U.S. patent application number 15/499089 was filed with the patent office on 2021-11-18 for predicting when a user is in need of a loan and notifying the user of loan offers.
The applicant listed for this patent is STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY. Invention is credited to Melissa Attig, Reena Batra, Puneit Dua, Elizabeth Flowers, Adam Mattingly, Alan Zwilling.
Application Number | 20210358030 15/499089 |
Document ID | / |
Family ID | 1000002606448 |
Filed Date | 2021-11-18 |
United States Patent
Application |
20210358030 |
Kind Code |
A1 |
Flowers; Elizabeth ; et
al. |
November 18, 2021 |
PREDICTING WHEN A USER IS IN NEED OF A LOAN AND NOTIFYING THE USER
OF LOAN OFFERS
Abstract
Techniques are disclosed to determine when a user is in need of
a loan and notifying the user of loan offers. With user permission
or affirmative consent, user data may be monitored for several
users, which is used to build a user profile for each user. The
user profile may then be analyzed to determine whether a user will
require a loan within a future time period. To do so, the user data
may include data from various sources, which indicate the user's
interactions and behaviors such as demographic data, data
indicative of user shopping habits, online browsing, life events,
or other relevant behaviors. This data may then be analyzed to
predict a statistical likelihood that a user will need a loan. When
this statistical likelihood is exceeded, a user may be preapproved
for a loan and/or a targeted notification may be sent indicating
offers for certain types of loans.
Inventors: |
Flowers; Elizabeth;
(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: |
1000002606448 |
Appl. No.: |
15/499089 |
Filed: |
April 27, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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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 40/025 20130101;
G06N 7/005 20130101 |
International
Class: |
G06Q 40/02 20060101
G06Q040/02; G06N 7/00 20060101 G06N007/00 |
Claims
1. A computer-implemented method comprising: training, by one or
more processors, a machine learning model to determine a set of
weights based on historical profiles associated with a set of
users, wherein the set of weights indicate correlations between: a
set of user inputs indicated in the historical profiles, and
instances of individual users, in the set of users, obtaining
loans; receiving, by the one or more processors, user input data
associated with a user; generating, by the one or more processors,
a user profile based upon the user input data; predicting, by the
one or more processors and using the machine learning model, a
statistical likelihood that the user will require a loan within a
future time period, by: identifying at least one user input, of the
set of user inputs, indicated in the user profile; determining at
least one weight, of the set of weights, associated with the at
least one user input; and determining the statistical likelihood
based on the at least one weight; determining, by the one or more
processors, that the statistical likelihood exceeds a threshold
likelihood; and based at least in part on the statistical
likelihood exceeding the threshold likelihood, transmitting, by the
one or more processors, a notification to a computing device
associated with the user, wherein the notification includes one or
more offers for one or more customized loans.
2. The computer-implemented method of claim 1, further comprising:
calculating, by the one or more processors and based at least in
part on the user profile, a set of probabilities associated with a
range of loan amounts, wherein probabilities in the set of
probabilities indicate respective likelihoods that the loan will be
for corresponding loan amounts; identifying, by the one or more
processors, a loan amount associated with a highest probability of
the set of probabilities; and preapproving, by the one or more
processors, the user for the loan amount.
3. The computer-implemented method of claim 1, further comprising:
receiving, by the one or more processors as part of the user input
data, a web browsing history associated with the user; identifying,
by the one or more processors, one or more of search terms and
websites from the web browsing history that are relevant to the
user; and storing, by the one or more processors, the one or more
of the search terms and websites as part of the user profile,
wherein the statistical likelihood is based at least in part upon
the one or more of the search terms and websites.
4. The computer-implemented method of claim 1, wherein the user
input data indicates a current age of a family member of the user,
the method further comprising: determining, by the one or more
processors and based at least in part on the current age, a number
of days until the family member reaches a legal driving age; and
determining, by the one or more processors, that the statistical
likelihood exceeds the threshold likelihood based at least in part
on the number of days being less than a threshold number of
days.
5. The computer-implemented method of claim 1, wherein the user
input data indicates one or more life events associated with the
user.
6. The computer-implemented method of claim 1, wherein the
notification is one or more of: (i) a text message, (ii) an email
message, or (iii) a push notification.
7. (canceled)
8. A computer 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: train
a machine learning model to determine a set of weights based on
historical profiles associated with a set of users, wherein the set
of weights indicates correlations between: a set of user inputs
indicated in the historical profiles, and instances of individual
users, in the set of users, obtaining loans; receive user input
data from a client device associated with a user; generate a user
profile based upon the user input data; predict, using the machine
learning model, a statistical likelihood that the user will require
a loan within a future time period, by identifying at least one
user input, of the set of user inputs, indicated in the user
profile; determining at least one weight, of the set of weights,
associated with the at least one user input; and determining the
statistical likelihood based on the at least one weight; determine
that the statistical likelihood exceeds a threshold likelihood; and
based at least in part on the statistical likelihood exceeding the
threshold likelihood, transmit a notification to the client device,
wherein the notification includes one or more offers for one or
more customized loans.
9. The computer system of claim 8, wherein the computer-executable
instructions further cause the one or more processors to:
calculate, based at least in part on the user profile, a set of
probabilities associated with a range of loan amounts, wherein
probabilities in the set of probabilities indicate respective
likelihoods that the loan will be for corresponding loan amounts;
identify a loan amount associated with a highest probability of the
set of probabilities; and preapprove the user for the loan
amount.
10. The computer system of claim 8, wherein: the user input data
includes a web browsing history associated with the user, the
computer-executable instructions further cause the one or more
processors to: (i) identify one or more of search terms and
websites from the web browsing history that are relevant to the
user requiring the loan, and (ii) store the one or more of the
search terms and websites as part of the user profile, and the
statistical likelihood is based at least in part upon the one or
more of the search terms and websites.
11. The computer system of claim 8, wherein the user input data
indicates a current age of a family member of the user, and the
computer-executable instructions further cause the one or more
processors to: (i) determine, based at least in part on the current
age, a number of days until the family member reaches a legal
driving age, and (ii) determine that the statistical likelihood
exceeds the threshold likelihood based at least in part on when the
number of days being less than a threshold number of days.
12. The computer system of claim 8, wherein the user input data
indicates one or more life events associated with the user.
13. The computer system of claim 8, wherein the notification is one
or more of: (i) a text message, (ii) an email message, or (iii) a
push notification.
14. (canceled)
15. A non-transitory computer-readable medium storing instructions
thereon that, when executed by one or more processors, cause the
one or more processors to perform operations comprising: training a
machine learning model to determine a set of weights based on
historical profiles associated with a set of users, wherein the set
of weights indicate correlations between: a set of user inputs
indicated in the historical profiles, and instances of individual
users, in the set of users, obtaining loans; receiving user input
data associated with a user from a mobile device of the user;
generating a user profile based upon the user input data;
predicting, using the machine learning model, a statistical
likelihood that the user will require a loan within a future time
period, by: identifying at least one user input, of the set of user
inputs, indicated in the user profile; determining at least one
weight, of the set of weights, associated with the at least one
user input; and determining the statistical likelihood based on the
at least one weight; determining that the statistical likelihood
exceeds a threshold likelihood; and based at least in part on the
statistical likelihood exceeding the threshold likelihood,
transmitting a notification to the mobile device, wherein the
notification includes one or more offers for one or more customized
loans.
16. The non-transitory computer-readable medium of claim 15,
wherein the operations further comprise: receiving, as part of the
user input data, a web browsing history associated with the user;
identifying one or more of search terms and web sites from the web
browsing history that are relevant to the user requiring the loan;
and storing the one or more of the search terms and web sites as
part of the user profile, wherein the statistical likelihood is
based at least in part upon the one or more of the search terms and
websites.
17. The non-transitory computer-readable medium of claim 15,
wherein the user input data indicates a current age of a family
member of the user, and the operations further comprise:
determining, based at least in part on the current age, a number of
days until the family member reaches a legal driving age; and
determining that the statistical likelihood exceeds the threshold
likelihood based at least in part on the number of days being less
than a threshold number of days.
18. The non-transitory computer-readable medium of claim 15,
wherein the user input data indicates one or more life events
associated with the user.
19. (canceled)
20. (canceled)
21. The computer-implemented method of claim 1, wherein the
computing device associated with the user is a mobile device, and
the one or more processors receives the user input data from the
mobile device.
22. The computer-implemented method of claim 1, further comprising
determining, by the one or more processors, at least one of a loan
type, a loan term, and a monetary amount associated with the one or
more customized loans.
23. The computer-implemented method of claim 1, wherein the
threshold likelihood comprises a first threshold likelihood, the
method further comprising: determining, by the one or more
processors, that the statistical likelihood exceeds a second
threshold likelihood, the second threshold likelihood being higher
than the first threshold likelihood; and pre-approving, by the one
or more processors, the user for the one or more customized loans
based on determining that the statistical likelihood exceeds the
second threshold likelihood.
24. The computer-implemented method of claim 1, further comprising:
determining, by the one or more processors, that the at least one
user input corresponds to a predetermined condition associated with
a particular type of loan; and determining, by the one or more
processors, that the statistical likelihood exceeds the threshold
likelihood, based on determining that the at least one user input
corresponds to the predetermined condition, wherein the
notification includes an offer associated with the particular type
of loan.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to (1) Provisional
Application No. 62/338,749, entitled "Using Cognitive Computing To
Customize Loans," filed on May 19, 2016; (2) Provisional
Application No. 62/332,226, entitled "Using Cognitive Computing To
Provide a Personalized Banking Experience," filed on May 5, 2016;
(3) Provisional Application No. 62/338,752, entitled "Using
Cognitive Computing To Provide a Personalized Banking Experience,"
filed on May 19, 2016; and (4) Provisional Application No.
62/341,677, entitled "Using Cognitive Computing To Improve
Relationship Pricing," filed on May 26, 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 predicting a user's
loan needs and proactively notifying the user of loan offers.
BACKGROUND
[0003] Traditionally, to apply for various types of loans, people
would visit a brick-and-mortar bank or other financial institution
to meet with a loan officer or other representative, submit the
necessary documentation and, if approved, sign a loan agreement
defining the loan specifics, such as the loan term and rate. More
recently, the loan application and approval process has been
simplified by providing users with online options to apply, and
potentially be approved for, various types of loans. Therefore,
because of the ease in which people may apply for loans and compare
interest rates among different lenders, the competition amongst
lenders has dramatically increased.
[0004] This competition has increased the importance of advertising
to users and identifying potential clients that may require loans.
However, typical methods of doing so are often arduous and time
consuming, and may include procedures such as manual review of
client information, phone calls to clients or potential clients,
and sending out promotional materials or mailers via the postal
service. Therefore, the identification of clients that may require
loans is valuable but may require time and resources that may not
be recouped by the lender.
BRIEF SUMMARY
[0005] In one aspect, a computer-implemented method for predicting
when a user requires a loan and presenting the user with a loan
offer may be provided. The method may include one or more
processors (and/or associated transceivers) (1) receiving user
input data associated with a user; (2) generating a user profile
based upon the user input data and being indicative of whether the
user will require a loan within the future time period based upon
the user profile; (3) calculating a statistical likelihood of the
user requiring the loan within a future time period based upon the
user profile; (4) determining whether the statistical likelihood
exceeds a threshold likelihood; and/or (5) when the statistical
likelihood exceeds a threshold likelihood, transmitting a
notification to a computing device associated with the user
including offers for one or more customized loans. The method may
include additional, less, or alternate actions, including those
discussed elsewhere herein.
[0006] In yet another aspect, a system for predicting when a user
requires a loan and presenting the user with a loan offer may be
provided. The system may include (1) a client device associated
with a user, which may be configured to periodically transmit user
input data associated with the user; and (2) one or more back-end
components configured to (i) receive the user input data; (ii)
generate a user profile based upon the received user input data and
being indicative of whether the user will require a loan within a
future time period; (iii) calculate a statistical likelihood of the
user requiring a loan within a future time period based upon the
user profile; (iv) determine whether the statistical likelihood
exceeds a threshold likelihood; and/or (v) when the statistical
likelihood exceeds a threshold likelihood, transmit a notification
to the client device including offers for one or more customized
loans. The system may include additional, less, or alternate
components, including those discussed elsewhere herein.
[0007] In still another aspect, a computer-implemented method for
predicting when a user requires a loan and presenting the user with
a loan offer may be provided. The method may include one or more
processors (and/or associated transceivers) (1) receiving user
input data associated with a user from a mobile device via wireless
communication or data transmission over one or more radio links or
wireless communication channels; (2) generating a user profile
based upon the user input data, the user profile including data
indicative of whether the user will require a loan within a future
time period; (3) calculating a statistical likelihood of the user
requiring the loan within the future time period based upon the
user profile; (4) determining whether the statistical likelihood
exceeds a threshold likelihood; and/or (5) when the statistical
likelihood exceeds a threshold likelihood, transmitting a
notification to the mobile device associated with the user via
wireless communication or data transmission over one or more radio
links or wireless communication channels. The notification may
include, for example, offers for one or more customized loans. The
method may include additional, less, or alternate actions,
including those discussed elsewhere herein.
[0008] In an additional aspect, a computer-implemented method for
predicting when a user requires a loan and presenting the user with
a loan offer may be provided. The method may include one or more
processors (and/or associated transceivers) (1) receiving user
input data associated with a user from a user mobile device via
wireless communication or data transmission over one or more radio
links or wireless communication channels; (2) generating a user
profile based upon the user input data and being indicative of
whether the user will require a loan within a future time period;
(3) calculating a statistical likelihood of the user requiring the
loan within the future time period based upon the user profile; (4)
determining whether the statistical likelihood exceeds a threshold
likelihood; and/or (5) when the statistical likelihood exceeds a
threshold likelihood, transmitting a notification to the user
mobile device for one or more loan offers via wireless
communication or data transmission over one or more radio links or
wireless communication channels to facilitate providing customized
loans. The method may include additional, less, or alternate
actions, including those discussed elsewhere herein.
[0009] In yet another aspect, a system for predicting when a user
requires a loan and presenting the user with a loan offer may be
provided. The system may include (1) a mobile device associated
with a user, the mobile device configured to periodically transmit
user input data associated with the user; and (2) one or more
back-end components configured to (i) receive the user input data
transmitted by the mobile device via wireless communication or data
transmission over one or more radio links or wireless communication
channels; (ii) generate a user profile based upon the received user
input data and being indicative of whether the user will require a
loan within a future time period; (iii) calculate a statistical
likelihood of the user requiring a loan within the future time
period based upon the user profile; (iv) determine whether the
statistical likelihood exceeds a threshold likelihood; and/or (v)
when the statistical likelihood exceeds a threshold likelihood,
transmit a notification to the user mobile device for user review
via wireless communication or data transmission over one or more
radio links or wireless communication channels, the notification
detailing one or more loan offers, to facilitate providing
customized loans. The system may include additional, less, or
alternate components, including those discussed elsewhere
herein.
[0010] 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
[0011] FIG. 1 is a block diagram of an exemplary loan prediction
and notification system 100 in accordance with one aspect of the
present disclosure;
[0012] FIG. 2 illustrates exemplary user profiles 200 in accordance
with one aspect of the present disclosure;
[0013] FIG. 3 illustrates exemplary logic diagrams 300 indicating
the occurrence of several example conditions and their
corresponding impact on various predictions in accordance with one
aspect of the present disclosure; and
[0014] FIG. 4 illustrates an exemplary computer-implemented method
flow 400 in accordance with one aspect of the present
disclosure.
[0015] 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
[0016] The present aspects relate to, inter alia, using cognitive
computing and/or predictive modeling (and/or machine learning
techniques or algorithms) to identify when one or more users may be
looking for, or in need of, a loan. To accomplish this, user input
data may be collected (with user permission or affirmative consent)
and stored by one or more back-end components and used to construct
a user profile. This data may include, for example, demographic
data associated with the user such as the user's age, the age or
birthdate of each member of the user's family, financial data,
location data, and/or data indicative of various life events. In
addition, the one or more back-end components may collect other
types of data, such as data indicative of the user's behavior,
including online browsing history, spending habits, etc. Using any
portion of this data, a statistical likelihood may be calculated
regarding whether the user requires or is actively looking for a
loan. If this statistical likelihood exceeds a threshold value,
then the one or more back-end components may transmit a targeted
notification to the user indicating various loan offers.
System Overview
[0017] FIG. 1 is a block diagram of an exemplary loan prediction
and notification system 100 in accordance with one aspect of the
present disclosure. In the present aspect, loan prediction and
notification system 100 may include one or more client devices 102,
a loan prediction and notification engine 120, one or more
financial institutions 150, and/or a communication network 116.
Loan prediction and notification system 100 may include additional,
less, or alternate components, including those discussed elsewhere
herein.
[0018] For the sake of brevity, loan prediction and notification
system 100 is illustrated as including a single client device 102,
a single loan prediction and notification engine 120, two financial
institutions 150, and a single communication network 116. However,
the aspects described herein may include any suitable number of
such components. For example, loan prediction and notification
engine 120 may communicate with several client devices 102, each of
which may be operated by a separate user, to receive data from each
separate client device 102 and/or to transmit notifications to each
separate client device 102, as further discussed herein.
[0019] To provide another example, loan prediction and notification
engine 120 may receive data from one or more client devices 102
such that a user profile for each user may include data received
from each user's respective client device. To provide yet another
example, client device 102 may represent one client device from
several different client devices for the same user or for different
users. For example, client device 102 may represent a user's
smartphone as well as a user's desktop computer, each of which may
collect and transmit data to one or more financial institutions 150
and/or loan prediction and notification engine 120, as further
discussed below.
[0020] Communication network 116 may be configured to facilitate
communications between one or more client devices 102, one or more
financial institutions 150, and/or loan prediction and notification
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,
radio frequency links, wireless or digital communication channels,
additional wired and/or wireless networks that may facilitate one
or more landline connections, internet service provider (ISP)
backbone connections, satellite links, public switched telephone
network (PSTN), etc.
[0021] To facilitate communications between the various components
of loan prediction and notification system 100, the present aspects
include communication network 116 being implemented, for example,
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 116 may include wired telephone and cable
hardware, satellite, cellular phone communication networks, base
stations, macrocells, femtocells, etc. In the present aspects,
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 loan prediction and
notification engine 120.
[0022] 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 loan prediction and notification engine 120 via
communication network 116 using one or more of radio links or radio
frequency links 117.1-117.3 or wireless communication channels.
[0023] 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 or other suitable mobile computing device, 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.
[0024] As further discussed below, data collected and/or
transmitted by client device 102 to one or more financial
institutions 150 and/or loan prediction and notification engine 120
may include, for example, any suitable or relevant information used
by loan prediction and notification engine 120 to track a location
of client device 102 and/or to track other types of information
about users associated with client device 102 to anticipate whether
the user associated with client device 102 may require a loan.
Additionally or alternatively, data collected and/or transmitted by
client device 102 to one or more financial institutions 150 and/or
loan prediction and notification engine 120 may include data used
by loan prediction and notification engine 120 to preapprove a user
for a loan and/or to calculate an adjusted loan rate or other loan
specifics for a particular user.
[0025] For example, with user permission, such as user opt-in to a
rewards or other program that provides financial benefits or cost
savings to the user, client device 102 may collect, monitor, store,
and/or transmit demographic information, data indicative of the
user's behavior such as spending habits, where the user has shopped
physically and/or online, data indicative of certain life events,
financial information such as account balances of one or more users
associated with client device 102, online web browsing history,
life event data, etc. This data is discussed in more detail below
with reference to FIG. 2.
[0026] Furthermore, data received by client device 102 from loan
prediction and notification engine 120 may include any suitable
information used to notify the user of relevant loan offers, the
result of a loan preapproval, whether the user qualifies for a
particular loan product and, if so, the particular loan products
the user may qualify for. Additionally or alternatively, data
received by client device 102 from loan prediction and notification
engine 120 may facilitate providing user notifications regarding
specific loan terms and/or loan rates, whether a particular loan
includes personalized rates or other terms, and the specific
details about how the customized loan details were adjusted for
that user.
[0027] For example, if it is determined by loan prediction and
notification engine 120 that a user associated with client device
102 is likely to require a loan, then client device 102 may display
a notification transmitted via loan prediction and notification
engine 120 regarding loan offers of a specific loan type and amount
before the user has obtained the loan. To provide another example,
if a user applies for a loan via client device 102 or in another
manner, loan prediction and notification engine 120 may calculate
user-specific loan specifics that take into account changes in the
user's statistical risk during the loan term, which is further
discussed below. Assuming the loan is approved, loan prediction and
notification engine 120 may adjust the initial loan specifics based
upon this additional information and transmit this information to
client device 102, which may in turn display a suitable
notification including the details of the personalized loan
specifics.
Detailed Operation of Loan Customization System
[0028] In the present aspects, 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.
[0029] Communication unit 106 may be configured to facilitate data
communications between client device 102 and one or more of
communication network 116, one or more financial institutions 150,
and/or loan prediction and notification engine 120 in accordance
with any suitable number and/or type of communication protocols. In
the present aspects, 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.
[0030] Such communications may facilitate the transmission of
collected data from client device 102 that is utilized by loan
prediction and notification engine 120 to provide loan
customization and/or to predict when a user associated with client
device 102 is likely in the market for a new loan, 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.
[0031] 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.
[0032] 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
regarding available loan offers, preapproval results, the terms of
a particular customized loan, which may be received, for example,
via loan prediction and notification engine 120, etc.
[0033] 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
Russian Federation, the BeiDou system primarily used in China,
and/or the Galileo system primarily used in Europe.
[0034] 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.
[0035] 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.
[0036] 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 (e.g., a random
access memory (RAM)), or a non-volatile memory (e.g.,
battery-backed RAM, FLASH, etc.). In the present aspects, 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.
[0037] In the present aspects, loan 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 aspects as described herein. For example, instructions
stored in loan application 115 may facilitate one or more
processors 104 performing functions such as periodically reporting
and/or transmitting the location of client device 102 as part of a
running background process (or causing location acquisition unit
112 to do so), collecting various types of data, sending various
types of data to one or more financial institutions 150 and/or loan
prediction and notification engine 120, receiving data and/or
notifications from one or more financial institutions 150 and/or
loan prediction and notification engine 120, displaying
notifications and/or other information using data received via one
or more financial institutions 150 and/or loan prediction and
notification engine 120, etc.
[0038] In some aspects, loan 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, loan 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
source via a connection to the Internet or other suitable device,
network, external memory storage device, etc.
[0039] For example, loan 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,
loan application 115 may be installed on client device 102 as part
of an installation package such that, upon installation of loan
application 115, memory unit 114 may store executable instructions
such that, when executed by one or more processors 104, cause
client device 102 to implement the various functions of the aspects
as described herein.
[0040] The user may initially create a user profile upon first
launching loan application 115, 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. For example, upon installing and
launching loan 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 lender or
other relevant party associated with or otherwise affiliated with
loan prediction and notification engine 120.
[0041] Additionally or alternatively, loan application 115 may
periodically request data directly from the user as opposed to
collecting data in a passive manner. For example, loan application
115 may request certain types of information from the user as part
of one or more surveys and/or questionnaires. This information may
be requested, for example, upon an initial registration and/or at
any suitable time after the initial registration. In this way, a
user may be asked for certain types of information that may be
difficult to obtain through third party data providers and/or
public records. For example, a user may be asked about the ages of
his family members, his current income level (or where his earnings
fit within a range of earnings), his hobbies and interests, whether
he is planning to attend a university or other training program in
the near future and, if so, in what field of study, whether he
intends to make a large purchase in the near future, etc.
[0042] An initial registration process may additionally or
alternatively include, for example, obtaining the user's consent to
track her location or to otherwise collect and utilize other types
of data to provide the various loan customization and/or prediction
services as discussed herein. For example, a user may opt in to
allow loan prediction and notification engine 120 to track and/or
receive the user's financial account data including financial
transactions, spending history, credit card balances, web browsing
history, 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. In
return, the user may be presented with various offers or customized
financial products or loan offers, or other products discussed
herein.
[0043] In the present aspects, loan application 115 may provide
different levels of functionality based upon options selected by a
user and/or different implementations of loan application 115. For
example, in some aspects, loan application 115 may facilitate
client device 102 working in conjunction with one or more financial
institutions 150 and/or loan prediction and notification engine 120
to predict when a user requires a loan and/or to send data relevant
to these functions to loan prediction and notification engine 120.
Furthermore, in accordance with such aspects, loan application 115
may facilitate receiving notifications for offers regarding loans
anticipated by loan prediction and notification engine 120. To
provide another example, other aspects include loan application 115
facilitating client device 102 working in conjunction with one or
more financial institutions 150 and/or loan prediction and
notification engine 120 to collect and transmit data to one or more
financial institutions and/or loan prediction and notification
engine 120.
[0044] 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, lenders, and/or brokers. One or more users (e.g., a user
associated with client device 102) may hold one or more accounts
with one or more financial institutions 150 such as checking
accounts, savings accounts, credit accounts, lines of credit, loan
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.
[0045] 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 loan prediction and notification engine 120. For
example, one or more financial institutions 150 may provide online
services that allow a user to access her 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 loan prediction and notification engine 120.
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, loan payoff balances, credit report history, credit
scores, credit card utilization, derogatory credit marks, spending
data such as the time, amount, and specific merchant for which
previous account debits and/or charges were made, whether the user
has previously defaulted on a particular loan, etc.
[0046] Loan prediction and notification engine 120 may be
affiliated or otherwise associated with one or more parties, which
may be the same party or a different party than those affiliated
with one or more financial institutions 150. Loan prediction and
notification 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, loan
prediction and notification 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.
[0047] For example, as shown in FIG. 1, loan prediction and
notification engine 120 may communicate with one or more external
computing devices such as servers, databases, database servers, web
servers, etc. The present aspects include loan prediction and
notification 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.
[0048] In the present aspects, loan prediction and notification
engine 120 may be implemented, for example, as any suitable number
and/or type of servers 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 accessible by loan prediction
and notification engine 120 from any number N of data sources, such
as third-party data providers or other data sources in addition to
and/or including one or more financial institutions 150. This
additional data may be utilized by loan prediction and notification
engine 120 to build a personalized user profile for each user,
which may be stored in one or more suitable storage units (e.g.,
storage unit 180) and include other types of data, as further
discussed below. Loan prediction and notification engine 120 may
access each user's profile as part of the execution of one or more
cognitive computing and/or predictive modeling algorithms to
calculate a statistical likelihood that a particular user is
interested in acquiring a loan and/or to determine a specific loan
type and accompanying loan specifics for a particular user based
upon that user's profile.
[0049] To provide some illustrative examples, additional data
sources 170 may include, again with user permission, data mined
from social media and/or user's web browsing habits (e.g., search
terms and websites), 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,
mortgage loan information associated with various users, how much
users typically spend at various retailers, etc. To provide further
examples, additional data sources 170 may include specific
demographic information for a particular user and his family
members, such as the age and gender of each child, for example.
[0050] To provide even more examples, additional data sources 170
may include data indicative of various user life events such as
getting married, a child about to attend (or currently attending)
college, paying off a previous loan, receiving a settlement or
inheritance, etc. Still further, additional data sources 170 may
include user data such as spending habits or detailed information
such as a college or university the user may be attending, courses
being taken by the user, a college major or other focused field of
study, etc. Loan prediction and notification engine 120 may utilize
any portion of such data (as well as data from other data sources)
as input to one or more cognitive computing and/or predictive
modeling algorithms to perform the predictions and statistical
calculations as discussed herein, some examples of which are
further discussed below.
[0051] Loan prediction and notification engine 120 may be
implemented as any suitable number of servers that are configured
to generate and/or store various user profiles in storage unit 180.
Each user's profile may include, for example, an aggregation of
aforementioned data and/or any other suitable data that may be
utilized to predict whether a user is actively pursuing and/or
requires a loan for a new item purchase and/or data which may be
used to calculate loan terms associated with such predicted
loans.
[0052] For example, a user's profile may include demographic data,
data submitted by a user as part of an initial registration
process, data submitted by a user in response to solicited surveys,
spending data, financial data (e.g., credit card utilization,
credit card balances, bank account balances, etc.), credit report
data, credit scores, family or individual income, etc. Each user's
profile may be stored in storage unit 180 in any suitable manner
such that loan prediction and notification engine 120 may access
each user's profile and correlate each user to his or her
profile.
[0053] To provide an illustrative example, storage unit 180 may
include a number of user profiles organized in accordance with
suitable type of information to uniquely identify each particular
user so that each user may later be matched to her profile stored
in storage unit 180. For example, each user's profile may be
identified by a username that is used by one or more users in
accordance with loan application 115, a first and last name of each
user, etc. These user profiles are discussed in further detail
below with reference to FIG. 2.
[0054] In the present aspects, loan prediction and notification
engine 120 may include one or more processors 122, a communication
unit 124, and a memory unit 126. One or more processors 122,
communication unit 124, and memory unit 126 may perform
substantially similar functions as one or more processors 104,
communication unit 106, and memory unit 114, respectively, of
client device 102. Therefore, only differences between these
components will be further discussed herein.
[0055] Of course, differences between components of loan prediction
and notification 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 loan
prediction and notification 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.).
[0056] In the present aspects, loan prediction and notification
engine 120 may be configured to send or otherwise transmit various
types of notifications and/or inquiries to client device 102. These
notifications and/or inquiries may be sent, for example, from loan
prediction and notification engine 120 via communication unit 124,
and may include any suitable type of data transmissions. For
example, loan prediction and notification engine 120 may transmit
appropriate notifications and/or inquiries via emails, text
messages, push notifications, etc., to client device 102
[0057] Again, in various aspects, loan prediction and notification
engine 120 may acquire data from various sources to facilitate the
various aspects described herein. 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 user's financial account data
via any suitable authentication techniques, such as via a secure
connection, password authentication, public and/or private key
exchanges, biometric identification, etc.
[0058] Therefore, in the present aspects, loan prediction and
notification engine 120 may, when appropriate, implement any
suitable techniques to obtain information in a legal and
technically feasible manner. For example, as discussed above, a
user may setup an account and/or profile with a third party
associated with loan prediction and notification engine 120. As
discussed above, the user may then opt in to data collection via
the various data sources that are to be collected via loan
prediction and notification engine 120. 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., such that loan prediction and
notification engine 120 may obtain any suitable type of data to
carry out the aspects described herein.
Cognitive Computing
[0059] 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.
[0060] Therefore, in various aspects, loan prediction and
notification 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. For example, loan prediction and
notification 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, code, logic, and/or instructions to facilitate the
behavior, functionality, and/or processing of a cognitive computing
system.
[0061] 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 may
include a data aggregation module 129 and a loan prediction module
131. These modules are for illustrative purposes and represent
examples of some of the functionality that may be performed by loan
prediction and notification engine 120 in accordance with a
cognitive computing-based system. However, aspects include
cognitive computing and predictive modeling application 127
including additional, less, or alternate actions, including those
discussed elsewhere herein. Furthermore, aspects include cognitive
computing and predictive modeling application 127 implementing
traditional, non-cognitive computing processes.
[0062] In one aspect, data aggregation module 129 may be 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 loan prediction and
notification engine 120 providing the requested authorization to
one or more financial institutions 150 and/or additional data
sources 170 as needed and to receive data from one or more of these
sources.
[0063] In the present aspects, instructions stored in data
aggregation module 129 may facilitate loan prediction and
notification engine 120 aggregating and organizing received data
into one or more user profiles, which may then be stored in storage
unit 180. Again, these user profiles may be associated with each
user and organized such that data contained as part of each user's
profile may be associated with each user. For example, each user's
username and/or other suitable identifying information may be
stored as part of a user profile that includes an aggregation of
all data received for that particular user from one or more data
sources, as previously discussed.
[0064] In one aspect, loan prediction module 131 may be 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 loan prediction module 131 may facilitate
one or more processors 122 performing functions such as calculating
a statistical likelihood of the user requiring a loan within a
future time period based upon the user's profile. To do so, aspects
include loan prediction and notification engine 120 accessing
and/or receiving one or more inputs that may be stored as part of
the user's profile.
[0065] One or more processors 122 may be configured to execute
instructions stored in loan prediction module 131 to interpret,
organize, weight, and/or analyze these inputs in accordance with
any suitable cognitive computing and/or predictive modeling
techniques. For example, instructions stored in loan prediction
module 131 may facilitate one or more processors 122 weighting
these inputs as part of a weighting function, and calculating, as
an output of the weighting function, a statistical likelihood of
the user requiring a loan within a future time period. To provide
another example, instructions stored in loan prediction module 131
may facilitate one or more processors 122 making certain
determinations and/or identifying certain types of behavior as
being associated with the purchase of a particular item that will
require a particular type of monetary loan. In addition, aspects
include the use of different likelihood thresholds may be set based
upon a desired tradeoff between accuracy and sensitivity such that,
upon each threshold being exceeded, a particular triggering action
is performed. For example, a threshold likelihood value A (e.g.,
75%) being exceeded may result in a determination that the user
requires a loan. To provide another example, a threshold likelihood
value B (e.g., 80%) threshold being exceeded may result in the user
being preapproved for a loan.
[0066] Furthermore, loan prediction and notification engine 120 may
have access to different types of data, with some types of data
better indicating a user's intention of requiring a loan than
others. In some of the present aspects, these different inputs may
be weighted accordingly, as discussed above. However, in other
aspects, loan prediction and notification engine 120 may calculate
the statistical likelihood of a user requiring a monetary loan (or
other predictions) based upon certain logical conditions being
satisfied, which may be based upon different types to data. That
is, different portions of collected data aggregated as part of a
user's profile may be analyzed such that, when certain types of
information are present, certain logical conditions are considered
to be satisfied. Some types of information may be considered such
good indicators of a user's intention to obtain a loan that, if one
or more of such logical conditions are satisfied, then a
determination may be made that the user requires a loan, which may
trigger one or more actions as discussed above. Further details of
the implementation of both weighted calculations and logical
condition calculations to determine the statistical likelihood of a
user requiring a loan are further discussed below.
[0067] To provide an illustrative example, a statistical likelihood
of a user requiring a loan within the next 60 days may be
calculated that exceeds a threshold likelihood (e.g., 75%). To
provide another illustrative example, one or more logical
conditions may be satisfied that indicate that the user is actively
looking for a loan within the next 30 days. In either event, loan
prediction and notification engine 120 may transmit a notification
to client device 102 indicating that several types of loans are
available and their specifics. In this way, a user may be notified
of potential loan offers from a particular lender even if the user
has not actively solicited a loan from that lender.
[0068] In the present aspects, loan prediction and notification
engine 120 may not only determine that a user is looking for a
loan, but may utilize other sources of data to actively begin or
otherwise simplify the loan application process for the user. For
example, one or more processors 122 may be configured to execute
instructions stored in loan prediction module 131 to prequalify
and/or preapprove a user for a predicted loan that the user will
likely soon require. To do so, one or more processors 122 may be
configured to execute instructions stored in loan prediction module
131 to also predict other details associated with the loan such as
a type of loan, a loan term, and a range of monetary amounts for
which the user is statistically likely to require.
[0069] To provide an illustrative example, a user may perform
online research for three different vehicles that are the same type
of vehicle (e.g., a sedan), but from three different car
manufacturers. Loan prediction and notification engine 120 may then
receive this information from client device 102 in the form of
search terms used and websites visited as part of the user's online
research. Once it is determined that a user requires an auto loan
(from the user's online history and/or additional data) loan
prediction module 131 may facilitate the determination of a range
of loan amounts associated with each vehicle that was most often
researched. Continuing this example, loan prediction module 131 may
facilitate the identification of a range of prices (e.g., via
accessing data sources 170) or a manufacturer's suggested retail
price (MSRP) and/or another range of prices commonly associated
with purchasing the identified vehicles in that user's region. Once
this information is known, the loan preapproval process may use the
highest value from these ranges, for example, to preapprove the
user for the maximum likely amount needed for the auto loan. This
ranging procedure may also be used for any other suitable type of
loan such as new mortgages, lines of credit, home equity loans,
student loans, etc.
[0070] Of course, prequalification and/or preapproval processes may
require other information from the user such as household income,
contact information, household debt, etc. To the extent that
additional information is needed to prequalify and/or preapprove a
user for a particular type of loan, loan prediction and
notification engine 120 may acquire such information in advance.
For example, this information may be requested as part of the
initial opt in and registration process, acquired via communication
via one or more financial institutions 150 and/or data sources 170,
requested form the user, etc. Additional information required for
prequalification and/or preapproval may also include user consent,
signatures, etc., which may be obtained via similar methods.
[0071] To provide another illustrative example, a user may perform
online research to shop for a new home via one or more realtor
websites or other websites often used for such purposes. As part of
this process, the user may email or otherwise communicate with
realtors in the area. Loan prediction and notification engine 120
may receive detailed information from client device 102 in the form
of specific geographic regions searched, the specific type of homes
searched, filters applied on particular websites associated with
searches, key words used when communicating with realtors, etc. The
present aspects include one or more processors 122 executing
instructions stored in loan prediction module 131 to identify a
range of home costs and a future time line for which the user is
likely to purchase the home. Using this information, loan
prediction and notification engine 120 may send the appropriate
notification to the user indicating available loan offers for homes
of a specific type and/or for a specific amount that the user may
be (or already be) qualified for.
[0072] To provide yet another illustrative example, a user may
perform online research regarding colleges or universities to
attend and/or fill out online applications for such institutions.
Loan prediction and notification engine 120 may receive detailed
information from client device 102 in the form of specific
institutions searched and, based upon the current time in the
academic year, when classes start at the institutions. Loan
prediction and notification engine 120 may also receive the
applicant's current age and the average cost for the student to
attend these institutions. Using this aggregated pool of
information, loan prediction and notification engine 120 may
calculate a range of tuition costs and time line for which the user
is likely to require a student loan. Using this information, loan
prediction and notification engine 120 may send the appropriate
notification to the user indicating available student loan offers,
amounts, and their specific of the loan offers.
Exemplary Calculations for Predicting Loan Needs Using Cognitive
Computing
[0073] FIG. 2 illustrates exemplary user profiles 200 in accordance
with one aspect of the present disclosure. As shown in FIG. 2, user
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., storage unit 180, as shown in FIG. 1). User profiles 200 may
be generated, organized, modified, and/or accessed via a
personalized loan engine, such as loan prediction and notification
engine 120, for example, as shown in FIG. 1.
[0074] As shown in FIG. 2, user profiles 200 may include a number
of different types of collected data associated with a number of
individual users A-D. FIG. 2 illustrates four exemplary user
profiles and five different types of data associated with each user
profile. However, the present aspects include user profiles 200
including any suitable number of user profiles for any suitable
number of different users, which may be associated with any
suitable number and/or type of data.
[0075] User profiles 200 illustrate a number of different types of
data associated with each user. The different types of data
aggregated as part of each user's financial profile may be
collectively referred to as "user input data," although the user
input data may include information about the user as well as other
people, such as the user's family members. Again, this user input
data may be used in accordance with the present aspects to perform
calculations and predictions regarding whether a particular user
will soon require a loan and/or the calculation of specific
customized loan specifics.
[0076] As shown in FIG. 2, each user's financial profile contains
different types of information, and portions of each respective
type of information may represent user inputs for cognitive
computing and predictive modeling application 127. Inputs (a1-a6)
may represent each user's demographic data, such as the user's
individual age and/or the ages of each member of the user's family
(e.g., their birthdates), gender, marital status, household size,
whether the user owns a home or rents, the user's ethnicity, the
ethnicity of members of the user's family, etc.
[0077] Inputs (b1-b3) may represent different types of financial
data such as, for example, the user's current individual and/or
family annual income, the user's potential individual and/or
potential family annual income (which may be calculated from other
portions of the user's financial profile, as previously discussed),
the user's credit information such as credit card utilization,
debt-to-income ratios, credit scores, credit reports, etc.
[0078] Inputs (c1-c3) may represent various types of behavioral
data that indicate the user's previous and likely future choices,
behaviors, and/or actions. For example, behavioral data may
represent online data, online search terms, the content of email
such as various identified key words and their frequency of use,
browsing history such as various websites visited by the user,
etc.
[0079] To provide an illustrative example, behavioral data may
indicate that the user recently researched different makes and
models of cars by visiting several car review websites, by entering
online search terms for specific types of vehicles, and/or by
visiting automotive manufacturer websites. To provide additional
illustrative examples, the behavioral data may indicate that a user
has visited different lending websites seeking specific types of
loans. To provide yet another illustrative example, the behavioral
data may indicate that the user has visited several real-estate
based websites looking to purchase a home and the range of home
values searched. To provide even further illustrative examples,
behavioral data may indicate that a user has completed an online
application for a university, has researched options for student
loans, and/or has visited certain college or university
websites.
[0080] Inputs (d1-d6) may represent various types of life event
data that may indicate if and when the user is ready to acquire a
new loan. The life event data may include different types of data
from public records, data submitted and/or collected from the user,
and/or data collected via other third party sources. As shown in
FIG. 2, the life event data may include information such as birth
records (e.g., birth certificates recently recorded), marriage
certificates, an indication that a child has started college, a
determination of whether a child is approaching legal driving age,
whether the user has recently purchased a new home, an indication
that a user's current home is a particular age that may require
repairs (and thus the user may be potentially interested in a home
equity loan), etc.
[0081] Inputs (e1-e2) may represent different types of location
data that may indicate the user's current geographic location
and/or a history of the user's previous locations. In the present
aspects, this location data may be analyzed and/or referenced to
other locations known to be associated with a user actively seeking
or otherwise likely to require a monetary loan. To provide an
illustrative example, the location data may indicate that the user
recently visited physical geographic locations associated with car
dealerships, college campuses, physical banks or lender locations,
high-end retailers, etc.
[0082] Again, in the present aspects, loan prediction and
notification engine 120 may actively collect, aggregate, and/or
monitor data representing user inputs for each user's profile to
calculate a statistical likelihood of the user requiring a loan, as
discussed herein. To perform these tasks, the present aspects
include loan prediction and notification engine 120 analyzing the
user input data in accordance with any suitable predictive modeling
and/or cognitive computing techniques. For example, loan prediction
and notification engine 120 may calculate the statistical
likelihood of the user requiring a loan within some future time
period as an output of a weighting function that weights a
plurality of user inputs extracted from any combination of data
stored in a user's financial profile. The weighting function may,
for example, place weights upon various user inputs that tend to
contribute or correlate more to the determination of whether a user
will likely require a loan.
[0083] This may be analyzed, for example, in light of previous data
stored in the user's financial profile and/or a history of data
stored in other user's financial profiles. To provide an
illustrative example, in the case of predicting whether a user is
likely to require a car loan in the next 30 days, an analysis of
data stored across one or more user profiles may indicate a strong
correlation between a child turning legal driving age and the
child's parents, within some period of time thereafter, purchasing
a vehicle and obtaining a loan for that vehicle.
Exemplary Logic Diagram for Cognitive Computing Predictions
[0084] FIG. 3 illustrates exemplary logic diagrams 300 indicating
the occurrence of several example conditions and their
corresponding impact on various predictions in accordance with an
aspect of the present disclosure. As shown in FIG. 3, each of
examples 1-3 is based upon different portions of user input data
stored as part of user profiles associated with three different
users A-C. The user profiles shown in FIG. 3 may be implementations
of user profiles 200, for example, as shown in FIG. 2.
[0085] As discussed above, various aspects include some user inputs
being weighted in any suitable manner to predict when a user may
require a loan, the specific type of loan, and when it is likely to
be needed. Some user inputs, however, may be of particular
importance such that, when treated individually or in combination
with other user inputs, they satisfy a logical condition that is
associated with various different types of loans. For example, as
shown in Example 1 of FIG. 3, user A's profile may reveal the age
of a user A's child based upon that child's birthdate and the
current date. Using this information, loan prediction and
notification engine 120 may determine a number of days until the
child turns a legal driving age from the current date. A logical
condition may be setup such that, once this number of days is equal
to or less than a minimum future time period (e.g., 90 days, as
shown in Example 1), the statistical likelihood of the user
purchasing a car within this time period (or some time period
thereafter) exceeds a threshold likelihood.
[0086] That is, the logical condition, when satisfied, may set the
statistical likelihood to 100% to ensure that logical condition,
when satisfied, results in the occurrence of specific types of
actions and/or notifications. In this way, once a logical condition
has been satisfied, a number of associated loan offers
corresponding to that satisfied logical condition may be generated.
For example, as shown in FIG. 3 for Example 1, three different loan
offers may be calculated and sent to user A for various interest
rates, amounts, and/or loan terms once the logical conditions have
been met.
[0087] To provide another example, Example 2 of FIG. 3 illustrates
user inputs that indicate a user B has recently married and never
owned a home. In this scenario, a logical condition may be
satisfied to trigger the calculation of various home mortgage loans
for a first time homebuyer, which may include additional incentives
such as lower interest rates and/or require less money down. In
this way, the user B's specific inputs may be considered such that,
when a specific logical condition is satisfied based upon these
inputs, specific types of loans may be calculated and these offers
sent to user B.
[0088] To provide yet another example, Example 3 of FIG. 3
illustrates user inputs indicating that a user C recently had a new
baby, that the family's size with the new child is 4 total people,
and that user C's current home has two total bedrooms. Using this
information, a logical condition may be satisfied since it is
likely that, based upon these factors, that the user C will be
looking to purchase a new home to accommodate this larger family
size. In doing so, loan prediction and notification engine 120 may
calculate specific mortgage loan offers as shown in FIG. 3 and send
these offers to user C.
[0089] For brevity, the Examples shown in FIG. 3 and previously
described illustrate logical conditions satisfied by one to three
user inputs. However, the present aspects include any suitable
number of user inputs or other factors being considered as part of
the determination of whether particular conditions have been
satisfied. Furthermore, aspects also include a trigger condition
causing the calculation of various loans resulting from more than
one logical condition being satisfied. For example, logical
conditions may be satisfied when the user has been married for more
than 30 days, when a newborn baby is 45 days old, etc.
Exemplary Computer-Implemented Method of Predicting when a User is
in Need of a Loan and Notifying the User of Loan Offers
[0090] FIG. 4 illustrates an exemplary computer-implemented method
flow 400 in accordance with an 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 loan prediction and notification engine 120, as
shown in FIG. 1. In one embodiment, method 400 may be performed by
any suitable combination of one or more processors, instructions,
applications, programs, algorithms, routines, etc. For example,
method 400 may be performed via 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 loan prediction and notification engine 120,
such as user input data discussed herein, for example.
[0091] Method 400 may start when one or more processors receive
user input data (block 402). In the present aspects, the user input
data may be received from any suitable number and/or type of data
sources. For example, the user input data may correspond to data
collected via client device 102, one or more financial institutions
150, and/or one or more additional data sources 170, as shown and
discussed with reference to FIG. 1. To provide another example, the
user input data may include one or more of types of information
received and used to generate a user's profile, as shown and
discussed with reference to FIG. 2.
[0092] Method 400 may include one or more processors generating
and/or monitoring a user profile based upon the user input data
(block 404). This may include, for example, a loan prediction and
notification engine (e.g., loan prediction and notification engine
120) organizing, aggregating, and/or storing the various different
types of user input data to a storage unit (block 404). This may
also include, for example, a loan prediction and notification
engine continuously and/or periodically monitoring the contents of
a user's profile for changes (block 404).
[0093] Method 400 may include one or more processors calculating a
statistical likelihood of the user requiring a loan within a future
time period (block 406). This may include, for example calculating
the statistical likelihood based upon any suitable cognitive
computing techniques that may analyze the user input data and draw
conclusions from this analysis (block 406). This may also include,
for example, calculating the output of a weighting function or
other suitable predictive function that utilizes one or more of the
user inputs, as further discussed herein (block 406). Still
further, this may also include, for example, calculating or
otherwise determining whether one or more logical conditions that
are associated with the user potentially requiring a loan have been
satisfied, as further discussed herein (block 406).
[0094] Method 400 may include one or more processors determining
whether the calculated statistical likelihood of the user requiring
a loan (block 406) exceeds a threshold likelihood (block 408). In
some aspects, this may include the determination of whether the
statistical likelihood, which may be calculated as the output of a
suitable predictive function (block 406), exceeds a threshold
likelihood (block 408). In other aspects, this determination may
include determining whether one or more logical conditions have
been satisfied, which alternatively may be viewed as the
statistical likelihood being set to 100% or some other value
exceeding a threshold likelihood (block 408). If so, then method
400 may continue (block 410). If not, then method 400 may revert
back to continuing to monitor the user input data (block 404).
[0095] Method 400 may include one or more processors transmitting a
notification to a user's client device indicating one or more loan
offers the user likely needs (block 410). Again, these
notifications may include, for example, push notifications, email
messages, text messages, etc. (block 410). These loan offers may
include, for example, the details associated with one or more
customized loans that are generated once it has been determined
(block 408) that the user likely requires a loan (block 410). For
example, the loan offers indicated in the transmitted notification
could include those discussed herein with reference to FIG. 3,
whereby various auto loan offers and mortgage loan offers are
determined based upon each user's specific user profile.
Cognitive Computing & Machine Learning
[0096] The cognitive computing and/or predictive modeling
techniques discussed herein may include machine learning techniques
or algorithms. For instance, customer data may be input into
machine learning programs may be trained to (i) determine a
statistical likelihood that a customer is looking for a loan, or
may default on a loan, (ii) customize a loan product, and/or (iii)
predict a life event, based upon the customer data, such as the
types of customer data discussed elsewhere herein.
[0097] In certain embodiments, the cognitive computing and/or
predictive modeling techniques discussed herein may heuristic
engine and algorithms, and/or machine learning, cognitive learning,
deep learning, combined learning, and/or pattern recognition
techniques. For instance, a processor 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.
[0098] Additionally or alternatively, the machine learning programs
may be trained by inputting sample data sets or certain data into
the programs, such as de-personalized customer data, image, mobile
device, insurer database, and/or third-party database data. 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.
[0099] 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 identify potential loan applicants and customize loan
products for individual customers.
[0100] In one embodiment, a processing element (and/or heuristic
engine or algorithm discussed herein) may be trained by providing
it with a large sample of images and/or user data with known
characteristics or features. Based upon these analyses, the
processing element may learn how to identify characteristics and
patterns that may then be applied to analyzing user device details,
user request or login details, user device sensors, geolocation
information, image data, the 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 the user and/or the asset that is to be the subject of a
transaction, such as generating an insurance quote or claim,
opening a financial account, handling a loan or credit application,
processing a financial (such as a credit card) transaction or the
like.
Technical Advantages
[0101] 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 loan prediction and
notification engine, for example. Furthermore, the aspects
described herein may be implemented as part of a computer network
architecture and/or a cognitive computing 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.
[0102] For instance, aspects include analyzing various sources of
data to predict whether a user will likely require a loan, the
specific type of loan needed, and the specific amounts. In doing
so, the aspects overcome issues associated with the inconvenience
of manual and/or unnecessary monitoring of user input data by
replacing manual procedures with a cognitive-based computing
system. Without the improvements suggested herein, additional
processing and memory usage would be required to perform such
monitoring and/or to calculate the likelihood of various users
require a loan.
[0103] Furthermore, the embodiments described herein function to
personalize loans based upon an analysis of data that facilitates
the impact of likely future event. The process improves upon
existing technologies by more accurately forecasting such events
may concurrently analyzing user input data from a larger number of
sources than would be feasible or practical. As a result, the
customization of loan specifics (e.g., the loan type, specific loan
programs, loan terms, etc.) improves the speed, efficiency, and
accuracy in which such calculations could otherwise be performed.
Due to these improvements, the aspects address computer-related
issues regarding efficiency over the traditional amount of
processing power and models used to calculate loan specifics and/or
perform data forecasting. Thus, the aspects also improve upon
computer technology by requiring less calculations due to the
increased efficiency provided, for example, by cognitive computing
versus traditional computing techniques. For instance, because
cognitive computing may be leveraged to determine whether a user
requires a loan, this result reflects an improvement in accuracy
and efficiency versus traditional computers, even assuming that
non-cognitive computers could perform the tasks associated with the
aspects disclosed herein. Therefore, the application of cognitive
computers in particular allows for less calculations and less
resource and power consumption than would otherwise be
possible.
A First Exemplary Computer-Implemented Method for Predicting a
User's Loan Needs and Presenting the User with a Loan Offer
[0104] In one aspect, a computer-implemented method for predicting
when a user requires a loan and presenting the user with a loan
offer may be provided. The method may include one or more
processors (and/or associated transceivers) (1) receiving user
input data associated with a user; (2) generating a user profile
based upon the user input data and being indicative of whether the
user will require a loan within the future time period based upon
the user profile; (3) calculating a statistical likelihood of the
user requiring the loan within a future time period based upon the
user profile; (4) determining whether the statistical likelihood
exceeds a threshold likelihood; and/or (5) when the statistical
likelihood exceeds a threshold likelihood, transmitting a
notification to a computing device associated with the user
including offers for one or more customized loans. The method may
include additional, less, or alternate actions, including those
discussed elsewhere herein.
[0105] For instance, in various aspects, the method may include
calculating a range of loan amounts that have the statistically
highest probability of being required by the user based upon the
user profile, and preapproving the user for the highest amount
within the range of loan amounts.
[0106] Additionally or alternatively, the user input data may
include search terms and/or websites from the user's web browsing
history relevant to the user requiring the loan. In such a case,
the method may include receiving the user's web browsing history as
part of the user input data, identifying, such search terms and
websites from the user's web browsing history relevant to the user
requiring the loan, and storing the relevant search terms and
websites as part of the user's profile. In such aspects, the
statistical likelihood of the user requiring the loan within the
future time period may be based upon these relevant search terms
and websites.
[0107] The user input data may indicate other types of relevant
information used to predict whether a user requires a loan such as,
for example, a number of life events and/or the current age of each
member of the user's family. When the user input data includes data
regarding the age of the user's family members, the method may
additionally or alternatively use the user input data to determine
a number of days until a member of the user's family turns legal
driving age, and then determine that the statistical likelihood
exceeds the threshold likelihood when the number of days is less
than a minimum future time period
[0108] Furthermore, the transmission of the notification may
include a notification being sent in various ways, sending one or
more of (i) a text message, (ii) an email message, or (iii) a push
notification, to a computing device associate with the user
[0109] Additionally or alternatively, the user may be one user from
among several users that are monitored via the method. Thus, the
method may additionally or alternatively include (i) calculating a
statistical likelihood of each of the plurality of users requiring
a loan within a future time period based upon each user's
respective user profile; (ii) determining whether the statistical
likelihood for each of the plurality of users exceeds a respective
threshold likelihood; and (3) when the statistical likelihood for
one of the plurality of users exceeds a respective threshold
likelihood, transmitting a notification to a computing device
associated with that particular user from among the plurality of
users including offers for one or more customized loans.
A Second Exemplary Computer-Implemented Method for Predicting a
User's Loan Needs and Presenting a Loan Offer
[0110] In another aspect, a computer-implemented method for
predicting when a user requires a loan and presenting the user with
one or more loan offers may be provided. The method may include one
or more processors (1) receiving user input data associated with a
user, such as from a user mobile or client device via wireless
communication or data transmission over one or more radio links or
wireless communication channels; (2) generating a user profile
based upon the user input data and being indicative of whether the
user will require a loan within a future time period; (3)
calculating a statistical likelihood of the user requiring the loan
within the future time period based upon the user profile; (4)
determining whether the statistical likelihood exceeds a threshold
likelihood; and/or (5) when the statistical likelihood exceeds a
threshold likelihood, transmitting a notification to the user for
one or more loan offers, such as to the user's mobile or client
device via wireless communication or data transmission over one or
more radio links or wireless communication channels. The method may
include additional, less, or alternate actions, including those
discussed elsewhere herein.
[0111] For instance, in various aspects, the method may include
calculating a range of loan amounts associated with the loan that
have the statistically highest probability of being required by the
user based upon the user profile, and preapproving the user for the
highest amount within the range of loan amounts.
[0112] Additionally or alternatively, the method may include
receiving the user's web browsing history as part of the user input
data, identifying search terms and websites from the user web
browsing history relevant to the user requiring the loan, and
storing the relevant search terms and websites as part of the
user's profile. In this way, the statistical likelihood of the user
requiring the loan within the future time period may be based upon
the relevant search terms and websites.
[0113] Furthermore, the user input data may indicate a current age
of each member of the user's family and/or a number of life events
associated with the user. The method may additionally include
determining a number of days until a member of the user's family
turns legal driving age. Using this information, the method may
determine that the statistical likelihood exceeds the threshold
likelihood when the number of days is less than a minimum future
time period.
[0114] The user may also be associated with a computing device, and
in such a case the method may include transmitting the notification
to the user by sending one or more of (i) a text message, (ii) an
email message, or (iii) a push notification, to the computing
device.
[0115] Additionally or alternatively, the user may be from among a
plurality of users, and the method may include (i) calculating a
statistical likelihood of each of the plurality of users purchasing
requiring a loan within a future time period based upon each user's
respective user profile; (ii) determining whether the statistical
likelihood for each of the plurality of users exceeds a respective
threshold likelihood; and (iii) when the statistical likelihood for
one of the plurality of users exceeds a respective threshold
likelihood, selectively transmitting a notification for one or more
loan offers to that particular user from among the plurality of
users
A Third Exemplary Computer-Implemented Method for Predicting a
User's Loan Needs and Presenting the User with a Loan Offer
[0116] In another aspect, a computer-implemented method for
predicting when a user requires a loan and presenting the user with
one or more loan offers may be provided. The method may include one
or more processors and/or transceivers (1) receiving user input
data associated with a user from a user mobile device via wireless
communication or data transmission over one or more radio links or
wireless communication channels; (2) generating a user profile
based upon the user input data and being indicative of whether the
user will require a loan within a future time period; (3)
calculating a statistical likelihood of the user requiring the loan
within the future time period based upon the user profile; (4)
determining whether the statistical likelihood exceeds a threshold
likelihood; and/or (5) when the statistical likelihood exceeds the
threshold likelihood, transmitting, by one or more processors
and/or transceivers, a notification to the user mobile device for
one or more loan offers via wireless communication or data
transmission over one or more radio links or wireless communication
channels to facilitate providing customized loans. The method may
include additional, less, or alternate actions, including those
discussed elsewhere herein.
A First Exemplary System for Predicting a User's Loan Needs and
Presenting the User with a Loan Offer
[0117] In yet another aspect, a computer system for predicting when
a user requires a loan and presenting the user with a loan offer
may be provided. The system may include (1) a client device (or
mobile device) associated with a user, which may be configured to
periodically transmit user input data associated with the user,
such as via wireless communication or data transmission over one or
more radio links or wireless communication channels; and (2) one or
more back-end components configured to (i) receive the user input
data, such as via wireless communication or data transmission over
one or more radio links or wireless communication channels; (ii)
generate a user profile based upon the received user input data and
being indicative of whether the user will require a loan within a
future time period; (iii) calculate a statistical likelihood of the
user requiring a loan within the future time period based upon the
user profile; (iv) determine whether the statistical likelihood
exceeds a threshold likelihood; and/or (v) when the statistical
likelihood exceeds a threshold likelihood, transmit a notification
to the user client or mobile device for one or more loan offers,
such as via wireless communication or data transmission over one or
more radio links or wireless communication channels. The system may
include additional, less, or alternate components, including those
discussed elsewhere herein.
[0118] For instance, in various aspects, the one or more back-end
components may be further configured to (i) calculate a range of
loan amounts associated with the loan that have the statistically
highest probability of being required by the user based upon the
user profile; and (ii) preapprove the user for the highest amount
within the range of loan amounts.
[0119] Additionally or alternatively, the client device may be
further configured to transmit the user's web browsing history to
the one or more back-end components as part of the user input data,
and the one or more back-end components may be further configured
to (i) identify search terms and websites from the user web
browsing history relevant to the user requiring the loan, and (ii)
store the relevant search terms and websites as part of the user's
profile such that the statistical likelihood of the user requiring
the loan within the future time period is based upon the relevant
search terms and websites.
[0120] Furthermore, the user input data may indicate a current age
of each member of the user's family and/or a number of life events
associated with the user. The client device may be further
configured to (i) determine a number of days until a member of the
user's family turns legal driving age, and (ii) determine that the
statistical likelihood exceeds the threshold likelihood when the
number of days is less than a minimum future time period.
[0121] Additionally or alternatively, the one or more back-end
components may be further configured to transmit the notification
to the user for the one or more loan offers as one or more of (i) a
text message, (ii) an email message, or (iii) a push
notification.
[0122] Still further, aspects include the user being from among a
plurality of users. In such aspects, the one or more back-end
components may be further configured to (i) calculate a statistical
likelihood of each of the plurality of users requiring a loan
within a future time period based upon each user's respective user
profile; (ii) determine whether the statistical likelihood for each
of the plurality of users exceeds a respective threshold
likelihood; and (iii) when the statistical likelihood for one of
the plurality of users exceeds a respective threshold likelihood,
transmit a notification for one or more loan offers to that
particular user from among the plurality of users.
A Second Exemplary System for Predicting a User's Loan Needs and
Presenting the User with a Loan Offer
[0123] In still another aspect, a computer system for predicting
when a user requires a loan and presenting the user with a loan
offer may be provided. The system may include (1) a mobile device
associated with a user and configured to periodically transmit user
input data associated with the user; and (2) one or more back-end
components. The one or more back-end components may be configured
to (i) receive the user input data transmitted by the mobile device
via wireless communication or data transmission over one or more
radio links or wireless communication channels; (ii) generate a
user profile based upon the received user input data and being
indicative of whether the user will require a loan within a future
time period; (iii) calculate a statistical likelihood of the user
requiring a loan within the future time period based upon the user
profile; (iv) determine whether the statistical likelihood exceeds
a threshold likelihood; and (v) when the statistical likelihood
exceeds a threshold likelihood, transmit a notification to the user
mobile device for user review via wireless communication or data
transmission over one or more radio links or wireless communication
channels, the notification detailing one or more loan offers, to
facilitate providing customized loans. The system may include
additional, less, or alternate components, including those
discussed elsewhere herein.
Additional Considerations
[0124] 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 be implement numerous alternate embodiments, using either
current technology or technology developed after the filing date of
this application.
[0125] Further to this point, although the aspects described herein
often utilize credit report information as an example of sensitive
information, the aspects described herein are not limited to such
examples. Instead, the aspects 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 aspects 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 aspects 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.
[0126] 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.
[0127] 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.
[0128] 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 example 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.
[0129] 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.
[0130] 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.
[0131] 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).
[0132] 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.
[0133] 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.
[0134] 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 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.
[0135] 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.
[0136] 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.
[0137] 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.
[0138] 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).
[0139] 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.
[0140] 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).
[0141] The various systems and methods described herein are
directed to an improvement to computer functionality, and improve
the functioning of conventional computers, as described, for
example, in the "Technical Advantages" Section and elsewhere
herein.
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