Patent Application
20170132607
In U.S. Published Patent Application No. 2013/0290119 (which names Howe et al. as inventors and which is commonly assigned herewith), it was proposed to use time and location data associated with payment card accounts with time and location data associated with cell phones to infer connections between the payment card accounts and the users of the cell phones. A stated reason for this analysis is to further a payment transaction security strategy based on detecting when a corresponding cell phone was not in proximity to a location of a current payment card account transaction.
The present inventors have now recognized that inferred matching from payment card accounts to mobile devices may also present novel opportunities to enhance data analytics.
Features and advantages of some embodiments of the present disclosure, and the manner in which the same are accomplished, will become more readily apparent upon consideration of the following detailed description of the disclosure taken in conjunction with the accompanying drawings, which illustrate preferred and exemplary embodiments and which are not necessarily drawn to scale, wherein:
Embodiments of the present disclosure relate to systems and methods for analyzing transaction data and data indicative of individuals' locations at various times. More particularly, embodiments relate to systems and methods for comparing mobile device travel profiles with transaction profiles for individual holders of payment card accounts, in order to potentially match transaction profiles with mobile device travel profiles. Residence location data for the owner of the mobile device associated with the mobile device travel profile may be obtained and associated with the matched transaction profile to enhance location-based analyses.
The term “mobile device travel profile” refers to a set of data that reflects locations at which a cell phone or other mobile device was present at various points of time in the past.
The terms “de-identified data” or “de-identified data sets” are used to refer to data or data sets which have been processed or filtered to remove any personally identifiable information (“PII”). The de-identification may be performed in any of a number of ways, although in some embodiments, the de-identified data may be generated using a filtering process which removes PII and associates a de-identified unique identifier (or de-identified unique “ID”) with each record (as will be described further below).
The term “payment card network” or “payment network” is used to refer to a payment network or payment system such as the systems operated by MasterCard
International Incorporated (which is the assignee hereof), or other networks which process payment transactions on behalf of a number of merchants, issuers and cardholders. The terms “payment card network data” or “network transaction data” are used to refer to transaction data associated with payment transactions that have been processed over a payment network. For example, network transaction data may include a number of data records associated with individual payment transactions that have been processed over a payment card network. In some embodiments, network transaction data may include information identifying a payment device or account, transaction date and time, transaction amount, and information identifying a merchant or merchant category, and a location at which the transaction occurred. Additional transaction details may be available in some embodiments.
The data analysis system 100 may include a source 102 of network transaction data produced in and stored by a conventional payment network (not shown) in connection with payment card account transactions handled by the payment network. The transaction data may be in the form of transaction profiles, or may be processed so as to be in that form. Each transaction profile may represent transactions performed using a particular payment card account.
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Block 108 in
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Features of some embodiments of the present disclosure will now be described with reference to
The data analysis system 100 includes a matching/probabilistic engine 202 to provide matching of payment card account data sets to mobile device travel profiles and to assign confidence scores to the matches. In some embodiments, the matching/probabilistic engine 202 receives or analyzes data from two or more data sources, including transaction data 204 (which may come from the transaction data source 102 shown in
Furthermore, at block 212, the transaction data 204 may be anonymized by removing any PII therefrom. For example, the anonymizing block 212 may substitute a de-identified unique identifier code for the PII that was associated with each transaction profile before anonymization. In some embodiments the PII may be a PAN (primary account number) for the corresponding payment card account and the de-identified unique identifier code may be generated by applying a function to the PAN. The function may be, for example, a hash function or the like. The anonymizing block 212 may generate a lookup table 214 to link the de-identified unique identifier for each transaction profile to the PAN or other PII originally associated with the transaction profile before it was anonymized. Consequently, in some embodiments, the transaction data as provided to the matching/probabilistic engine 202 may be de-identified data.
At block 216, the mobile device travel profile data may be pre-processed to place it in a correct format for the matching/probabilistic engine 202 and/or to remove unnecessary data elements.
In some embodiments, the matching/probabilistic engine 202 may operate to perform an inferred match analysis to assess an inferred linkage between the mobile device travel profile data and the transaction data. The inferred match analysis may be based in part on the portion of the transaction data that indicates the dates/times/locations of the transactions.
As used herein, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within modules, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network. In addition, entire modules, or portions thereof, may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like or as hardwired integrated circuits.
In some embodiments, the modules of
The matching/probabilistic engine 202 may be operated to establish a linkage between the mobile device travel profile data and the transaction data. In some embodiments, the linkage may be a probability score or other scoring measure that indicates, as between a mobile device travel profile and a transaction profile how likely it is that the two profiles correspond to the same individual. Examples of suitable analytic techniques will be discussed below in connection with
The computer system 302 may be conventional in its hardware aspects but may be controlled by software to cause it to function as described herein. In some embodiments, functionality disclosed herein may be distributed among two or more computers having a hardware architecture similar to that described below. In some embodiments, the computer system 302 may be constituted in its hardware aspects by one or more mainframe or server computers.
The computer system 302 may include a computer processor 300 operatively coupled to a communication device 301, a storage device 304, an input device 306 and an output device 308.
The computer processor 300 may be constituted by one or more conventional processors. Processor 300 operates to execute processor-executable steps, contained in program instructions described below, so as to control the computer system 302 to provide desired functionality.
Communication device 301 may be used to facilitate communication with, for example, other devices (such as sources of mobile device travel profile data and transaction data). For example, communication device 301 may comprise one or more communication ports (not separately shown), to allow the computer system 302 to communicate with other computers and other devices.
Input device 306 may comprise one or more of any type of peripheral device typically used to input data into a computer. For example, the input device 306 may include a keyboard and a mouse. Output device 308 may comprise, for example, a display and/or a printer.
Storage device 304 may comprise any appropriate information storage device, including combinations of magnetic storage devices (e.g., hard disk drives), optical storage devices such as CDs and/or DVDs, and/or semiconductor memory devices such as Random Access Memory (RAM) devices and Read Only Memory (ROM) devices, as well as so-called flash memory. Any one or more of such information storage devices may be considered to be a computer-readable storage medium or a computer usable medium or a memory.
Storage device 304 stores one or more programs for controlling processor 300. The programs comprise program instructions (which may be referred to as computer readable program code means) that contain processor-executable process steps of the computer system 302, executed by the processor 300 to cause the computer system 302 to function as described herein.
The programs may include one or more conventional operating systems (not shown) that control the processor 300 so as to manage and coordinate activities and sharing of resources in the computer system 302, and to serve as a host for application programs (described below) that run on the computer system 302.
The programs stored in the storage device 304 may also include, in some embodiments, a data preparation program 310 that controls the processor 300 to enable the computer system 302 to perform operations on data received by the computer system 302 to place the data in an appropriate condition for subsequent analysis and profile matching. For example, mobile device travel profile data may be translated into a standard format to facilitate detection of possible matches between mobile device travel profiles and transaction profiles.
Another program that may be stored in the storage device 304 is profile matching application program 312 that controls the processor 300 to enable the computer system 302 to perform analysis with respect to the profile data, to detect potential matches between mobile device travel profiles and transaction profiles and to calculate scores that may be applied to the potential matches to indicate the degree of confidence that the potential matches are correct. Other aspects of the profile matching application program 312 will be described below in connection with
In addition, the storage device 304 may store a location data append program 314 that controls the processor 300 to enable the computer system 302 to obtain residence location data that corresponds to an owner of a mobile device for which the mobile device travel profile was matched with at least a certain degree of confidence with a transaction data set. The location data append program 314 may further append the mobile-related residence location data to the transaction data set and/or to an identifier for the transaction data set.
Still further, the storage device 304 may store an application program 316 that performs one or more analyses on collections of data sets that have had the mobile-related residence location data appended thereto. Such analysis may utilize the appended residence location data.
The storage device 304 may also store, and the computer system 302 may also execute, other programs, which are not shown. For example, such programs may include e.g., communication software, a reporting program, a database management program, device drivers, etc.
The storage device 304 may also store one or more databases 318 required for operation of the computer system 302. Such databases, for example, may store at least temporarily the mobile device travel profiles and the transaction profiles to be analyzed and matched by the computer system 302. Such databases may also store combined data sets to which mobile device related residence location data has been appended.
At 402 in
At 404 in
Block 406 in
At decision block 408, the computer system 302 may determine, for the current transaction data set, whether the location/time profile it represents contains enough transactions and/or exhibits sufficient diversity in location and/or time to make it likely that a reliable match could be made between the current transaction data set and one of the mobile device travel profiles. If such is not the case, the process may conclude (reference numeral 409) with respect to the current transaction data set.
If a positive determination is made at decision block 408, then blocks 410 and 412 may follow decision block 408 with respect to the current transaction data set. At block 410, the computer system 302 applies a matching algorithm to determine to what extent the current transaction data set should be considered a match for each of the mobile device travel profiles received at 404. A number of different types of matching algorithms may be employed. For example, the matching algorithm may include a calculation of the mathematical distance between the set of location/time pairs contained in the current transaction data set and the set of location/time pairs contained in the mobile device travel profile that is currently under consideration. In addition or alternatively, the algorithm may involve calculating a spline of the geographic location data contained in one or both of the current transaction data set and the current mobile device travel profile.
In addition to or instead of the matching techniques referred to above, it is contemplated to use one or more of a considerable number of matching techniques, including one or more measurements of correlation, linear or logistic regression, variable reduction analysis, distance statistics, clustering analysis, and/or decision tree analysis. Other matching analysis techniques may also or alternatively be used.
As part of block 410, and based on the match algorithm or algorithms employed therein, the mobile device travel profile that best matches the current transaction data set is determined Then, at 412, again based on the match algorithm or algorithms, a degree of confidence is determined as to the validity of the match between the current transaction data set and the mobile device travel profile that best matches the current transaction data set.
A decision block 414 may follow block 412. At decision block 414, the computer system 302 may determine whether the degree of confidence assigned at 412 exceeds a predetermined confidence level threshold. If so, then the match or link between the current transaction data set and the best matching mobile device travel profile may be deemed reliable or valid, as indicated at block 416. In other words, it is reasonable to conclude that the payment card account that corresponds to the current transaction data set is owned by the same individual who owns the mobile device that corresponds to the best matching mobile device travel profile. In a formal, final sense, this may reflect an operational match between the current transaction data set and the best matching mobile device travel profile. (If a negative determination is made at decision block 414, the process may conclude (reference numeral 415) with respect to the current transaction data set.)
At block 502 in
In some embodiments, the computer system 302 may obtain the mobile device related residence location data by analyzing the corresponding mobile device travel profile and/or other time/location data provided by the data source 104 (
At block 504, the mobile device owner's residence location data may be appended to a transaction data set and/or to an identifier that represents the transaction data set. It will be appreciated that the transaction data set and/or identifier to which the residence location data is appended has been operationally matched to the corresponding mobile device travel profile. In this way, mobile-related residence location data, which may be more accurate than transaction-based inferred residence location data, may be used to provide the necessary location based data to support improved location-based analysis of individuals, or aggregated groups of individuals, who are represented by the transaction data. Block 506 represents the subsequent location-based analysis, based on the residence location data obtained via matching of mobile devices to transaction data sets. This data analysis may be performed by the computer system 302 and/or by another computer system to which the enhanced location-based data is exported.
The mobile to transaction account matching, with importation of mobile based residence location data, as described above, may facilitate improved and/or more reliable location based analysis of groups of individuals, which may lead to improved marketing strategies and/or other location-based initiatives. This may occur in a manner that does not involve analysis or disclosure of personally identifiable information. That is, the benefit of improved location based analysis/data sets may be achieved based on processing of anonymized data.
To give just one example of the type of analysis that may be enabled or improved based on teachings of this disclosure, it may be feasible for a retail store or retail chain to obtain a more accurate understanding of the aggregate residence locations of their customers.
Although a number of “assumptions” are provided herein, the assumptions are provided as illustrative, but not limiting examples of one particular embodiment—those skilled in the art will appreciate that other embodiments may have different rules or assumptions.
As used herein and in the appended claims, the term “computer” should be understood to encompass a single computer or two or more computers in communication with each other.
As used herein and in the appended claims, the term “processor” should be understood to encompass a single processor or two or more processors in communication with each other.
As used herein and in the appended claims, the term “memory” should be understood to encompass a single memory or storage device or two or more memories or storage devices.
The flow charts and descriptions thereof herein should not be understood to prescribe a fixed order of performing the method steps described therein. Rather the method steps may be performed in any order that is practicable, including simultaneous performance of at least some steps.
As used herein and in the appended claims, the term “payment card system account” includes a credit card account, a deposit account that the account holder may access using a debit card, a prepaid card account, or any other type of account from which payment transactions may be consummated. The terms “payment card system account” and “payment card account” are used interchangeably herein. The term “payment card account number” includes a number that identifies a payment card system account or a number carried by a payment card, or a number that is used to route a transaction in a payment system that handles debit card and/or credit card transactions. The term “payment card” includes a credit card, debit card, prepaid card, or other type of payment instrument, whether an actual physical card, virtual accounts or electronic wallets.
Although the present disclosure has been described in connection with specific exemplary embodiments, it should be understood that various changes, substitutions, and alterations apparent to those skilled in the art can be made to the disclosed embodiments without departing from the spirit and scope of the disclosure as set forth in the appended claims.
