U.S. patent application number 15/495549 was filed with the patent office on 2021-08-26 for heuristic identity authentication engine.
The applicant listed for this patent is STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY. Invention is credited to Eric Balota, Puneit Dua, Elizabeth Flowers, Shanna L. Phillips.
Application Number | 20210264437 15/495549 |
Document ID | / |
Family ID | 1000002611096 |
Filed Date | 2021-08-26 |
United States Patent
Application |
20210264437 |
Kind Code |
A1 |
Flowers; Elizabeth ; et
al. |
August 26, 2021 |
HEURISTIC IDENTITY AUTHENTICATION ENGINE
Abstract
A heuristic engine includes capabilities to collect an
unstructured data set and authenticate a user's identity. By
providing a heuristic algorithm with unstructured data, such as
question and answer sets related to a user's activities in the
past, a correlation factor can be calculated with a current
context. Authenticating users using heuristic algorithms ensures
precision and predictability of authentication decisions. Such
heuristic algorithms may learn from each interaction and improve
the accuracy of authentication decisions in the future.
Inventors: |
Flowers; Elizabeth;
(Bloomington, IL) ; Dua; Puneit; (Bloomington,
IL) ; Balota; Eric; (Bloomington, IL) ;
Phillips; Shanna L.; (Bloomington, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY |
Bloomington |
IL |
US |
|
|
Family ID: |
1000002611096 |
Appl. No.: |
15/495549 |
Filed: |
April 24, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62368548 |
Jul 29, 2016 |
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62368271 |
Jul 29, 2016 |
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62368298 |
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62368332 |
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62368359 |
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62368406 |
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62368448 |
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62368503 |
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62368512 |
Jul 29, 2016 |
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62368525 |
Jul 29, 2016 |
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62368536 |
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62368572 |
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62368588 |
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62337711 |
May 17, 2016 |
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62335374 |
May 12, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 40/40 20200101;
G06Q 30/016 20130101; H04W 12/08 20130101 |
International
Class: |
G06Q 30/00 20060101
G06Q030/00; H04W 12/08 20060101 H04W012/08; G06F 17/28 20060101
G06F017/28 |
Claims
1. A computer-implemented method, executed with a computer
processor, to authenticate an identity in a transaction,
comprising: retrieving, with the computer processor, an
un-structured transaction data set associated with a customer;
receiving, with the computer processor and via a human-machine
interface, an encoded context indicating a natural language input
received from the customer, wherein the natural language input is
associated with past usage of at least one account; accessing, with
the computer processor, a heuristic algorithm; generating, by
executing the heuristic algorithm with the computer processor, and
using the un-structured transaction data set and the natural
language input as inputs to the heuristic algorithm: a suggested
set of identity authentication questions, and a set of expected
identity authentication answers corresponding to the suggested set
of identity authentication questions; receiving, with the computer
processor, an indication of a customer response to at least one
question of the suggested set of identity authentication questions;
and training, with the computer processor, the heuristic algorithm
using machine learning, wherein the machine learning updates the
heuristic algorithm based at least partly on a correlation factor
between the set of expected identity authentication answers and the
customer response to the at least one question.
2. (canceled)
3. (canceled)
4. The computer-implemented method of claim 1, wherein the
un-structured transaction data set comprises information indicative
of past transactions related to the at least one account.
5. The computer-implemented method of claim 1, wherein the
un-structured transaction data set is retrieved by the computer
processor from an external transaction server.
6. The computer-implemented method of claim 1, wherein the
heuristic algorithm is accessed by the computer processor from an
external heuristic server.
7. (canceled)
8. A computer system configured to authenticate an identity in a
transaction, the computer system comprising at least one of a
processor or a transceiver configured to: retrieve an un-structured
transaction data set associated with a customer; receive an encoded
context via a human-machine interface, the encoded context
indicating a natural language input received from the customer,
wherein the natural language input is associated with past usage of
at least one account; access a heuristic algorithm; generate, by
executing the heuristic algorithm using the un-structured
transaction data set and the natural language input as inputs to
the heuristic algorithm: a suggested set of identity authentication
questions, and a set of expected identity authentication answers
corresponding to the suggested set of identity authentication
questions; receiving an indication of a customer response to at
least one question of the suggested set of identity authentication
questions; and train the heuristic algorithm using machine
learning, wherein the machine learning updates the heuristic
algorithm based at least partly on a correlation factor between the
set of expected identity authentication answers and the customer
response to the at least one question.
9. (canceled)
10. (canceled)
11. The computer system of claim 8, wherein the un-structured
transaction data set comprises information indicative of past
transactions related to the at least one account.
12. The computer system of claim 8, wherein the un-structured
transaction data set is retrieved from an external transaction
server.
13. The computer system of claim 8, wherein the heuristic algorithm
is accessed from an external heuristic server.
14. A non-transitory computer readable medium, comprising computer
readable instructions that, when executed by a computer processor,
cause the computer processor to: retrieve an un-structured
transaction data set associated with a customer; receive an encoded
context via a human-machine interface, the encoded context
indicating a natural language input received from the customer,
wherein the natural language input is associated with past usage of
at least one account; access a heuristic algorithm; generate, by
executing the heuristic algorithm using the un-structured
transaction data set and the natural language input as inputs to
the heuristic algorithm: a suggested set of identity authentication
questions, and a set of expected identity authentication answers
corresponding to the suggested set of identity authentication
questions; receive an indication of a customer response to at least
one question of the suggested set of identity authentication
questions; and train the heuristic algorithm using machine
learning, wherein the machine learning updates the heuristic
algorithm based at least partly on a correlation factor between the
set of expected identity authentication answers and the customer
response to the at least question.
15. (canceled)
16. (canceled)
17. The non-transitory computer readable medium of claim 14,
wherein the un-structured transaction data set comprises
information indicative of past transactions related to the at least
one account.
18. The non-transitory computer readable medium of claim 14,
wherein the un=structured transaction data set is retrieved from an
external transaction server.
19. The non-transitory computer readable medium of claim 14,
wherein the heuristic algorithm is accessed from an external
heuristic server.
20. (canceled)
21. The computer-implemented method of claim 1, further comprising:
generating, by the computer processor and using the heuristic
algorithm, an authentication score based upon the customer
response; and determining, with the computer processor, an identity
authentication result by determining that the authentication score
exceeds a threshold score.
22. The computer-implemented method of claim 21, wherein the
transaction is initiated by the customer, and the threshold score
indicates a level of authentication required to perform the
transaction.
23. The computer system of claim 8, wherein the at least one of the
processor or the transceiver is further configured to: generate,
using the heuristic algorithm, an authentication score based upon
the customer response; and determine an identity authentication
result by determining that the authentication score exceeds a
threshold score.
24. The computer system of claim 23, wherein the transaction is
initiated by the customer, and the threshold score indicates a
level of authentication required to perform the transaction.
25. The non-transitory computer readable medium of claim 14,
wherein the computer readable instructions further cause the
computer processor to: generate, using the heuristic algorithm, an
authentication score based upon the customer response; and
determine an identity authentication result by determining that the
authentication score exceeds a threshold score.
26. The non-transitory computer readable medium of claim 25,
wherein a transaction is initiated by the customer, and the
threshold score indicates a level of authentication required to
perform the transaction.
27. The computer-implemented method of claim 1, further comprising:
presenting the at least one question of the suggested set of
identity authentication questions to the customer via a computer
terminal, wherein the customer response is received via the
computer terminal.
28. The computer-implemented method of claim 1, wherein the
transaction is initiated by the customer, and the retrieving, the
receiving of the encoded context, the accessing, the generating,
the receiving of the indication of the customer response, and the
training are performed in response to initiation of the
transaction.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Patent Application Nos. 62/337,711 and 62/335,374, filed
respectively on May 12, 2016 and May 17, 2016, and U.S. Provisional
Application Nos. 62/368,448, 62/368,406, 62/368,359, 62/368,588,
62/368,572, 62/368,548, 62/368,536, 62/368,525, 62/368,512,
62/368,503, 62/368,332, 62/368,298, 62/368,271, filed on Jul. 29,
2016, the disclosures of which are hereby incorporated herein by
reference.
FIELD OF THE INVENTION
[0002] The disclosure generally relates to systems, methods,
apparatus, and non-transitory computer readable media for using
heuristic algorithms to authenticate the identity of a user in a
customer service environment and, more particularly, to process
natural language inputs and unstructured data sets to ensure
precision and predictability of identity authentication
decisions.
BACKGROUND
[0003] Organizations involved in customer service activities often
process large amounts of unstructured data to make decisions while
interacting with a customer in real-time. For example, in the case
of a customer service representative speaking on the telephone with
a customer experiencing an issue with a product or service,
appropriate solutions may include a combination of timeliness of
response and accuracy in content.
[0004] Such unstructured data may include voluminous transaction
records spanning decades, unstructured customer service data, or
real-time transcripts of customer service interactions with
scattered contextual indicators. To reasonably expect a customer
service representative to effectively leverage such large data sets
in real-time places an unreasonable burden on a customer service
representative. However, failing to do so robs the customer service
representative of vital context not readily apparent, and the
wealth of knowledge gained throughout the history of an
organization that would otherwise need to be distilled to briefing
materials and expensively trained over time. Thus, organizations
may value tools to rapidly process large data sets, to infer
context, suggest lessons learned based upon transaction data, while
learning through successive process iterations. Furthermore,
appropriate application of such tools may provide a competitive
advantage in a crowded and competitive customer service
industry.
[0005] In an effort to automate and provide better predictability
of customer service experiences, many organizations develop
customer relationship management (CRM) software packages.
Organizations that develop these software packages often develop
custom solutions, at great expense, to best meet the needs of their
customers in unique industries. Such tools while providing a great
level of detail for the customer service representative, lack the
flexibility to react to changing business conditions or fully
exploit the underlying technology, driving additional cost into an
already expensive solution.
[0006] Some organizations where able to make concessions on
customized solutions turn to off-the-shelf or commercially
available software solutions that reduce the overall cost of
implementation. Such solutions may provide customer service
representative prompting tools with question and answer formats
that allow for consistency of customer experience, however, at the
expense of a less personalized experience required in many
industries. While more flexible than fully-custom solutions, the
impersonal question-answer format of customer interaction may not
improve without costly software revisions, rarely performed by
original equipment manufacturers (OEMs) of off-the-shelf
solutions.
[0007] The ability for a customer service experience to learn and
improve over successive iterations remains paramount for
organizations to offer discriminating customer service experiences.
Often the burden of continual improvement falls to the customer
service representative, as a human being able to adapt and learn to
changing conditions more rapidly even within the confines of a
rigid customer service software application. However, with the
advent of outsourcing prevalent in the customer service industry,
the customer service representative may lack much of the necessary
context required to provide high levels of relevant customer
service. This lack of context in an interconnected company is less
an issue of distance and more an issue of data access and the
ability to contextually process data to present relevant solutions
in a timely manner.
SUMMARY
[0008] One exemplary embodiment includes a computer-implemented
method, executed with a computer processor, to authenticate a
customer identity. The method may include retrieving an
un-structured transaction data set, receiving an encoded context,
and/or accessing and executing a heuristic algorithm using the data
set and the context. The algorithm may output an authentication
score, a suggested set of authentication questions, and/or a
suggested set of authentication answers based upon the data set and
the context and update the algorithm in the second memory and the
context using a correlation factor between the suggested
authentication answers and a customer response. The method may
include additional, less, or alternate actions or functionality,
including that discussed elsewhere herein.
[0009] Still another embodiment includes a computer-implemented
method, executed with a computer processor, that generates an
indication of money laundering activity. This embodiment includes
retrieving an un-structured transaction set comprising aggregated
transaction data that includes a plurality of users and at least
one indication of prior money laundering activity, receiving a
plurality of financial transactions, and/or accessing and executing
a heuristic algorithm to generate a predicted indication of money
laundering activity using the transaction set and the plurality of
financial transactions. Such an embodiment may subsequently
generate a compliance report using the predicted indications of
money laundering activity. The method may include additional, less,
or alternate actions, including those discussed elsewhere
herein.
[0010] Further exemplary embodiments include a computer-implemented
method, executed with a computer processor, that verifies
documentation in a financial deposit transaction. The method may
include retrieving, with the processor, a plurality of document
image data. At least one of the data may include an indication of
document fraud. The method may include receiving a customer
document image associated with a deposit account transaction,
and/or accessing and executing a heuristic algorithm to generate a
correlation between the image characteristics of the customer
document image and at least one of the document image data. The
method may also include receiving a notification of actual fraud
associated with the customer document image and/or updating the
algorithm using the correlation and the notification. The method
may include additional, less, or alternate actions, including those
discussed elsewhere herein.
[0011] Another embodiment includes a computer-implemented method,
executed with a computer processor, that generates an indication of
fraudulent transaction activity within a financial account. The
method may include retrieving an un-structured transaction set
comprising aggregated transaction data that includes a plurality of
users and at least one indication of fraudulent transaction
activity and receiving a plurality of financial transactions. The
method may also include accessing and executing a heuristic
algorithm to generate a predicted indication of fraudulent account
activity using the transaction set and the plurality of financial
transactions. The method may include additional, less, or alternate
actions, including those discussed elsewhere herein.
[0012] Exemplary embodiments may include computer-implemented
methods that may in other embodiments include apparatus configured
to implement the method, and/or non-transitory computer readable
mediums comprising computer-executable instructions that cause a
processor to perform the method.
[0013] Advantages will become more apparent to those skilled in the
art from the following description of the preferred embodiments
which have been shown and described by way of illustration. As will
be realized, the present embodiments may be capable of other and
different embodiments, 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 THE DRAWINGS
[0014] The Figures described below depict various aspects of the
system and methods disclosed herein. It should be understood that
each figure depicts an aspect of a particular aspect of the
disclosed system and methods, and that each of the Figures is
intended to accord with a possible aspect thereof. Further,
wherever possible, the following description refers to the
reference numerals included in the following Figures, in which
features depicted in multiple Figures are designated with
consistent reference numerals.
[0015] There are shown in the Figures arrangements which are
presently discussed, it being understood, however, that the present
embodiments are not limited to the precise arrangements and
instrumentalities shown, wherein:
[0016] FIG. 1 illustrates an exemplary computer system to
authenticate a customer in accordance with one aspect of the
present disclosure;
[0017] FIG. 2 illustrates an exemplary computer-implemented method
to authenticate a customer in accordance with one aspect of the
present disclosure;
[0018] FIG. 3 illustrates an exemplary computer system to detect
money laundering in accordance with one aspect of the present
disclosure;
[0019] FIG. 4 illustrates an exemplary computer-implemented method
to detect money laundering in accordance with one aspect of the
present disclosure;
[0020] FIG. 5 illustrates an exemplary computer system to validate
documents in accordance with one aspect of the present
disclosure;
[0021] FIG. 6 illustrates an exemplary computer-implemented method
to validate documents in accordance with one aspect of the present
disclosure;
[0022] FIG. 7 illustrates an exemplary computer system to detect
fraud in accordance with one aspect of the present disclosure;
[0023] FIG. 8 illustrates an exemplary computer-implemented method
to detect fraud in accordance with one aspect of the present
disclosure;
[0024] FIG. 9 illustrates an exemplary computing system to in
accordance with one aspect of the present disclosure; and
[0025] FIG. 10 illustrates an exemplary article of manufacture in
accordance with one aspect of the present disclosure.
[0026] The Figures depict preferred embodiments for purposes of
illustration only. Alternative embodiments of the systems and
methods illustrated herein may be employed without departing from
the principles of the invention described herein.
DETAILED DESCRIPTION
[0027] Various embodiments of the present disclosure include the
collection of unstructured data sets together with a current
context. Heuristic algorithms processing these unstructured data
sets together the context may allow calculation of a future
context, and the presentation of context relevant data that
improves over time. By subsequently training the heuristic
algorithm with the outcome of a current and future predicted
context, and the relevance of presented data, the heuristic
algorithm may improve its efficiency as the unstructured data set
grows.
[0028] 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.
Identity Authentication
[0029] FIG. 1 illustrates a block diagram of an exemplary system
100 to authenticate a customer identity in a financial transaction.
The exemplary system 100 enables a user 105 to interface with a
user terminal 110 to transmit a request for a transaction that may
require an identity authentication. Such an request may, in one
embodiment, result from an earlier initiation by a customer service
representative 130, interfacing with a service terminal 135,
interacting through a human-machine interface 115. In another
embodiment, the financial transaction may result from a user
initiated request, absent the representative 130.
[0030] The human-machine interface 115 may encode the customer 105
and representative 130 interactions with the respective user
terminals 110 and service terminal 135 to encode a context in an
electronic format, such that a processor 125 reads the context. The
processor 125 may in one embodiment include a single processor, or
in another embodiment include a variety of interconnected
processors that operate in concert to efficiently perform tasks in
parallel. The processor 125 may, in one embodiment, interface to a
heuristic server 140, and a transaction server 145, storing
respectively, a heuristic algorithm and a set of transaction data.
In other embodiments, the heuristic algorithm and/or transaction
data may reside on the same server.
[0031] The computer processor 125 includes an interface to a
network interface 117, that may in one embodiment include wired and
wireless interfaces to allow communication over any variety of
computer networks communicating over a variety of mediums. One such
embodiment includes an interface to a network 150, for example a
wide-area network such as the Internet, that interconnects any
number of computing devices, for example a remote transaction
and/or heuristic server 155, over wired or wireless networks, or
over one or more radio frequency links or wireless or digital
communication channels. In another embodiment, the network 150 may
comprise a local-area network with access controls to a specific
entity, such as a business.
[0032] In one embodiment, a heuristic engine 120, may comprise the
human-machine interface 115, computer processor 125, and network
interface 117. However, in other embodiments the heuristic engine
120 may include other digital and analog interface devices, memory
devices, and supplemental processing devices for performing
calculations and storing data.
[0033] In accordance with one aspect of the present disclosure, the
system 100 may perform the method 200, as illustrated in FIG. 2.
However, the method 200 does not, or may not, specifically require
the system 100, or the elements included therein in a particular
arrangement, to perform the method steps illustrated in the process
200.
[0034] In one embodiment, a customer, for example the customer 105
of FIG. 1, may initiate a transaction that may require
authentication at step 205. At step 210, a system, for example the
the heuristic engine 120 of FIG. 1, may retrieve a transaction data
set related to the customer. The customer may provide additional
natural language input relating to recent account use at step 215.
The heuristic engine 120 may, at step 220, encode a context based
the natural language input from step 215, or otherwise, together or
in isolation with the transaction data set from step 210. At step
225, a processor, for example the processor 125 of FIG. 1, may
retrieve a heuristic algorithm from the heuristic server 140, or
otherwise. In one embodiment, the processor 125 may execute the
heuristic algorithm with the context and the transaction data set
at step 235.
[0035] At step 240, in one embodiment, the processor 125 may
generate further questions and expected answers, for example using
the heuristic algorithm, using the available data. A customer
service representative, such as the representative 130 of FIG. 1,
may pose further questions to the customer 105, for example using
the terminal 135 and terminal 110. The processor 125 may process a
response from the customer 105 and compare the response against a
threshold score to determine an authentication result. Such a
threshold score may indicate a level of authentication required to
perform the transaction initiated at step 205, or otherwise. At
step 255, the representative 130 may indicate to the customer 105
success or failure of authentication, or in another embodiment,
take subsequent action appropriate given the context of the
customer service interaction.
Money Laundering Detection
[0036] FIG. 3 illustrates a block diagram of an exemplary computer
system 300 to generate an indication of money laundering activity.
The exemplary system 300 enables a user 305 to interface with a
mobile device 310 to perform a transaction and allows at least one
customer service representative 345 and/or 385, to receive an
indication of money laundering activity. Such a request may, in one
embodiment involve an earlier initiation by a customer service
representative 345 and/or 385, interacting with either of the
service terminals 335 and 380. In another embodiment, the
transaction may result from a user initiated request, absent the
representatives 345 and 385.
[0037] The customer may in one embodiment communicate using the
mobile device 310 through any of a variety of wireless
communication protocols 315 using a wireless access point 320 that
implements such protocols and translates communications into a
computer readable format. The wireless access point 320 may connect
to a network 325, for example the Internet or a corporate intranet,
that likewise interfaces to a remote server 330 and a service
terminal 335 accessible to a customer service representative 345,
such as via wireless communication or data transmission over one or
more radio frequency links, or wireless or digital communication
channels.
[0038] In one exemplary embodiment, a heuristic engine 355 may
include a network interface 350 that communicatively couples to the
network 325, and a computer processor 360. In other embodiments,
the heuristic engine 355 may include a variety of memory devices,
interface devices, and processing devices, to execute required
functions. The network interface 350 may include interfaces to a
database server 365 and a service terminal 380 used by a customer
service representative 385. The computer processor 360 may
communicatively couple to a heuristic server 370 and transaction
server 375.
[0039] In accordance with one aspect of the present disclosure, the
system 300 may perform the method 400, as illustrated in FIG. 4.
However, in one embodiment, the method 400 may not, or does not,
specifically require the system 300, nor do the elements included
therein require a particular arrangement, to perform the method
steps illustrated in the process 400.
[0040] Method 400 may include a regulatory authority demanding an
audit report related to money laundering activity on a particular
account (block 405). In one embodiment, a user, such as the user
305 from FIG. 3, initiates a request for an audit report using a
mobile device, such as mobile device 310. In one embodiment, the
processor 360 may retrieve an unstructured transaction set from the
remote server 330, the database server 365, or the transaction
server 375. The processor 360 may retrieve a heuristic algorithm
from the heuristic server 370 (block 420). In one embodiment, the
processor 360 may generate an indication of money laundering
activity related to one or more transactions, for example
transactions stored in the transaction server 375.
[0041] The processor 360 may execute the algorithm with the
retrieved transaction set and the indication of money laundering
activity (block 430). In one exemplary embodiment, the processor
360 may generate an audit report for a regulatory authority. The
processor 360 may update the algorithm stored in the heuristic
server 370 (block 440), for example using the indication and
additional transactions identified as comprising money laundering
activity.
Document Verification
[0042] FIG. 5 illustrates a block diagram of an exemplary computer
system 500 to perform document verification. The exemplary system
500 enables a user 505, using for example a document scanner 510 to
transmit document images over a computer network 515 to a network
interface 525. The network 515 may include interfaces to a remote
server 520, storing document image data. A customer service
representative 555 may interface with a service terminal 550
through a human-machine interface 540 communicatively coupled to a
processor 530. The network interface 525, computer processor 530,
and human machine interface 540 together may comprise a heuristic
engine 535. In other embodiments, the heuristic engine 535 may
include a variety of memory devices, interface devices, and
processing devices, to execute required functions. The processor
530 may interface to a heuristic server 550 and a transaction
server 545.
[0043] In accordance with one aspect of the present disclosure, the
system 500 may perform the computer-implemented method 600, as
illustrated in FIG. 6. However, in one embodiment, the method 600
does not, or may not, specifically require the system 500, nor do
the elements included therein require a particular arrangement, to
perform the method steps illustrated in the process 600.
[0044] Method 600 may include, in one embodiment, a customer such
as the user 505 of FIG. 5, initiates a transaction requiring
authentication of a document (block 605). The processor 530, for
example, may retrieve document image data from, the remote server
520 or the transaction server 545 in some embodiments (block 610).
The user 505 may in some embodiments provide a document image
(block 615), and the processor 530 may retrieve (block 620) a
heuristic algorithm, for example from the heuristic server 550. The
processor 530 may execute the algorithm with the image data and a
correlation of the image to an indication of fraud (block 625). In
one embodiment, the processor 530 (block 630) may calculate a
probability of fraud, and prompt the user 505.
[0045] A service representative 555 may determine if fraud exists
(block 635), and the processor may calculate a correlation between
the probability of fraud and an actual fraud detection (block 640).
The processor 530 may update the algorithm stored in the heuristic
server 550 with the calculated correlation (block 645).
Fraud Detection
[0046] FIG. 7 illustrates a block diagram of an exemplary computer
system 700 to perform fraud detection based upon past instances of
fraud. The exemplary system 700 enables a user 705, using for
example a cellular telephone 710 to initiate a transaction that may
or may not be fraudulent. The cellular telephone 710 may wirelessly
communicate to a wireless access point 720, via a wireless protocol
715 to a human machine interface 725. A customer service
representative 760 may use a customer service terminal 765 to
communicate with the user and receive data. The service terminal
765 may, in one embodiment, communicatively couple to a network
interface 740. The network interface 740 may interface to a remote
server 755, for example storing fraud instance data in one
embodiment. The network interface 740 and human-machine interface
725 may communicatively couple to a processor 730, that together
comprise a heuristic engine 735 in one embodiment. In other
embodiments, the heuristic engine 735 may include a variety of
memory devices, interface devices, and processing devices, to
execute required functions. The computer processor 730 may
interface to a heuristic server 750 and a transaction server
745.
[0047] In accordance with one aspect of the present disclosure, the
system 700 may perform the computer-implemented method 800, as
illustrated in FIG. 8. However, in one embodiment, the method 800
does not, or may not, specifically require the system 700, nor do
the elements included therein require a particular arrangement, to
perform the method steps illustrated in the process 800.
[0048] The method 800 includes a user, for example the user 705 of
FIG. 7, that initiates a financial transaction that may or may not
be fraudulent (block 805). The processor 730 may retrieve a
transaction set correlating aggregated transactions from a
plurality of users for example, with indications of fraud (block
810). In one embodiment, the processor 730 (block 820) may retrieve
a heuristic algorithm. A user or customer may provide context data
for current and past transactions (block 815), for example with the
cellular telephone 710.
[0049] In one embodiment, the processor 730 may execute the
algorithm with the aggregated transactions and a current context.
The processor 730 may generate a probability of fraudulent activity
in a current context (block 830). In another embodiment, the
service representative 750 may prompt the customer 705 for
additional context, for example related to the transaction being
processed. The processor 730 may update the heuristic algorithm
stored in the heuristic server 750 with the probability (block
840), the current context, and any additional context
available.
[0050] FIG. 9 illustrates an exemplary computing system 900 in
accordance with the embodiments disclosed in FIGS. 1-8 and 10. The
exemplary computing system 900 and components disclosed therein may
comprise part, all, or none of the disclosed embodiments of FIGS.
1-8 and 10. The system 900 includes one or more microprocessors
905, coupled to supporting devices through multi-access busses 925
and 940. Dynamic random access memory 930 and 935 may interface to
data bus 925, and store data used by the one or more
microprocessors 905. The system 900 includes instruction registers
920 that store executable instructions for the one or more
microprocessors 905, and data registers 915 that store data for
execution. In some embodiments, the system 900 includes one or more
arithmetic co-processors 910, to assist or supplement the one or
more microprocessors 905.
[0051] Data bus 940 includes interfaces to a graphics interface 945
that may in some embodiments process and transmit graphical data
for a user on a display or similar devices. Likewise, data bus 940
includes interfaces for a digital I/O interface that processes and
transmits, for example, keyboard, pointing device, and other
digital and analog signals produced and consumed by users or other
machines. A network interface 955 processes and transmits encoded
information over wired and wireless networks to connect the system
900 to other machines and users. Data bus 940 also includes at
least one interface to a non-volatile memory interface, that may
process and transmit data that resides on non-volatile memory
devices.
[0052] FIG. 10 illustrates a non-transitory computer readable
medium 1005, that comprises processor executable instructions 1010.
Such processor executable instructions may include instructions
executed by the one or more processors 905 of FIG. 9.
Machine Learning and Other Matters
[0053] In certain embodiments, the heuristic engine and algorithms
discussed herein may include 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.
[0054] Additionally or alternatively, the machine learning programs
may be trained by inputting sample data sets or certain data into
the programs, such as 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.
[0055] In supervised machine learning, a processing element may be
provided with example inputs and their associated outputs, and may
seek to discover a general rule that maps inputs to outputs, so
that when subsequent novel inputs are provided the processing
element may, based upon the discovered rule, accurately predict the
correct output. In unsupervised machine learning, the processing
element may be required to find its own structure in unlabeled
example inputs. In one embodiment, machine learning techniques may
be used to extract the relevant data for one or more tokenized
icons from 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.
[0056] 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.
Additional Considerations
[0057] All of the foregoing computer systems may include
additional, less, or alternate functionality, including that
discussed herein. All of the computer-implemented methods may
include additional, less, or alternate actions, including those
discussed herein, and may be implemented via one or more local or
remote processors and/or transceivers, and/or via
computer-executable instructions stored on computer-readable media
or medium.
[0058] The processors, transceivers, mobile devices, service
terminals, servers, remote servers, database servers, heuristic
servers, transaction servers, and/or other computing devices
discussed herein may communicate with each via wireless
communication networks or electronic communication networks. For
instance, the communication between computing devices may be
wireless communication or data transmission over one or more radio
links, or wireless or digital communication channels.
[0059] Customers may opt into a program that allows them share
mobile device and/or customer, with their permission or affirmative
consent, with a service provider remote server. In return, the
service provider remote server may provide the functionality
discussed herein, including security, fraud, or other monitoring,
and generate recommendations to the customer and/or generate alerts
for the customers in response to abnormal activity being
detected.
[0060] 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.
[0061] 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.
[0062] 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.
[0063] 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.
[0064] 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).
[0065] 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.
[0066] 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.
[0067] 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.
[0068] 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.
[0069] 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.
[0070] 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.
[0071] 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).
[0072] 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.
[0073] 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).
[0074] The systems and methods described herein are directed to
improvements to computer functionality, and improve the functioning
of conventional computers.
[0075] 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.
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