1206); generating, via the one or more
processors, an electronic offer for the customer that includes a
reward for an account associated with the customer if the customer
increases usage of the at least one self-service channel and
decreases usage of the at least one full-service channel for future
customer interactions with the vendor (block 1208); and
transmitting, via the one or more processors, the electronic offer
to a customer's mobile device for their review to facilitate
incentivizing the customer to utilize the at least one self-service
channel for customer interactions with the vendor (block 1210). The
method 1200 may include additional, less, or alternate actions,
including those discussed elsewhere herein.
[0175] In some embodiments, the computer implemented method may
further comprise depositing, via the one or more processors, the
reward into the account associated with the customer. Similarly,
monitoring customer interactions (block 1202) may further comprise:
receiving, via the one or more processors, transaction data
associated with transactions incurred by the customer; generating,
via the one or more processors, a customer shopping profile based
upon the transaction data, wherein the customer shopping profile
comprises customer shopping trends; and customizing, via the one or
more processors, the reward to the customer shopping trends using
the customer shopping profile.
[0176] In some embodiments, the customer shopping profile includes
data indicative of at least one vendor at which the customer shops,
an education level for the customer, what offerings the customer
purchases, the type of offerings the customer purchases, or some
combination thereof. In other embodiments, the at least one
self-service channel is used by the customer for enrollment (e.g.,
into an automatic payment program), receiving e-statements,
visiting a website associated with the vendor, or some combination
thereof.
Targeted Loan Offers to Customers
[0177] FIG. 13 illustrates an exemplary computer-implemented method
1300 in accordance with one aspect of the present disclosure. In
the present aspects, one or more portions of method 1300 (or the
entire method 1300) may be implemented by any suitable device, and
one or more portions of method 1300 may be performed by more than
one suitable device in combination with one another. For example,
one or more portions of method 1300 may be performed by client
device 102 and/or personalized banking engine 120, as shown in FIG.
1. In one embodiment, method 1300 may be performed by any suitable
combination of one or more processors, applications, algorithms,
and/or routines. For example, method 1300 may be performed via one
or more processors 122 executing instructions stored in cognitive
computing and predictive modeling application 127, and/or
instructions stored in commercial communication module 133, in
conjunction with data collected, received, and/or generated via
personalized banking engine 120.
[0178] The method 1300 may include generating a financial profile
associated with a customer (block 1302). The shopping profile may
include a dataset including the received financial transaction
data, and/or other data indicative of a user's shopping preferences
(e.g., favorite stores, favorite products), or transaction history.
The method 1300 may include receiving one or more locations of a
customer mobile device over a period of time (block 1304), and
determining that at least one of the one or more locations of the
customer mobile device is within a predetermined distance of an
asset listed for sale (block 1306). Whether a user is at, or near,
the location of an asset listed for sale may be determined by the
user's mobile device crossing a predetermined threshold distance of
the physical asset location. The threshold distance may be decided
by the user, by a vendor selling the asset, or by personalized
banking engine 120. The threshold distance may be the customer
mobile device being within the same zip code as the asset listed
for sale, the customer mobile device being within 10 feet, 100
feet, or 1000 feet of the asset listed for sale, etc.
[0179] The method 1300 may include determining a loan offer for the
customer based upon the financial profile (block 1308), and
transmitting the loan offer to the customer for their review to
facilitate providing loan offers to customers currently shopping
for assets (block 1310). The method may base the loan offer on the
financial profile by analyzing the user's financial profile to
determine a risk level for the customer, a financial ability of the
customer to repay, and/or other relevant metrics. The other
relevant metrics may be determined by the system by performing
historical analysis of other customer's financial profiles to see
which metrics correlate to failure to pay back loans. The method
may also use cognitive computing, machine learning, or predictive
analytics to compare the customer's financial profile to other
users' financial profiles and determine which loan offer to offer
the customer. The method may include additional, less, or alternate
actions, including those discussed elsewhere herein.
[0180] For example, in some embodiments the method may include
receiving the one or more locations of a customer mobile device
over a period of time (block 1304), which further comprises
receiving the locations over a day, a week, or a month. The
locations may be received from the customer's mobile device, from a
vehicle used by the customer, or from a cellular network that
tracks the customer's mobile device.
[0181] In some embodiments, the method may include determining the
loan offer (block 1308) and may further comprise adjusting a loan
length, a loan interest rate, or some combination thereof, for the
loan offer based upon the customer's financial profile. Similarly,
in some embodiments determining the loan details (e.g., the loan
length, the loan interest rate) may include utilizing a predictive
analytics or cognitive computing algorithm that compares the
customer to existing customers for a vendor selling the asset
listed for sale.
[0182] In some embodiments, the financial profile includes net
worth, net income, credit rating, credit score, or some combination
thereof, for the customer. The customer's net worth may be the
total assets the customer owns minus the total liabilities for
which the customer is liable. The net income for the customer may
be the amount of earnings over a period of time the customer has
left over after all of the customer's expenses over the period of
time have been subtracted from all of the customer's earnings. The
customer's credit rating may be an estimate of the ability of the
customer to pay off all of his or her financial obligations, or
meet new financial obligations. Similarly, the customer's credit
score may be a number, calculated by analyzing a customer's
financial profile, that is indicative of the customer's ability to
pay off a loan, or a number indicative of his or her general credit
worthiness. Both the credit rating and credit score may be used to
determine the customer's overall financial risk to a lending
institution, such as the vendor offering the asset for sale.
[0183] In some embodiments, the financial profile includes customer
longevity, customer loyalty, or some combination thereof. The
customer longevity may be how long a customer has been a customer
of a vendor. The customer loyalty may be a metric used by a vendor
to indicate how loyal a customer is to the vendor. The metric may
include how long the customer has been a customer of the vendor,
how frequently the customer shops at the vendor, how much money the
customer spends at the vendor, or some combination thereof.
[0184] In some embodiments the financial profile for the customer
includes a predicted life event, and the loan offer is based upon
the predicted life event. Similarly, the predicted life event may
be the customer or the customer's children graduating from high
school or college. The predicted life event may be an age-related
event, and the loan offer may be an offer for a vehicle loan. The
age-related event may be the customer or a member of the customer's
family turning a certain age. In other embodiments, the predicted
life event includes a birth of a child, a marriage, an increase in
income, or a move to a new residence for the customer.
Exemplary Use Case for Presenting Targeted Loan Offers
[0185] FIG. 14 illustrates an exemplary use case 1400 for
presenting targeted loan offers to customers via a mobile device.
FIG. 14 includes a first time period 1402, a second time period
1404, a client device 102, a retailer 160, and a boundary 161. The
use case 1400 illustrated in FIG. 14 may be an example operation of
the disclosed systems and methods for presenting targeted loan
offers to a customer via a mobile device. During time period 1402,
a user (not shown) may be in possession of client device 102.
Client device 102 is in relative proximity to retailer 160.
However, client device 102 has not crossed boundary 161. Boundary
161 may be the geofence described above as determined by client
device 102, personalized banking engine 120, retailer 160, or some
combination thereof. During time period 1402, personalized banking
engine 120, or client device 102, may generate a financial profile
for the customer. Personalized banking engine 120, or client device
102, may use any of the different types of methods described above
to generate the financial profile. In some embodiments, the
financial profile is not built until after the user has crossed
boundary 161.
[0186] At time period 1404, the user and client device 102 have
crossed boundary 161 and are located within proximity of retailer
160. Between time periods 1402 and 1404, the customer mobile device
may receive multiple locations for the customer. At time 1404,
personalized banking engine 120, or client device 102, may
determine that the user is located within a threshold distance of
retailer 160 and a predetermined distance of the asset listed for
sale. The multiple received locations for the customer mobile
device may be over a period of time, and be derived from the
customer driving by the asset listed for sale multiple times over
the period of time. Personalized banking engine 120, or client
device 102, may determine a personalized loan offer to transmit to
the customer (via his or her mobile device) for financing that the
customer can take advantage of to purchase the asset listed for
sale. Personalized banking engine 120, or client device 102, may
determine the loan offer by analyzing the customer's financial
profile. Once the loan offer is determined the loan offer may be
transmitted to the customer via his or her mobile device.
TECHNICAL ADVANTAGES
[0187] The aspects described herein may be implemented as part of
one or more computer components (such as a client device) and/or
one or more back-end components (such as a personalized banking
engine), for example. Furthermore, the aspects described herein may
be implemented as part of a computer network architecture and/or a
cognitive computing (or machine learning) architecture that
facilitates communications between various other devices and/or
components. Thus, the aspects described herein address and solve
issues of a technical nature that are necessarily rooted in
computer technology.
[0188] For instance, some aspects include analyzing various sources
of data to predict whether a user will likely spend money at a
particular retailer, and how much. Once this is determined, the
aspects may also allow for a determination of whether the predicted
transaction amount will negatively influence a user's financial
accounts. In doing so, the aspects may overcome issues associated
with the inconvenience of manual and/or unnecessary funds
transfers. Without the improvements suggested herein, additional
processing and memory usage may be required to perform such
transfers, as a client device may need to download additional data
and process this data as part of the transfer process.
[0189] Furthermore, the embodiments described herein may function
to optimize the interest accrued across a user's accounts. The
process may improve upon existing technologies by more accurately
forecasting a user's account balance using additional data sources.
Due to this increase in accuracy, the aspects may address
computer-related issues regarding efficiency over the traditional
amount of processing power and models used to forecast financial
data. Thus, the aspects may also address computer related issues
that are related to efficiency metrics, such as consuming less
power, for example.
ADDITIONAL CONSIDERATIONS
[0190] This detailed description is to be construed as exemplary
only and does not describe every possible embodiment, as describing
every possible embodiment would be impractical, if not impossible.
One may implement numerous alternate embodiments, using either
current technology or technology developed after the filing date of
this application.
[0191] Further to this point, although the embodiments described
herein often utilize credit report information as an example of
sensitive information, the embodiments described herein are not
limited to such examples. Instead, the embodiments described herein
may be implemented in any suitable environment in which it is
desirable to identify and control specific type of information. For
example, the aforementioned embodiments may be implemented by a
financial institution to identify and contain bank account
statements, brokerage account statements, tax documents, etc. To
provide another example, the aforementioned embodiments may be
implemented by a lender to not only identify, re-route, and
quarantine credit report information, but to apply similar
techniques to prevent the dissemination of loan application
documents that are preferably delivered to a client for signature
in accordance with a more secure means (e.g., via a secure login to
a web server) than via email.
[0192] Furthermore, although the present disclosure sets forth a
detailed description of numerous different embodiments, it should
be understood that the legal scope of the description is defined by
the words of the claims set forth at the end of this patent and
equivalents. The detailed description is to be construed as
exemplary only and does not describe every possible embodiment
since describing every possible embodiment would be impractical.
Numerous alternative embodiments may be implemented, using either
current technology or technology developed after the filing date of
this patent, which would still fall within the scope of the claims.
Although the following text sets forth a detailed description of
numerous different embodiments, it should be understood that the
legal scope of the description is defined by the words of the
claims set forth at the end of this patent and equivalents. The
detailed description is to be construed as exemplary only and does
not describe every possible embodiment since describing every
possible embodiment would be impractical. Numerous alternative
embodiments may be implemented, using either current technology or
technology developed after the filing date of this patent, which
would still fall within the scope of the claims.
[0193] The following additional considerations apply to the
foregoing discussion. Throughout this specification, plural
instances may implement components, operations, or structures
described as a single instance. Although individual operations of
one or more methods are illustrated and described as separate
operations, one or more of the individual operations may be
performed concurrently, and nothing requires that the operations be
performed in the order illustrated. Structures and functionality
presented as separate components in example configurations may be
implemented as a combined structure or component. Similarly,
structures and functionality presented as a single component may be
implemented as separate components. These and other variations,
modifications, additions, and improvements fall within the scope of
the subject matter herein.
[0194] Additionally, certain embodiments are described herein as
including logic or a number of routines, subroutines, applications,
or instructions. These may constitute either software (e.g., code
embodied on a machine-readable medium or in a transmission signal)
or hardware. In hardware, the routines, etc., are tangible units
capable of performing certain operations and may be configured or
arranged in a certain manner. In exemplary embodiments, one or more
computer systems (e.g., a standalone, client or server computer
system) or one or more hardware modules of a computer system (e.g.,
a processor or a group of processors) may be configured by software
(e.g., an application or application portion) as a hardware module
that operates to perform certain operations as described
herein.
[0195] In various embodiments, a hardware module may be implemented
mechanically or electronically. For example, a hardware module may
comprise dedicated circuitry or logic that is permanently
configured (e.g., as a special-purpose processor, such as a field
programmable gate array (FPGA) or an application-specific
integrated circuit (ASIC)) to perform certain operations. A
hardware module may also comprise programmable logic or circuitry
(e.g., as encompassed within a general-purpose processor or other
programmable processor) that is temporarily configured by software
to perform certain operations. It will be appreciated that the
decision to implement a hardware module mechanically, in dedicated
and permanently configured circuitry, or in temporarily configured
circuitry (e.g., configured by software), may be driven by cost and
time considerations.
[0196] Accordingly, the term "hardware module" should be understood
to encompass a tangible entity, be that an entity that is
physically constructed, permanently configured (e.g., hardwired),
or temporarily configured (e.g., programmed) to operate in a
certain manner or to perform certain operations described herein.
Considering embodiments in which hardware modules are temporarily
configured (e.g., programmed), each of the hardware modules need
not be configured or instantiated at any one instance in time. For
example, where the hardware modules comprise a general-purpose
processor configured using software, the general-purpose processor
may be configured as respective different hardware modules at
different times. Software may accordingly configure a processor,
for example, to constitute a particular hardware module at one
instance of time and to constitute a different hardware module at a
different instance of time.
[0197] Hardware modules may provide information to, and receive
information from, other hardware modules. Accordingly, the
described hardware modules may be regarded as being communicatively
coupled. Where multiple of such hardware modules exist
contemporaneously, communications may be achieved through signal
transmission (e.g., over appropriate circuits and buses) that
connect the hardware modules. In embodiments in which multiple
hardware modules are configured or instantiated at different times,
communications between such hardware modules may be achieved, for
example, through the storage and retrieval of information in memory
structures to which the multiple hardware modules have access. For
example, one hardware module may perform an operation and store the
output of that operation in a memory device to which it is
communicatively coupled. A further hardware module may then, at a
later time, access the memory device to retrieve and process the
stored output. Hardware modules may also initiate communications
with input or output devices, and may operate on a resource (e.g.,
a collection of information).
[0198] The various operations of example methods described herein
may be performed, at least partially, by one or more processors
that are temporarily configured (e.g., by software) or permanently
configured to perform the relevant operations. Whether temporarily
or permanently configured, such processors may constitute
processor-implemented modules that operate to perform one or more
operations or functions. The modules referred to herein may, in
some example embodiments, comprise processor-implemented
modules.
[0199] Similarly, the methods or routines described herein may be
at least partially processor-implemented. For example, at least
some of the operations of a method may be performed by one or more
processors or processor-implemented hardware modules. The
performance of certain of the operations may be distributed among
the one or more processors, not only residing within a single
machine, but deployed across a number of machines. In some example
embodiments, the processor or processors may be located in a single
location (e.g., within a home environment, an office environment or
as a server farm), while in other embodiments the processors may be
distributed across a number of locations.
[0200] The performance of some of the operations may be distributed
among the one or more processors, not only residing within a single
machine, but deployed across a number of machines. In some example
embodiments, the one or more processors or processor-implemented
modules may be located in a single geographic location (e.g.,
within a home environment, an office environment, or a server
farm). In other example embodiments, the one or more processors or
processor-implemented modules may be distributed across a number of
geographic locations.
[0201] Unless specifically stated otherwise, discussions herein
using words such as "processing," "computing," "calculating,"
"determining," "presenting," "displaying," or the like may refer to
actions or processes of a machine (e.g., a computer) that
manipulates or transforms data represented as physical (e.g.,
electronic, magnetic, or optical) quantities within one or more
memories (e.g., volatile memory, non-volatile memory, or a
combination thereof), registers, or other machine components that
receive, store, transmit, or display information.
[0202] As used herein any reference to "one embodiment" or "an
embodiment" means that a particular element, feature, structure, or
characteristic described in connection with the embodiment is
included in at least one embodiment. The appearances of the phrase
"in one embodiment" in various places in the specification are not
necessarily all referring to the same embodiment.
[0203] Some embodiments may be described using the expression
"coupled" and "connected" along with their derivatives. For
example, some embodiments may be described using the term "coupled"
to indicate that two or more elements are in direct physical or
electrical contact. The term "coupled," however, may also mean that
two or more elements are not in direct contact with each other, but
yet still co-operate or interact with each other. The embodiments
are not limited in this context.
[0204] As used herein, the terms "comprises," "comprising,"
"includes," "including," "has," "having" or any other variation
thereof, are intended to cover a non-exclusive inclusion. For
example, a process, method, article, or apparatus that comprises a
list of elements is not necessarily limited to only those elements
but may include other elements not expressly listed or inherent to
such process, method, article, or apparatus. Further, unless
expressly stated to the contrary, "or" refers to an inclusive or
and not to an exclusive or. For example, a condition A or B is
satisfied by any one of the following: A is true (or present) and B
is false (or not present), A is false (or not present) and B is
true (or present), and both A and B are true (or present).
[0205] In addition, use of the "a" or "an" are employed to describe
elements and components of the embodiments herein. This is done
merely for convenience and to give a general sense of the
description. This description, and the claims that follow, should
be read to include one or at least one and the singular also
includes the plural unless it is obvious that it is meant
otherwise.
[0206] The patent claims at the end of this patent application are
not intended to be construed under 35 U.S.C. .sctn. 112(f) unless
traditional means-plus-function language is expressly recited, such
as "means for" or "step for" language being explicitly recited in
the claim(s).
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