# US 8,751,399 B2 · Multi-channel data driven, real-time anti-money laundering system for electronic payment cards > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,751,399 B2 - Document ID: US8751399B2 - Status: Granted - Kind: B2 - Application number: 13/549,492 - Filed: 2012-07-15 - Published: 2013-01-17 - Granted: 2014-06-10 - Earliest claimed: 2011-07-15 - Assignees: Wal-Mart Stores, Inc. - Applicants: Jayashree S. Kolhatkar; Sangita S. Fatnani; Yitao Yao; Kazuo Matsumoto - Inventors: Jayashree S. Kolhatkar; Sangita S. Fatnani; Yitao Yao; Kazuo Matsumoto - Web page: https://patentgrep.com/patent/US8751399B2 ## CPC - G06Q 20/02: involving a neutral party, e.g. certification authority, notary or trusted third party [TTP] - G06Q 20/28: Pre-payment schemes, e.g. "pay before" - G06Q 20/34: using cards, e.g. integrated circuit [IC] cards or magnetic cards - G06Q 20/4016: {involving fraud or risk level assessment in transaction processing} - G06Q 20/405: {Establishing or using transaction specific rules} - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract Electronic payment card money laundering detection includes receiving real-time payment card transaction data from ingress channels and an egress channels of at least one payment card system through a first API; generating transactional profiles for each of at least payment cards, the ingress channel, the egress channels, and funding sources of the payment cards; in response to receiving transaction data for a current payment card transaction, evaluating the transaction data using a predictive algorithm that compares the transaction data to the transactional profiles to calculate a probabilistic money laundering score for the current transaction; evaluating the probabilistic money laundering score and current transaction data based on a set of rules to generate a suspicious activity report that recommends whether to approve or report the current transaction; and transmitting the suspicious activity report back to the payment card system and transmitting the suspicious activity report to an identified regulatory body. ## Claims ### Claim 1 A computer-implemented method for detecting electronic payment card money laundering, comprising: receiving, by at least one software component executing on at least one processor, real-time payment card transaction data from ingress channels and channels of at least one payment card system through a first application programming interface (API); generating, by the at least one software component, at least two types of transactional profiles for each of at least: payment cards, the ingress channel, the egress channels, and funding sources of the payment cards, wherein a first type of transactional profile comprises a network graph of links between the ingress channels, the egress channels, the payment cards, any users of the payment cards, and geo-location identities associated with the payment cards; and wherein a second type of transactional profile comprises a series of transactional usage patterns at different time intervals of each of the payment cards, the ingress channels, the egress channels, and the funding sources; in response the at least one software component to receiving transaction data for a current payment card transaction, evaluating the transaction data using a predictive algorithm that compares the transaction data to the transactional profiles to calculate a probabilistic money laundering score for the current transaction; evaluating the probabilistic money laundering score and the current transaction data based on a set of rules to generate a suspicious activity report that recommends whether to approve or report the current transaction; and transmitting the suspicious activity report back to the payment card system and transmitting the suspicious activity report to an identified regulatory body. ### Claim 2 (depends on 1) The method of claim 1 wherein generating transactional profiles further comprises populating an external data verification database with data retrieved from external sources and using the data for verification of geo-location, phone numbers, and addresses associated with the current payment card transaction. ### Claim 3 (depends on 1) The method of claim 1 further comprising calculating the probabilistic money laundering score using multiple predictive algorithms comprising any combination of regression, decision trees, neural networks, random forest, and genetic algorithms. ### Claim 4 (depends on 1) The method of claim 1 wherein evaluating the probabilistic money laundering score further comprises utilizing a rules language to build and deploy cutoff strategies for creating appropriate recommendations. ### Claim 5 (depends on 1) The method of claim 1 wherein transmitting the suspicious activity report back to the payment card system further comprises one of embedding the recommendation as part of a response back to an incoming transaction request and posting the recommendation asynchronously to a pre-defined location. ### Claim 6 (depends on 1) The method of claim 1 wherein the method is performed by an anti-money laundering system that communicates with one or more card payments systems using the first and second APIs. ### Claim 7 (depends on 1) The method of claim 1 wherein the first API comprises a fixed component that allows for uniformity of sending data across multiple channels and a flexible component that allows each of the channels to send data that is unique to that channel. ### Claim 8 An executable software product stored on a non-transitory computer-readable medium containing program instructions for detecting electronic payment card money laundering, the program instructions for: receiving, by the program instructions executing on at least one processor, real-time payment card transaction data from ingress channels and egress channels of at least one payment card system through a first application programming interface (API); generating at least two types of transactional profiles for each of at least: payment cards, the ingress channel, the egress channels, and funding sources of the payment cards, wherein a first type of transactional profile comprises a network graph of links between the ingress channels, the egress channels, the payment cards, any users of the payment cards, and geo-location identities associated with the payment cards; and wherein a second type of transactional profile comprises a series of transactional usage patterns at different time intervals of each of the payment cards, the ingress channels, the egress channels, and the funding sources; in response to receiving transaction data for a current payment card transaction, evaluating the transaction data using a predictive algorithm that compare the transaction data to the transactional profiles to calculate a probabilistic money laundering score for the current transaction; evaluating the probabilistic money laundering score and the current transaction data based on a set of rules to generate a suspicious activity report that recommends whether to approve or report the current transaction; and transmitting the suspicious activity report back to the payment card system and transmitting the suspicious activity report to an identified regulatory body. ### Claim 9 (depends on 8) The software product of claim 8 wherein generating transactional profiles further comprises software instructions for populating an external data verification database with data retrieved from external sources and using the data for verification of geo-location, phone numbers, and addresses associated with the current payment card transaction. ### Claim 10 (depends on 8) The software product of claim 8 further comprising software instructions for calculating the probabilistic money laundering score using multiple predictive algorithms comprising any combination of regression, decision trees, neural networks, random forest, and genetic algorithms. ### Claim 11 (depends on 8) The software product of claim 8 wherein evaluating the probabilistic money laundering score further comprises software instructions for utilizing a rules language to build and deploy cutoff strategies for creating appropriate recommendations. ### Claim 12 (depends on 8) The software product of claim 8 wherein transmitting the suspicious activity report back to the payment card system further comprises software instructions for one of embedding the recommendation as part of a response back to an incoming transaction request and posting the recommendation asynchronously to a pre-defined location. ### Claim 13 (depends on 8) The software product of claim 8 wherein the software instructions comprise an anti-money laundering system that communicates with one or more card payments systems using the first and second APIs. ### Claim 14 (depends on 8) The software product of claim 8 wherein the first API comprises a fixed component that allows for uniformity of sending data across multiple channels and a flexible component that allows each of the channels to send data that is unique to that channel. ### Claim 15 A system, comprising: a memory; at least one processor coupled to the memory; a first application programming interface (API) executed by the processor that is configured to receive real-time payment card transaction data from ingress channels and egress channels of at least one payment card system; an anti-money laundering monitoring component executed by the processor that is configured to generate transactional profiles for each of at least—payment cards, the ingress channel, the egress channels, and funding sources of the payment cards, wherein a first type of transactional profile include a first profile comprises a network graph of links between the ingress channels, the egress channels, the payment cards, any users of the payment cards, and geo-location identities associated with the payment cards; and wherein a second type of transactional profile comprises a series of transactional usage patterns at different time intervals of each of the payment cards, the ingress channels, the egress channels, and the funding sources; an anti-money laundering rules component executed by the processor that is configured to receive transaction data for a current payment card transaction, and evaluate the transaction data using a predictive algorithm that compare the transaction data to the transactional profiles to calculate a probabilistic money laundering score for the current transaction; an anti-money laundering SAR generation component executed by the processor that is configured to evaluate the probabilistic money laundering score and current transaction data based on a set of rules to generate a suspicious activity report that recommends whether to approve or report the current transaction; and a second API executed by the processor that is configured to transmit the recommendation back to the payment card system. ### Claim 16 (depends on 15) The system of claim 15 wherein the anti-money laundering monitoring component further populates an external data verification database with data retrieved from external sources and using the data for verification of geo-location, phone numbers, and addresses associated with the current payment card transaction. ### Claim 17 (depends on 15) The system of claim 15 wherein the anti-money laundering rules component calculates the probabilistic money laundering score using multiple predictive algorithms comprising any combination of regression, decision trees, neural networks, random forest, and genetic algorithms. ### Claim 18 (depends on 15) The system of claim 15 wherein the anti-money laundering rules component evaluates the probabilistic money laundering score utilizing a rules language to build and deploy cutoff strategies for creating appropriate recommendations. ### Claim 19 (depends on 15) The system of claim 15 wherein the recommendation is transmitted back to the payment card system by one of embedding the recommendation as part of a response back to an incoming transaction request and posting the recommendation asynchronously to a pre-defined location. ### Claim 20 (depends on 15) The system of claim 15 wherein the system comprises an anti-money laundering system that communicates with one or more card payments systems using the first and second APIs. ### Claim 21 (depends on 15) The system of claim 15 wherein the first API comprises a fixed component that allows for uniformity of sending data across multiple channels and a flexible component that allows each of the channels to send data that is unique to that channel.