U.S. patent application number 15/868640 was filed with the patent office on 2018-12-06 for systems and methods for fraud detection by transaction ticket size pattern.
The applicant listed for this patent is MASTERCARD INTERNATIONAL INCORPORATED. Invention is credited to Po Hu, Ramamohan R. Sangasani, Qian Wang.
Application Number | 20180349906 15/868640 |
Document ID | / |
Family ID | 56111552 |
Filed Date | 2018-12-06 |
United States Patent
Application |
20180349906 |
Kind Code |
A9 |
Wang; Qian ; et al. |
December 6, 2018 |
SYSTEMS AND METHODS FOR FRAUD DETECTION BY TRANSACTION TICKET SIZE
PATTERN
Abstract
A method and system for detecting fraud in a payment card
network using a pattern of transaction ticket size are provided.
The method including receiving transaction information, for a
current financial transaction, from at least one of a merchant
point of sale (POS) device and a merchant website, the transaction
information including a current transaction amount, the transaction
information associated with a single payment card cardholder,
retrieving a predetermined number of historical transactions for
the single cardholder based on the transaction information, and
generating a historical spend ticket size pattern based on average
ticket size and dispersions for at least one of the same store,
similar stores, and relevant merchant categories. The method
further including comparing the current transaction amount to the
historical spend ticket size pattern and generating a
recommendation for approval or decline of the current financial
transaction based on the comparison.
Inventors: |
Wang; Qian; (Ridgefield,
CT) ; Hu; Po; (Norwalk, CT) ; Sangasani;
Ramamohan R.; (White Plains, NY) |
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Applicant: |
Name |
City |
State |
Country |
Type |
MASTERCARD INTERNATIONAL INCORPORATED |
Purchase |
NY |
US |
|
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Prior
Publication: |
|
Document Identifier |
Publication Date |
|
US 20180137513 A1 |
May 17, 2018 |
|
|
Family ID: |
56111552 |
Appl. No.: |
15/868640 |
Filed: |
January 11, 2018 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15231299 |
Aug 8, 2016 |
9875475 |
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15868640 |
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14567124 |
Dec 11, 2014 |
9412108 |
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15231299 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 20/4016 20130101;
G06Q 20/34 20130101; G06Q 40/00 20130101; G06Q 20/405 20130101 |
International
Class: |
G06Q 20/40 20060101
G06Q020/40; G06Q 20/34 20060101 G06Q020/34; G06Q 40/00 20060101
G06Q040/00 |
Claims
1-22. (canceled)
23. A computer-implemented method for fraud detection based on a
pattern of transaction ticket size over a payment card network, the
method implemented using a computer device coupled to a memory
device, the method comprising: electronically receiving transaction
information for a current financial transaction initiated by a
cardholder with a merchant, the transaction information including a
current transaction amount; comparing the current transaction
amount to a historical spend ticket size pattern, wherein the
historical spend ticket size pattern includes an average ticket
size and a standard deviation for the average ticket size; and
generating a recommendation for approval or decline of the current
financial transaction based on the comparison.
24. The computer-implemented method of claim 23, wherein the
transaction information includes a payment card identifier, an
identification associated with the merchant or merchant website, a
transaction amount, a transaction category, a time and date of when
the transaction occurred, and a transaction type.
25. The computer-implemented method of claim 23 further comprising
retrieving a predetermined number of historical transactions for
the cardholder based on the transaction information by retrieving a
predetermined number of approved historical transactions and a
predetermined number of declined historical transactions associated
with a payment card identifier of the cardholder.
26. The computer-implemented method of claim 23 further comprising
retrieving a predetermined number of historical transactions for
the cardholder based on a cardholder account identifier included in
the transaction information.
27. The computer-implemented method of claim 23, wherein comparing
the current transaction amount to a historical spend ticket size
pattern further comprises: retrieving a predetermined number of
historical transactions for the cardholder based on the transaction
information; generating the historical spend ticket size pattern
based on the retrieved historical transactions for the cardholder;
and determining if the current transaction amount is within the
standard deviation for the average ticket size for at least one of
a same store, similar stores, and relevant merchant categories.
28. The computer-implemented method of claim 23, wherein comparing
the current transaction amount to a historical spend ticket size
pattern further comprises comparing the current transaction amount
to the historical spend ticket size pattern, wherein the historical
spend ticket size pattern further includes (i) an aggregate ticket
size in combination with a number of visits over a predefined
period of time for the cardholder to at least one of a same store,
similar stores, and relevant merchant categories, and (ii) a
standard deviation for the aggregated ticket size.
29. The computer-implemented method of claim 23 further comprising:
generating one or more similarity measurements, wherein the one or
more similarity measurements include analytical parameters that
relate to at least one of a frequency of purchase, an average
ticket size for a selected time period, and seasonal adjustments to
the analytical parameters; and transmitting the one or more
similarity measurements to a model stored in the memory device.
30. The computer-implemented method of claim 23, wherein receiving
transaction information electronically further comprises receiving
transaction information electronically, for a current financial
transaction, from at least one of a merchant point of sale (POS)
device and a merchant website.
31. A fraud detection computing device for detecting potential
fraudulent transactions in a payment card system using a
transaction ticket size pattern, the fraud detection computing
device comprising a memory device for storing data, and a processor
in communication with the memory device, said processor programmed
to: electronically receive transaction information for a current
financial transaction initiated by a cardholder with a merchant,
the transaction information including a current transaction amount;
compare the current transaction amount to a historical spend ticket
size pattern, wherein the historical spend ticket size pattern
includes an average ticket size and a standard deviation for the
average ticket size; and generate a recommendation for approval or
decline of the current financial transaction based on the
comparison.
32. The fraud detection computing device of claim 31, wherein the
transaction information includes a payment card identifier, an
identification associated with the merchant or merchant website, a
transaction amount, a transaction category, a time and date of when
the transaction occurred, and a transaction type.
33. The fraud detection computing device of claim 31, wherein said
processor is further programmed to retrieve a predetermined number
of historical transactions for the cardholder based on the
transaction information, the predetermined number of historical
transactions including (i) a predetermined number of approved
historical transactions and (ii) a predetermined number of declined
historical transactions associated with a payment card identifier
of the cardholder.
34. The fraud detection computing device of claim 31, wherein said
processor is further programmed to retrieve a predetermined number
of historical transactions for the cardholder based on a cardholder
account identifier included in the transaction information.
35. The fraud detection computing device of claim 31, wherein said
processor is further programmed to compare the current transaction
amount to a historical spend ticket size pattern by: retrieving a
predetermined number of historical transactions for the cardholder
based on the transaction information; generating the historical
spend ticket size pattern based on the retrieved historical
transactions for the cardholder; and determining if the current
transaction amount is within the standard deviation for the average
ticket size for at least one of a same store, similar stores, and
relevant merchant categories.
36. The fraud detection computing device of claim 31, wherein said
processor is further programmed to compare the current transaction
amount to the historical spend ticket size pattern by comparing the
current transaction amount to the historical spend ticket size
pattern, the historical spend ticket size pattern further including
(i) an aggregate ticket size in combination with a number of visits
over a predefined period of time for the cardholder to at least one
of a same store, similar stores, and relevant merchant categories,
and (ii) a standard deviation for the aggregated ticket size.
37. The fraud detection computing device of claim 36, wherein said
processor is further programmed to: generate one or more similarity
measurements, wherein the one or more similarity measurements
include analytical parameters that relate to at least one of a
frequency of purchase, an average ticket size for a selected time
period, and seasonal adjustments to the analytical parameters; and
transmit the one or more similarity measurements to a model stored
in the memory device.
38. A non-transitory computer-readable storage media having
computer-executable instructions embodied thereon, wherein when
executed by at least one processor in communication with a memory
device, the computer-executable instructions cause the processor
to: electronically receive transaction information for a current
financial transaction initiated by a cardholder with a merchant,
the transaction information including a current transaction amount;
compare the current transaction amount to a historical spend ticket
size pattern, wherein the historical spend ticket size pattern
includes an average ticket size and a standard deviation for the
average ticket size; and generate a recommendation for the current
financial transaction based on the comparison.
39. The computer-readable storage media of claim 38, wherein the
computer-executable instructions further cause the processor to
compare the current transaction amount to the historical spend
ticket size pattern by: retrieving a predetermined number of
historical transactions for the cardholder based on the transaction
information; generating the historical spend ticket size pattern
based on the retrieved historical transactions for the cardholder;
and determining if the current transaction amount is within the
standard deviation for the average ticket size for at least one of
a same store, similar stores, and relevant merchant categories.
40. The computer-readable storage media of claim 38, wherein the
computer-executable instructions further cause the processor to
retrieve a predetermined number of historical transactions for the
cardholder based on a cardholder account identifier included in the
transaction information.
41. The computer-readable storage media of claim 38, wherein the
computer-executable instructions further cause the processor to
retrieve a predetermined number of historical transactions using
the transaction information, the predetermined number of historical
transactions including (i) a predetermined number of approved
historical transactions and (ii) a predetermined number of declined
historical transactions associated with a payment card identifier
of the cardholder.
42. The computer-readable storage media of claim 38, wherein the
computer-executable instructions further cause the processor to
compare the current transaction amount to a historical spend ticket
size pattern by comparing the current transaction amount to the
historical spend ticket size pattern, wherein the historical spend
ticket size pattern further includes (i) an aggregate ticket size
in combination with a number of visits over a predefined period of
time for the cardholder to at least one of a same store, similar
stores, and relevant merchant categories, and (ii) a standard
deviation for the aggregated ticket size.
Description
BACKGROUND
[0001] This disclosure relates generally to detecting fraudulent
transactions in a payment card system and, more particularly, to
computer systems and computer-based methods for comparing current
financial transaction to spending patterns established by the
cardholder.
[0002] Consumers that use credit and debit cards for purchases,
both at brick and mortar stores and online, tend to make at least
some of their purchases on a routine basis, for example, a
cardholder may make the same type of purchases for approximately
the same amount at the same stores or online outlets at relatively
consistent time intervals. Fraudulent users of the cardholders'
payment card tend to make purchases that do not follow the routine
established by the cardholder. For example, a fraudulent user may
use the cardholder's payment card at different types of stores than
the cardholder routinely shops at. Further, the fraudulent
cardholder may make larger purchases than the cardholder normally
spends.
[0003] While the aforementioned payment instruments or cards
generally provide account holders a measure of convenience to
conduct various transactions, they are susceptible to fraudulent
and/or other types of unauthorized use. For example, an
unauthorized user may attempt to make purchases or conduct other
transactions with a stolen or otherwise ill-gotten payment
instrument or card. To protect against these fraudulent and/or
unauthorized uses, various approaches have been previously
implemented in an effort to ensure that only the account holder
named or otherwise identified on the card is able to use the card.
For example, the card may carry the account holder's signature.
Accordingly, a signature provided by the user of the card at the
time of the transaction can be compared to the signature on the
card to verify that the user is in fact the account holder. In
another example, the user of the card may be required to supply a
PIN (Personal Identification Number) or other secret code before a
transaction can be initiated with the card. In yet another example,
the user of the card may be required to present some secondary form
of ID indicating that they are in fact the account holder named or
otherwise identified on the card.
[0004] Some degree of security against fraudulent or otherwise
unauthorized card use is provided by the foregoing solutions.
However, these solutions are limited in various respects. For
example, signatures can be forged, PINs can guessed or otherwise
become compromised, and false secondary IDs can be created or
obtained by unscrupulous individuals.
BRIEF DESCRIPTION
[0005] In one embodiment, a computer-implemented method for fraud
detection based on a pattern of transaction ticket size on a
payment card network is implemented using a computer device coupled
to a memory device. The method includes receiving transaction
information, for a current financial transaction, from at least one
of a merchant point of sale (POS) device and a merchant website
wherein the transaction information includes a current transaction
amount and the transaction information is associated with a single
payment card cardholder. The method further includes retrieving a
predetermined number of historical transactions for the single
cardholder based on the transaction information and generating a
historical spend ticket size pattern based on average ticket size
and dispersions for at least one of the same store, similar stores,
and in relevant merchant categories. The method further includes
comparing the current transaction amount to the historical spend
ticket size pattern and generating a recommendation for approval or
decline of the current financial transaction based on the
comparison.
[0006] In another embodiment, a fraud detection computing device
for detecting potential fraudulent transactions in a payment card
system using transaction ticket size pattern includes a memory for
storing data, and a processor in communication with the memory. The
processor is programmed to receive transaction information, for a
current financial transaction, from at least one of a merchant
point of sale (POS) device and a merchant website, the transaction
information including a current transaction amount, the transaction
information associated with a single payment card cardholder. The
processor is also programmed to retrieve a predetermined number of
historical transactions for the single cardholder based on the
transaction information and generate a historical spend ticket size
pattern based on average ticket size and dispersions for at least
one of the same store, similar stores, and relevant merchant
categories. The processor is further programmed to compare the
current transaction amount to the historical spend ticket size
pattern and generate a recommendation for approval or decline of
the current financial transaction based on the comparison.
[0007] In yet another embodiment, one or more non-transitory
computer-readable storage media has computer-executable
instructions embodied thereon, wherein when executed by at least
one processor, the computer-executable instructions cause the
processor to receive transaction information, for a current
financial transaction, from at least one of a merchant point of
sale (POS) device and a merchant website, the transaction
information including a current transaction amount, the transaction
information associated with a single payment card cardholder. The
instructions further causing the processor to retrieve a
predetermined number of historical transactions for the single
cardholder based on the transaction information and generate a
historical spend ticket size pattern based on average ticket size
and dispersions for at least one of the same store, similar stores,
and relevant merchant categories. The instructions further causing
the processor to compare the current transaction amount to the
historical spend ticket size pattern and generate a recommendation
for approval or decline of the current financial transaction based
on the comparison.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIGS. 1-9 show example embodiments of the methods and
systems described herein.
[0009] FIG. 1 is a schematic diagram illustrating an example
multi-party payment card industry system having a transaction
ticket size pattern module and that enables payment-by-card
transactions between merchants and cardholders.
[0010] FIG. 2 is a simplified block diagram of an example payment
processing system including a plurality of computer devices
including the transaction ticket size pattern module shown in FIG.
1 in accordance with one example embodiment of the present
disclosure.
[0011] FIG. 3 is an expanded block diagram of an example embodiment
of a server architecture of the payment processing system shown in
FIG. 2 in accordance with one example embodiment of the present
disclosure.
[0012] FIG. 4 illustrates an example configuration of a user system
operated by a user, such as the cardholder shown in FIG. 1.
[0013] FIG. 5 illustrates an example configuration of a server
system such as the server system shown in FIGS. 2 and 3.
[0014] FIG. 6 is a data flow diagram of a purchase transaction
implemented using the transaction ticket size pattern module of the
payment processing system shown in FIG. 2.
[0015] FIG. 7 is a data flow diagram of an example embodiment of
the transaction ticket size pattern module of the payment
processing system shown in FIG. 2.
[0016] FIG. 8 is a component view of an example transaction ticket
size pattern module of payment processing system shown in FIG.
2.
[0017] FIG. 9 is a flow chart of a method of fraud detection based
on a pattern of transaction ticket size on a payment card
network.
DETAILED DESCRIPTION
[0018] Embodiments of the methods and systems described herein
relate to an application executing on or in cooperation with a
payment card network that receives transaction information relating
to an item or service the merchant has for sale. Payment card
cardholders generally develop patterns of use of their payment card
over time. One of these patterns relates to a size of the spend in
each transaction, another pattern is a frequency of the occurrence
of the transactions. Each of these patterns are considered in
relation to an industry category of the merchant or even the
particular merchant. For example, a payment card cardholder may
establish a pattern of purchasing gasoline for their automobile.
The type of automobile and a typical amount of driving tend to
establish an amount of gasoline that is needed to be purchased on a
periodic basis. Additionally, variations in the typical driving
patterns may indicate that a range of gasoline purchases on a
periodic basis better defines the cardholder's typical pattern for
purchasing gasoline. Given the cardholder' driving pattern and
relatively fixed other parameters that affect the amount of
gasoline purchased, a transaction amount and a frequency of the
transactions can be established.
[0019] One of the events that may break these patterns is
fraudulent use of the card. A fraudulent user is not expected to
have the same spend patterns as the cardholder. A fraudulent user
is expected to make purchases having relatively large amounts and
at a greater frequency than the cardholder. By comparing the
transaction amount of each purchase transaction of a single
cardholder to a historical ticket size pattern and dispersions
(also referred to as a standard deviation) for the cardholder, an
indication of fraudulent use of the payment card may be detected.
As used herein, ticket size refers to an amount of a single
transaction. In some embodiments ticket size refers to an amount of
a plurality of transactions related to a single category of goods,
subdivision of a business entity, industry of a merchant, or the
like. As used herein, ticket size pattern refers to a behavior of a
cardholder represented by characteristics of purchases made by the
cardholder over a predetermined period of time, deviations from
which may indicate fraudulent use of the payment card.
[0020] An authorization request recommendation based on a
historical ticket size pattern of a cardholder's payment
transaction amounts may include a number of steps. A transaction is
attempted at a merchant via POS or online. The transaction
information is transmitted electronically to an interchange network
where a card number acquired during the transaction is matched to
corresponding records in a database in the interchange network. The
transaction information also includes a merchant identifier, a
transaction amount, transaction category, and a transaction type
(POS or online). Using the card number, all, or a sufficiently
large number of transactions (approved or declined) associated with
the payment card number are retrieved. A historical ticket size
pattern is created based on average ticket size or amount and
dispersions at the same store, similar stores, and relevant
merchant categories. The current transaction amount is compared
with the historical spend ticket size pattern, and similarity
measurements are created and input into a modeling process. The
similarity measurements include analytical parameters that related
to a regularity and frequency of purchase, an aggregate amount or
ticket size per selectable time periods, and seasonal adjustments
to the analytical parameters. For example, a cardholder's
consumption of a particular good or service may be consistent over
a long time period. The similarity measurements would reflect that
the cardholder purchases the good of service at a consistent
interval and at relatively fixed amounts, meaning each transaction
for the good service occurs regularly for a consistent amount. The
transaction for the consistent amount could occur at any interval.
Once a week, once a month, once a quarter, etc. The purchase does
not need to occur at the same part of the time period for the
similarity measurement to note the purchase transaction is part of
a regular pattern. For each transaction in a specific category, the
system will determine whether the current transaction ticket size
is dramatically different from a normal ticket size. With other
measurement and models, such as information from travel tickets,
the system can recommend approval or disapproval a transaction from
a ticket size perspective.
[0021] As used herein, the terms "transaction card," "financial
transaction card," and "payment card" refer to any suitable
transaction card, such as a credit card, a debit card, a prepaid
card, a charge card, a membership card, a promotional card, a
frequent flyer card, an identification card, a prepaid card, a gift
card, and/or any other device that may hold payment account
information, such as mobile phones, smartphones, personal digital
assistants (PDAs), key fobs, and/or computers. Each type of
transactions card can be used as a method of payment for performing
a transaction.
[0022] In one embodiment, a computer program is provided, and the
program is embodied on a computer readable medium. In an example
embodiment, the system is executed on a single computer system,
without requiring a connection to a sever computer. In a further
example embodiment, the system is being run in a Windows.RTM.
environment (Windows is a registered trademark of Microsoft
Corporation, Redmond, Wash.). In yet another embodiment, the system
is run on a mainframe environment and a UNIX.RTM. server
environment (UNIX is a registered trademark of AT&T located in
New York, N.Y.). The application is flexible and designed to run in
various different environments without compromising any major
functionality. In some embodiments, the system includes multiple
components distributed among a plurality of computing devices. One
or more components may be in the form of computer-executable
instructions embodied in a computer-readable medium. The systems
and processes are not limited to the specific embodiments described
herein. In addition, components of each system and each process can
be practiced independent and separate from other components and
processes described herein. Each component and process can also be
used in combination with other assembly packages and processes.
[0023] As used herein, the term "database" may refer to either a
body of data, a relational database management system (RDBMS), or
to both. A database may include any collection of data including
hierarchical databases, relational databases, flat file databases,
object-relational databases, object oriented databases, and any
other structured collection of records or data that is stored in a
computer system. The above examples are for example only, and thus
are not intended to limit in any way the definition and/or meaning
of the term database. Examples of RDBMS's include, but are not
limited to including, Oracle.RTM. Database, MySQL, IBM.RTM. DB2,
Microsoft.RTM. SQL Server, Sybase.RTM., and PostgreSQL. However,
any database may be used that enables the systems and methods
described herein. (Oracle is a registered trademark of Oracle
Corporation, Redwood Shores, Calif.; IBM is a registered trademark
of International Business Machines Corporation, Armonk, N.Y.;
Microsoft is a registered trademark of Microsoft Corporation,
Redmond, Wash.; and Sybase is a registered trademark of Sybase,
Dublin, Calif.)
[0024] The following detailed description illustrates embodiments
of the disclosure by way of example and not by way of limitation.
It is contemplated that the disclosure has general application to
processing financial transaction data by a third party in
industrial, commercial, and residential applications.
[0025] As used herein, an element or step recited in the singular
and proceeded with the word "a" or "an" should be understood as not
excluding plural elements or steps, unless such exclusion is
explicitly recited. Furthermore, references to "example embodiment"
or "one embodiment" of the present disclosure are not intended to
be interpreted as excluding the existence of additional embodiments
that also incorporate the recited features.
[0026] FIG. 1 is a schematic diagram illustrating an example
multi-party payment card industry system having a transaction
ticket size pattern module and that enables payment-by-card
transactions between merchants and cardholders. Embodiments
described herein may relate to a financial transaction card system,
such as a payment card network operated by MasterCard International
Incorporated. The payment card network, as described herein, is a
four-party payment card network that includes a plurality of
special purpose processors and data structures stored in one or
more memory devices communicatively coupled to the processors, and
a set of proprietary communications standards promulgated by
MasterCard International Incorporated for the exchange of financial
transaction data and the settlement of funds between financial
institutions that are members of the payment card network. As used
herein, financial transaction data includes a unique account number
associated with a cardholder using a payment card issued by an
issuer, purchase data representing a purchase made by the
cardholder, including a type of merchant, amount of purchase, date
of purchase, and other data, which may be transmitted between any
parties of multi-party payment processing system 20.
[0027] In a typical payment card system, a financial institution
called the "issuer" issues a payment card, such as a credit card,
to a consumer or cardholder 22, who uses the payment card to tender
payment for a purchase from a merchant 24. To accept payment with
the payment card, merchant 24 must normally establish an account
with a financial institution that is part of the financial payment
processing system. This financial institution is usually called the
"merchant bank," the "acquiring bank," or the "acquirer."
[0028] When cardholder 22 tenders payment for a purchase with a
payment card, merchant 24 requests authorization from a merchant
bank 26 for the amount of the purchase. The request may be
performed over the telephone, online, or through the use of a
point-of-sale terminal, which reads the cardholder's account
information from a magnetic stripe, a chip, or embossed characters
on the payment card and communicates electronically with the
transaction processing computers of merchant bank 26.
Alternatively, merchant bank 26 may authorize a third party to
perform transaction processing on its behalf. In this case, the
point-of-sale terminal will be configured to communicate with the
third party. Such a third party is usually called a "merchant
processor," an "acquiring processor," or a "third party
processor."
[0029] Using a payment card network 28, computers of merchant bank
26 or merchant processor will communicate with computers of an
issuer bank 30 to determine whether cardholder's account 32 is in
good standing and whether the purchase is covered by cardholder's
available credit line. To limit an amount of fraud that may occur
during such transactions a fraud detection module may be employed
to screen and analyze the received transaction data. In the example
embodiment, a transaction ticket size pattern module 34 evaluates
historical transaction data for patterns of usage by the
cardholder. The patterns relate to an amount of spend in categories
of goods, categories of stores, individual stores, and seasonal
variations. The patterns are used in a model to evaluate how
closely current transactions comport with the established patterns.
A score is generated that may be a stand-alone determination of the
fraud risk of a transaction or may be a component of a larger
determination of the fraud evaluation performed by, for example,
merchant 24, issuer 30, or both. Ticket size pattern module 34 may
be a stand-alone system that interfaces with network 28 directly
from a remote location or may be a component of systems of network
28. Based on these determinations, the request for authorization
will be declined or accepted. If the request is accepted, an
authorization code is issued to merchant 24.
[0030] When a request for authorization is accepted, the available
credit line of cardholder's account 32 is decreased. Normally, a
charge for a payment card transaction is not posted immediately to
cardholder's account 32 because bankcard associations, such as
MasterCard International Incorporated.RTM., have promulgated rules
that do not allow merchant 24 to charge, or "capture," a
transaction until goods are shipped or services are delivered.
However, with respect to at least some debit card transactions, a
charge may be posted at the time of the transaction. When merchant
24 ships or delivers the goods or services, merchant 24 captures
the transaction by, for example, appropriate data entry procedures
on the point-of-sale terminal. This may include bundling of
approved transactions daily for standard retail purchases. If
cardholder 22 cancels a transaction before it is captured, a "void"
is generated. If cardholder 22 returns goods after the transaction
has been captured, a "credit" is generated. Payment card network 28
and/or issuer bank 30 stores the financial transaction data, such
as a type of merchant, amount of purchase, date of purchase, in a
database 120 (shown in FIG. 2).
[0031] For debit card transactions, when a request for a PIN
authorization is approved by the issuer, the consumer's account is
decreased. Normally, a charge is posted immediately to a consumer's
account. The issuer 30 then transmits the approval to the merchant
bank 26 via the payment network 28, with ultimately the merchant 24
being notified for distribution of goods/services, or information
or cash in the case of an ATM.
[0032] After a purchase has been made, a clearing process occurs to
transfer additional transaction data related to the purchase among
the parties to the transaction, such as merchant bank 26, payment
card network 28, and issuer bank 30. More specifically, during
and/or after the clearing process, additional data, such as a time
of purchase, a merchant name, a type of merchant, purchase
information, cardholder account information, a type of transaction,
product or service for sale information, information regarding the
purchased item and/or service, and/or other suitable information,
is associated with a transaction and transmitted between parties to
the transaction as transaction data, and may be stored by any of
the parties to the transaction.
[0033] After a transaction is authorized and cleared, the
transaction is settled among merchant 24, merchant bank 26, and
issuer bank 30. Settlement refers to the transfer of financial data
or funds among the merchant's account, merchant bank 26, and issuer
bank 30 related to the transaction. Usually, transactions are
captured and accumulated into a "batch," which is settled as a
group. More specifically, a transaction is typically settled
between issuer bank 30 and payment card network 28, and then
between payment card network 28 and merchant bank 26, and then
between merchant bank 26 and merchant 24.
[0034] Network 28 is configured to interface with transaction
ticket size pattern module 34 configured to process historical
transaction data for a plurality of cardholders. Transaction ticket
size pattern module 34 receives the historical transaction data and
processes the transaction data to extract information that is
transmitted to a model included as part of transaction ticket size
pattern module 34 or a part of server 112.
[0035] FIG. 2 is a simplified block diagram of an example payment
processing system 122 including a plurality of computer devices
including transaction ticket size pattern module 34 in accordance
with one example embodiment of the present disclosure. In the
example embodiment, the plurality of computer devices includes, for
example, server system 112, client systems 114, and ticket size
pattern module 34.
[0036] More specifically, transaction ticket size pattern module 34
in communication with server system 112 is configured to receive
historical card-present and card-not-present payment card
transaction data from a plurality of merchants for cardholders
associated with respective unique primary account numbers. Using
the merchant information and transaction amount contained within
the received card-present payment card transaction data,
transaction ticket size pattern module 34 is configured to
determine a spending pattern profile of a cardholder. The spending
pattern profile may include a ticket size pattern profile and other
profiles relating to categories of spending, spend frequency, and a
variation in spend per visit to a merchant store or website. The
spending pattern profile may also be specific to a particular
industry, or merchant category. For example, a first spending
pattern profile may be determined for a hardware or home
improvement industry. A second spending pattern profile may be
determined for a grocery industry.
[0037] More specifically, in the example embodiment, payment
processing system 122 includes a server system 112, and a plurality
of client sub-systems, also referred to as client systems 114,
connected to server system 112. In one embodiment, client systems
114 are computers including a web browser, such that server system
112 is accessible to client systems 114 using the Internet. Client
systems 114 are interconnected to the Internet through many
interfaces including a network, such as a local area network (LAN)
or a wide area network (WAN), dial-in-connections, cable modems,
and special high-speed Integrated Services Digital Network (ISDN)
lines. Client systems 114 could be any device capable of
interconnecting to the Internet including a web-based phone, PDA,
or other web-based connectable equipment.
[0038] Payment processing system 122 also includes point-of-sale
(POS) terminals 118, which may be connected to client systems 114
and may be connected to server system 112. POS terminals 118 are
interconnected to the Internet through many interfaces including a
network, such as a local area network (LAN) or a wide area network
(WAN), dial-in-connections, cable modems, wireless modems, and
special high-speed ISDN lines. POS terminals 118 could be any
device capable of interconnecting to the Internet and including an
input device capable of reading information from a consumer's
financial transaction card.
[0039] A database server 116 is connected to database 120, which
contains information on a variety of matters, as described below in
greater detail. In one embodiment, centralized database 120 is
stored on server system 112 and can be accessed by potential users
at one of client systems 114 by logging onto server system 112
through one of client systems 114. In an alternative embodiment,
database 120 is stored remotely from server system 112 and may be
non-centralized.
[0040] Database 120 may include a single database having separated
sections or partitions or may include multiple databases, each
being separate from each other. Database 120 may store transaction
data generated as part of sales activities conducted over the
processing network including data relating to merchants, account
holders or customers, issuers, acquirers, purchases made. Database
120 may also store account data including at least one of a
cardholder name, a cardholder address, a primary account number
(PAN) associated with the cardholder name, and other account
identifier. Database 120 may also store merchant data including a
merchant identifier that identifies each merchant registered to use
the network, and instructions for settling transactions including
merchant bank account information. Database 120 may also store
purchase data associated with items being purchased by a cardholder
from a merchant, and authorization request data. Database 120 may
store picture files associated with the item or service for sale by
the merchant user, name, price, description, shipping and delivery
information, instructions for facilitating the transaction, and
other information to facilitate processing according to the method
described in the present disclosure.
[0041] Database 120 interfaces with ticket size pattern module 34
to provide ticket size pattern module 34 with transaction
information matched to a card number acquired during the
transaction. The transaction information includes a merchant
identifier, a transaction amount, transaction category, and a
transaction type (POS or online). Additional transaction
information may be provided.
[0042] In the example embodiment, one of client systems 114 may be
associated with acquirer bank 26 (shown in FIG. 1) while another
one of client systems 114 may be associated with issuer bank 30
(shown in FIG. 1). POS terminal 118 may be associated with a
participating merchant 24 (shown in FIG. 1) or may be a computer
system and/or mobile system used by a cardholder making an on-line
purchase or payment. Server system 112 may be associated with
payment card network 28. In the example embodiment, server system
112 is associated with a financial transaction processing network,
such as payment card network 28, and may be referred to as an
interchange computer system. Server system 112 may be used for
processing transaction data. In addition, client systems 114 and/or
POS 118 may include a computer system associated with at least one
of an online bank, a bill payment outsourcer, an acquirer bank, an
acquirer processor, an issuer bank associated with a transaction
card, an issuer processor, a remote payment processing system, a
biller, and a transaction ticket size pattern module 34.
Transaction ticket size pattern module 34 may be associated with
payment card network 28 or with an outside third party in a
contractual relationship with payment card network 28. Accordingly,
each party involved in processing transaction data are associated
with a computer system shown in payment processing system 122 such
that the parties can communicate with one another as described
herein.
[0043] Using payment card network 28, the computers of the merchant
bank or the merchant processor communicate with the computers of
the issuer bank to determine whether the consumer's account is in
good standing and whether the purchase is covered by the consumer's
available credit line. Based on these determinations, the request
for authorization will be declined or accepted. If the request is
accepted, an authorization code is issued to the merchant.
[0044] When a request for authorization is accepted, the available
credit line of consumer's account is decreased. Normally, a charge
is not posted immediately to a consumer's account because bankcard
associations, such as MasterCard International Incorporated.RTM.,
have promulgated rules that do not allow a merchant to charge, or
"capture," a transaction until goods are shipped or services are
delivered. When a merchant ships or delivers the goods or services,
the merchant captures the transaction by, for example, appropriate
data entry procedures on the point-of-sale terminal. If a consumer
cancels a transaction before it is captured, a "void" is generated.
If a consumer returns goods after the transaction has been
captured, a "credit" is generated.
[0045] For debit card transactions, when a request for a PIN
authorization is approved by the issuer, the consumer's account is
decreased. Normally, a charge is posted immediately to a consumer's
account. The bankcard association then transmits the approval to
the acquiring processor for distribution of goods/services, or
information or cash in the case of an ATM.
[0046] After a transaction is captured, the transaction is settled
between the merchant, the merchant bank, and the issuer. Settlement
refers to the transfer of financial data or funds between the
merchant's account, the merchant bank, and the issuer related to
the transaction. Usually, transactions are captured and accumulated
into a "batch," which is settled as a group.
[0047] The financial transaction cards or payment cards discussed
herein may include credit cards, debit cards, a charge card, a
membership card, a promotional card, prepaid cards, and gift cards.
These cards can all be used as a method of payment for performing a
transaction. As described herein, the term "financial transaction
card" or "payment card" includes cards such as credit cards, debit
cards, and prepaid cards, but also includes any other devices that
may hold payment account information, such as mobile phones,
personal digital assistants (PDAs), key fobs, or other devices,
etc.
[0048] FIG. 3 is an expanded block diagram of an example embodiment
of a server architecture of the payment processing system 122 shown
in FIG. 2 in accordance with one example embodiment of the present
disclosure. Components in system 122, identical to components of
payment processing system 122 (shown in FIG. 2), are identified in
FIG. 3 using the same reference numerals as used in FIG. 2. System
122 includes server system 112, client systems 114, and POS
terminals 118. Server system 112 further includes database server
116, a transaction server 124, a web server 126, a fax server 128,
a directory server 130, and a mail server 132. A storage device 134
is coupled to database server 116 and directory server 130. Servers
116, 124, 126, 128, 130, and 132 are coupled in a local area
network (LAN) 136. In addition, a system administrator's
workstation 138, a user workstation 140, and a supervisor's
workstation 142 are coupled to LAN 136. Alternatively, workstations
138, 140, and 142 are coupled to LAN 136 using an Internet link or
are connected through an Intranet. Processing system 122 also
includes transaction ticket size pattern module 34.
[0049] Each workstation, 138, 140, and 142 is a personal computer
having a web browser. Although the functions performed at the
workstations typically are illustrated as being performed at
respective workstations 138, 140, and 142, such functions can be
performed at one of many personal computers coupled to LAN 136.
Workstations 138, 140, and 142 are illustrated as being associated
with separate functions only to facilitate an understanding of the
different types of functions that can be performed by individuals
having access to LAN 136.
[0050] Server system 112 is configured to be communicatively
coupled to various individuals, including employees 144 and to
third parties, e.g., account holders, customers, auditors,
developers, consumers, merchants, acquirers, issuers, etc., 146
using an ISP Internet connection 148. The communication in the
example embodiment is illustrated as being performed using the
Internet, however, any other wide area network (WAN) type
communication can be utilized in other embodiments, i.e., the
systems and processes are not limited to being practiced using the
Internet. In addition, and rather than WAN 150, local area network
136 could be used in place of WAN 150.
[0051] In the example embodiment, any authorized individual having
a workstation 154 can access system 122. At least one of the client
systems includes a manager workstation 156 located at a remote
location. Workstations 154 and 156 are personal computers having a
web browser. Also, workstations 154 and 156 are configured to
communicate with server system 112. Furthermore, fax server 128
communicates with remotely located client systems, including a
client system 156 using a telephone link. Fax server 128 is
configured to communicate with other client systems 138, 140, and
142 as well.
[0052] Transaction ticket size pattern module 34 is communicatively
coupled to database 120, application server 124, and database
server 116 to request transaction data and receive the transaction
data.
[0053] FIG. 4 illustrates an example configuration of a user system
202 operated by a user 201, such as cardholder 22 (shown in FIG.
1). User system 202 may include, but is not limited to, client
systems 114, 138, 140, and 142, POS terminal 118, workstation 154,
and manager workstation 156. In the example embodiment, user system
202 includes a processor 205 for executing instructions. In some
embodiments, executable instructions are stored in a memory area
210. Processor 205 may include one or more processing units, for
example, a multi-core configuration. Memory area 210 is any device
allowing information such as executable instructions and/or written
works to be stored and retrieved. Memory area 210 may include one
or more computer readable media.
[0054] User system 202 also includes at least one media output
component 215 for presenting information to user 201. Media output
component 215 is any component capable of conveying information to
user 201. In some embodiments, media output component 215 includes
an output adapter such as a video adapter and/or an audio adapter.
An output adapter is operatively coupled to processor 205 and
operatively couplable to an output device such as a display device,
a liquid crystal display (LCD), organic light emitting diode (OLED)
display, or "electronic ink" display, or an audio output device, a
speaker or headphones.
[0055] In some embodiments, user system 202 includes an input
device 220 for receiving input from user 201. Input device 220 may
include, for example, a keyboard, a pointing device, a mouse, a
stylus, a touch sensitive panel, a touch pad, a touch screen, a
gyroscope, an accelerometer, a position detector, or an audio input
device. A single component such as a touch screen may function as
both an output device of media output component 215 and input
device 220. User system 202 may also include a communication
interface 225, which is communicatively couplable to a remote
device such as server system 112. Communication interface 225 may
include, for example, a wired or wireless network adapter or a
wireless data transceiver for use with a mobile phone network,
Global System for Mobile communications (GSM), 3G, or other mobile
data network or Worldwide Interoperability for Microwave Access
(WIMAX).
[0056] Stored in memory area 210 are, for example, computer
readable instructions for providing a user interface to user 201
via media output component 215 and, optionally, receiving and
processing input from input device 220. A user interface may
include, among other possibilities, a web browser and client
application. Web browsers enable users, such as user 201, to
display and interact with media and other information typically
embedded on a web page or a web site from server system 112. A
client application allows user 201 to interact with a server
application from server system 112.
[0057] FIG. 5 illustrates an example configuration of a server
system 301 such as server system 112 (shown in FIGS. 2 and 3).
Server system 301 may include, but is not limited to, database
server 116, transaction server 124, web server 126, fax server 128,
directory server 130, and mail server 132.
[0058] Server system 301 includes a processor 305 for executing
instructions. Instructions may be stored in a memory area 310, for
example. Processor 305 may include one or more processing units
(e.g., in a multi-core configuration) for executing instructions.
The instructions may be executed within a variety of different
operating systems on the server system 301, such as UNIX, LINUX,
Microsoft Windows.RTM., etc. It should also be appreciated that
upon initiation of a computer-based method, various instructions
may be executed during initialization. Some operations may be
required in order to perform one or more processes described
herein, while other operations may be more general and/or specific
to a particular programming language (e.g., C, C#, C++, Java, or
other suitable programming languages, etc.).
[0059] Server system 301 may be a part of or communicatively
coupled to transaction ticket size pattern module 34. Transaction
ticket size pattern module 34 in communication with server system
112 is configured to receive transaction information for a
plurality of transactions conducted by a cardholder. The
transaction information includes a merchant identifier, a
transaction amount, transaction category, and a transaction type
(POS or online). The transaction information is used to establish a
pattern of usage of the payment card associated with the
cardholder. The pattern of usage or cardholder profile indicates
the spending habits or the cardholder. The pattern of usage
establishes a baseline of spending of the cardholder for various
goods, categories of businesses, such as, but, not limited to
hardware, grocery, clothing, electronics, restaurants, and the
like. The baseline of spending also includes an average amount
spent at each category of business and a typical variation in the
amount such as a standard deviation from the average amount spent
per visit or per time period. For each new transaction by a
cardholder, transaction information is received by transaction
ticket size pattern module 34 and analyzed with respect to the
profile of the cardholder. Transaction ticket size pattern module
34 determines whether parameters of the new transaction are within
threshold ranges of determined parameters of the cardholder
profile. For example, if the ticket size, or amount of the
transaction is within a predetermined range about an average amount
and whether a total amount of spending for a predetermined time
period is within a predetermine range of the total amount of
spending for a similar time period in the cardholder profile. In
the example embodiment, transaction ticket size pattern module 34
is external to server system 301 and may be accessed by multiple
server systems 301. For example, transaction ticket size pattern
module 34 may be a computing device coupled to a memory unit. In
some embodiments, transaction ticket size pattern module 34 may be
integrated with server system 301. For example, transaction ticket
size pattern module 34 may be a specifically programmed section of
server system 301 configured to perform the functions described
herein when executed by processor 305.
[0060] Processor 305 is operatively coupled to a communication
interface 315 such that server system 301 is capable of
communicating with a remote device such as a user system or another
server system 301. For example, communication interface 315 may
receive requests from user system 114 via the Internet, as
illustrated in FIGS. 2 and 3.
[0061] Processor 305 may also be operatively coupled to a storage
device 134. Storage device 134 is any computer-operated hardware
suitable for storing and/or retrieving data. In some embodiments,
storage device 134 is integrated in server system 301. For example,
server system 301 may include one or more hard disk drives as
storage device 134. In other embodiments, storage device 134 is
external to server system 301 and may be accessed by a plurality of
server systems 301. For example, storage device 134 may include
multiple storage units such as hard disks or solid state disks in a
redundant array of inexpensive disks (RAID) configuration. Storage
device 134 may include a storage area network (SAN) and/or a
network attached storage (NAS) system.
[0062] In some embodiments, processor 305 is operatively coupled to
storage device 134 via a storage interface 320. Storage interface
320 is any component capable of providing processor 305 with access
to storage device 134. Storage interface 320 may include, for
example, an Advanced Technology Attachment (ATA) adapter, a Serial
ATA (SATA) adapter, a Small Computer System Interface (SCSI)
adapter, a RAID controller, a SAN adapter, a network adapter,
and/or any component providing processor 305 with access to storage
device 134.
[0063] Memory area 310 may include, but are not limited to, random
access memory (RAM) such as dynamic RAM (DRAM) or static RAM
(SRAM), read-only memory (ROM), erasable programmable read-only
memory (EPROM), electrically erasable programmable read-only memory
(EEPROM), and non-volatile RAM (NVRAM). The above memory types are
examples only, and are thus not limiting as to the types of memory
usable for storage of a computer program.
[0064] FIG. 6 is a data flow diagram 600 of a purchase transaction
implemented using transaction ticket size pattern module 34 of
payment processing system 122 (shown in FIG. 2). In the example
embodiment, a transaction 602 is attempted at merchant 24 via POS
or an online website associated with merchant 24. The transaction
information is transmitted to network 28. The card number of the
payment card being used for the transaction is matched with
historical transactions conducted using the same card number in,
for example, database 120 (shown in FIG. 2). From the historical
transactions, the merchant or store ID, transaction amount,
transaction category, and transaction type (whether card-present or
card-not-present) are determined. Using the payment card number, a
predetermined number of transactions (approved or declined)
associated with the payment card are extracted 604. For example,
the predetermined number of transactions may represent a
statistically significant number of transactions or the
transactions from a selectable time period.
[0065] A historical ticket size pattern is created 606 based on
average ticket size and dispersions at the same store, similar
stores, and relevant merchant categories. A current transaction
amount is compared with the historical spend ticket size pattern,
and similarity measurements are created and input into a modeling
process 608. For some frequent spending categories, the
cardholder's consumption level can be represented by a frequency
and an average ticket size. For example, a card may used at a
grocery store where the purchase transactions equal approximately
$50+/-$5, three times per week. With other dimensions or parameters
relating to the cardholder account or the particular transaction,
an alert may be generated if a new transaction is swiped with value
that exceeds a predetermined range about a normal or average
transaction value determined for that cardholder, for example, $25
or $85. For each transaction in a specific category, transaction
ticket size pattern module 34 determines whether the current
transaction ticket size exceeds a predetermined range about an
average or normal ticket size determined for that cardholder. With
other measurement and models, such as information from travel
tickets, transaction ticket size pattern module 34 can recommend
610 approval 612 or decline 614 of a transaction from a ticket size
perspective. Reported fraud 616 may be reported, which is used in
future transaction evaluations. Cleared transactions 618 are stored
for future processing.
[0066] An algorithm that may be used with transaction ticket size
pattern module 34 may include:
[0067] For a transaction, in a brick and mortar store or online
(BM/OL) is represented by (k) and a category of the BM/OL is
represented by (i): Amount A(k,i,t) and transaction count
N(k,i,t).
A(k,i)=.SIGMA..sub.tA(k,i, t) (1)
where, A represents a transaction amount, k represents whether
transaction is online or at bricks-mortar store, i represents an
industry category (one of the 100+industries available), and t
represents a time duration considered.
N(k,i)=.SIGMA..sub.tN(k,i,t) (2)
where, N represents a transaction count.
T ( k , i ) = A ( k , i ) N ( k , i ) ( 3 ) ##EQU00001##
where, T represents a ticket size.
.sigma. ( k , i ) .about. t A 2 ( k , i , t ) N ( k , i ) - T 2 ( k
, i ) ( 4 ) ##EQU00002##
where, .sigma. represents a standard deviation of ticket size,
TABLE-US-00001 For N(k, i) = 0 : D(k, i) = A(k, i, t + 1) =0 there
is no history, =1 there was one transaction in the past .gtoreq.2
there are more than 1 transaction in the past
For a new transaction:
A(k,i,t+1) (5)
For (k,i).gtoreq.2:
D(k,i)=A(k,i,t+1)-{T(k,i)+.alpha..sub.2(k,i).sigma.(k,i)} (6)
For N(k,i)=1: D(k,i)=A(k,i,t+1)-.alpha..sub.1(k,i)T(k,i) (7)
For N(k,i)=0: D(k,i)=A(k,i,t+1)-.alpha..sub.0(k,i) (8)
Overall:
[0068]
D(k,i)=A(k,i,t+1)-.delta..sub.n=0.alpha..sub.0(k,i)-.delta..sub.n=-
1.alpha..sub.1(k,i)T(k,i)-.delta..sub.n.gtoreq.2{T(k,i)+.alpha..sub.2(k,i)-
.sigma.(k,i)} (9)
[0069] Values for a range of an amount for future transactions is
determined by modeling and a parameter .alpha..sub.n(k,i) is
determined by the logistic equation:
ln ( B ( k , i ) G ( k , i ) ) = D ( k , i ) ( 10 )
##EQU00003##
[0070] A cut-off of D (k,i) as an alert by weight is determined
back to the original universe.
[0071] Here, .alpha..sub.n(k,i) are model coefficients, and
.delta..sub.n=i is a delta function which equals 1 when n=i and 0
otherwise.
[0072] The model is configured to find a best function of D and
other variables to create maximal separation between future good
(G) and bad (B) transactions.
[0073] FIG. 7 is a data flow diagram of an example embodiment of
transaction ticket size pattern module 34 of payment processing
system 122 (shown in FIG. 2). In the example embodiment,
transaction ticket size pattern module 34 is in communication with
payment processing system 122 or is a part of payment processing
system 122. A plurality of payment card transactions 702 are
received by payment processing system 122 from a plurality of
merchants 24 (shown in FIG. 1) for a plurality of cardholders 22
(shown in FIG. 1). The transactions may be received in batch or
each transaction may be received in real-time during the
transaction. The transactions are directed to database 120 and may
also be received by a transaction ticket size pattern comparison
module 704 of transaction ticket size pattern module 34. Plurality
of transactions 702 are stored in database 120 where they are
accessible to a transaction ticket size pattern model 706. In an
embodiment, transaction ticket size pattern model 706 includes an
algorithm, such as, the algorithm described above with reference to
FIG. 6. Moreover, transaction ticket size pattern model 706 may
include several selectable algorithms that are configured to
account for variations of parameters relating to plurality of
transactions 702 and a score or approval/decline recommendation
desired. For example, transaction ticket size pattern model 706 my
use separate algorithms to account for seasonal variations of a
cardholder's transaction ticket size pattern. Transaction ticket
size pattern model 706 is configured to generate profiles of the
cardholder's historical spending behavior and transmit the profiles
to transaction ticket size pattern comparison module 704 for
evaluation of incoming new transactions 702.
[0074] Initially, transaction ticket size pattern module 34
executes transaction ticket size pattern model 706 on plurality of
payment card transactions 702 that are stored in database 120.
After initial profiles are established, transaction ticket size
pattern model 706 may use incoming new transactions 702 to update
the existing cardholder profiles or may execute transaction ticket
size pattern model 706 on database 120 updated with incoming new
transactions 702. A transaction ticket size pattern score is
generated using the profiles and transmitted to merchant 24, issuer
30, and/or network 28 for incorporation into an authorization
request response. In some embodiments, the transaction ticket size
pattern score is used to directly affect the approval/decline
decision for the transaction. In other embodiments, the transaction
ticket size pattern score forms a portion of the approval/decline
decision for the transaction made by issuer 30 and/or merchant
24.
[0075] FIG. 8 is a component view of an example transaction ticket
size pattern module 34 of payment processing system 122 (shown in
FIG. 2). In the example embodiment, transaction ticket size pattern
module 34 includes a database 802. Database 802 stores, for
example, financial transaction data 813 received from, for example,
server system 112 (shown in FIG. 2). Database 802 may further store
operating parameter rules 814 and cardholder profiles 815.
[0076] In the example embodiment, transaction ticket size pattern
module 34 further includes a receiving component 802 configured to
receive transaction information from for example, at least one of a
merchant point of sale (POS) device and a merchant website, the
transaction information including a current transaction amount, the
transaction information associated with a single payment card
cardholder. Transaction ticket size pattern module 34 further
includes a retrieving component 804 configured to retrieve a
predetermined number of historical transactions for the single
cardholder based on the transaction information. Transaction ticket
size pattern module 34 further includes a generating component 806,
a historical spend ticket size pattern based on average ticket sixe
and dispersions for at least one of the same store, similar stores,
and relevant merchant categories. Transaction ticket size pattern
module 34 further includes a comparing component 810 configured to
compare the current transaction amount to the historical spend
ticket size pattern and a generating component 812 configured to
generate a recommendation for approval or decline of the current
financial transaction based on the comparison.
[0077] FIG. 9 is a flow chart of a method 900 of fraud detection
based on a pattern of transaction ticket size on a payment card
network. In the example embodiment, method 900 includes receiving
902 transaction information, for a current financial transaction,
from at least one of a merchant point of sale (POS) device and a
merchant website, the transaction information including a current
transaction amount, the transaction information associated with a
single payment card cardholder. Method 900 further includes
retrieving 904 a predetermined number of historical transactions
for the single cardholder based on the transaction information and
generating 906 a historical spend ticket size pattern based on
average ticket size and dispersions at at least one of the same
store, similar stores, and relevant merchant categories. Method 900
further includes comparing the current transaction amount to the
historical spend ticket size pattern and generating a
recommendation for approval or decline of the current financial
transaction based on the comparison.
[0078] The term processor, as used herein, refers to central
processing units, microprocessors, microcontrollers, reduced
instruction set circuits (RISC), application specific integrated
circuits (ASIC), logic circuits, and any other circuit or processor
capable of executing the functions described herein.
[0079] As used herein, the terms "software" and "firmware" are
interchangeable, and include any computer program stored in memory
for execution by mobile devices, clusters, personal computers,
workstations, clients, servers, and processor 205, 305 wherein the
memory includes RAM memory, ROM memory, EPROM memory, EEPROM
memory, and non-volatile RAM (NVRAM) memory. The above memory types
are examples only, and are thus not limiting as to the types of
memory usable for storage of a computer program.
[0080] As will be appreciated based on the foregoing specification,
the above-discussed embodiments of the disclosure may be
implemented using computer programming or engineering techniques
including computer software, firmware, hardware or any combination
or subset thereof. Any such resulting program, having
computer-readable and/or computer-executable instructions, may be
embodied or provided within one or more computer-readable media,
thereby making a computer program product, i.e., an article of
manufacture, according to the discussed embodiments of the
disclosure. The computer readable media may be, for instance, a
fixed (hard) drive, diskette, optical disk, magnetic tape,
semiconductor memory such as read-only memory (ROM) or flash
memory, etc., or any transmitting/receiving medium such as the
Internet or other communication network or link. The article of
manufacture containing the computer code may be made and/or used by
executing the instructions directly from one medium, by copying the
code from one medium to another medium, or by transmitting the code
over a network. The technical effect of the methods and systems may
be achieved by performing at least one of the following steps: (a)
receiving transaction information, for a current financial
transaction, from at least one of a merchant point of sale (POS)
device and a merchant website, the transaction information
including a current transaction amount, the transaction information
associated with a single payment card cardholder; (b) retrieving a
predetermined number of historical transactions for the single
cardholder based on the transaction information; (c) generating a
historical spend ticket size pattern based on average ticket size
and dispersions at at least one of the same store, similar stores,
and relevant merchant categories; (d) comparing the current
transaction amount to the historical spend ticket size pattern; and
(e) generating a recommendation for approval or decline of the
current financial transaction based on the comparison.
[0081] As used herein, the term "non-transitory computer-readable
media" is intended to be representative of any tangible
computer-based device implemented in any method or technology for
short-term and long-term storage of information, such as,
computer-readable instructions, data structures, program modules
and sub-modules, or other data in any device. Therefore, the
methods described herein may be encoded as executable instructions
embodied in a tangible, non-transitory, computer readable medium,
including, without limitation, a storage device and/or a memory
device. Such instructions, when executed by a processor, cause the
processor to perform at least a portion of the methods described
herein. Moreover, as used herein, the term "non-transitory
computer-readable media" includes all tangible, computer-readable
media, including, without limitation, non-transitory computer
storage devices, including, without limitation, volatile and
nonvolatile media, and removable and non-removable media such as a
firmware, physical and virtual storage, CD-ROMs, DVDs, and any
other digital source such as a network or the Internet, as well as
yet to be developed digital means, with the sole exception being a
transitory, propagating signal.
[0082] As used herein, the term "computer" and related terms, e.g.,
"computing device", are not limited to integrated circuits referred
to in the art as a computer, but broadly refers to a
microcontroller, a microcomputer, a programmable logic controller
(PLC), an application specific integrated circuit, and other
programmable circuits, and these terms are used interchangeably
herein.
[0083] As used herein, the term "cloud computing" and related
terms, e.g., "cloud computing devices" refers to a computer
architecture allowing for the use of multiple heterogeneous
computing devices for data storage, retrieval, and processing. The
heterogeneous computing devices may use a common network or a
plurality of networks so that some computing devices are in
networked communication with one another over a common network but
not all computing devices. In other words, a plurality of networks
may be used in order to facilitate the communication between and
coordination of all computing devices.
[0084] As used herein, the term "mobile computing device" refers to
any of computing device which is used in a portable manner
including, without limitation, smart phones, personal digital
assistants ("PDAs"), computer tablets, hybrid phone/computer
tablets ("phablet"), or other similar mobile device capable of
functioning in the systems described herein. In some examples,
mobile computing devices may include a variety of peripherals and
accessories including, without limitation, microphones, speakers,
keyboards, touchscreens, gyroscopes, accelerometers, and
metrological devices. Also, as used herein, "portable computing
device" and "mobile computing device" may be used
interchangeably.
[0085] Approximating language, as used herein throughout the
specification and claims, may be applied to modify any quantitative
representation that could permissibly vary without resulting in a
change in the basic function to which it is related. Accordingly, a
value modified by a term or terms, such as "about" and
"substantially", are not to be limited to the precise value
specified. In at least some instances, the approximating language
may correspond to the precision of an instrument for measuring the
value. Here and throughout the specification and claims, range
limitations may be combined and/or interchanged, such ranges are
identified and include all the sub-ranges contained therein unless
context or language indicates otherwise.
[0086] The above-described embodiments of a method and system of
fraud detection based on a pattern of transaction ticket size on a
payment card network. More specifically, the methods and systems
described herein facilitate generating profiles of cardholder
spending patterns, including representations of the cardholder's
typical spend in various categories of brick and mortar and online
stores. In addition, the above-described methods and systems
facilitate modeling the spend behavior of cardholders to account
for variations in their typical spend patterns, such as, for
out-of-town travel for vacations and business, seasonal variations
to account for weather or holiday spending changes to the typical
spend pattern. As a result, the methods and systems described
herein facilitate recommending an approval or decline of a
financial transaction in a cost-effective and reliable manner.
[0087] This written description uses examples to describe the
disclosure, including the best mode, and also to enable any person
skilled in the art to practice the disclosure, including making and
using any devices or systems and performing any incorporated
methods. The patentable scope of the application is defined by the
claims, and may include other examples that occur to those skilled
in the art. Such other examples are intended to be within the scope
of the claims if they have structural elements that do not differ
from the literal language of the claims, or if they include
equivalent structural elements with insubstantial differences from
the literal languages of the claims.
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