Abstract: A method and system for recommending a merchant are provided. The method includes receiving financial transaction data documenting financial transactions between a plurality of account holders and a plurality of merchants and generating a merchant correspondence matrix that includes the plurality of merchants and a plurality of indicators of interactions associated with pairs of the plurality of merchants. The plurality of indicators of interactions tallying financial transactions conducted by the plurality of account holders at both of the merchants in a pair of the plurality of merchants. The method further includes receiving a query for a recommendation of a merchant from an account holder and generating a ranked list of merchants based on a recommender algorithm. The recommender algorithm inferring user preferences from attributes of the plurality of merchants that were visited by the cardholder.

Abstract: A method and system for recommending a merchant are provided. The method includes receiving financial transaction data documenting financial transactions between a plurality of account holders and a plurality of merchants and generating a merchant correspondence matrix that includes the plurality of merchants and a plurality of indicators of interactions associated with pairs of the plurality of merchants. The plurality of indicators of interactions tallying financial transactions conducted by the plurality of account holders at both of the merchants in a pair of the plurality of merchants. The method further includes receiving a query for a recommendation of a merchant from an account holder and generating a ranked list of merchants based on a recommender algorithm. The recommender algorithm inferring user preferences from attributes of the plurality of merchants that were visited by the cardholder.

Abstract: A computer-implemented method for associating a merchant with an aggregate merchant uses a computing device having a processor and a memory. The method includes identifying an association rule for the aggregate merchant, including one or more antecedents. Each antecedent includes a model value for the antecedent associated with the aggregate merchant. The method also includes identifying one or more merchant data values associated with the merchant. Each of the one or more merchant data values correspond to one of the one or more antecedents. The method further includes applying the association rule to the one or more merchant data values by comparing the model value for each antecedent with a merchant data value associated with the corresponding antecedent, thereby generating a confidence score for the merchant. The confidence score represents a likelihood the merchant is associated with the aggregate merchant. The method also includes outputting the confidence score.

Abstract: A method and system for recommending a merchant are provided. The method includes receiving financial transaction data documenting financial transactions between a plurality of account holders and a plurality of merchants and generating a merchant correspondence matrix that includes the plurality of merchants and a plurality of indicators of interactions associated with pairs of the plurality of merchants. The plurality of indicators of interactions tallying financial transactions conducted by the plurality of account holders at both of the merchants in a pair of the plurality of merchants. The method further includes receiving a query for a recommendation of a merchant from an account holder and generating a ranked list of merchants based on a recommender algorithm. The recommender algorithm inferring user preferences from attributes of the plurality of merchants that were visited by the cardholder.

Abstract: A computer-based method for managing a profile for a cardholder is provided. The cardholder having an account associated with a payment card. The method includes electronically receiving, at the computer, transaction information for a cardholder for transactions with at least a first business entity and a second business entity, the transaction information including data representing each transaction initiated by the cardholder using the payment card and at least one subdivision within the first business entity, the transaction information further including data representing each transaction initiated by the cardholder using the payment card and the second business entity.

Abstract: Systems and methods are provided for performing anomaly detection. One example method relates to transaction data including fraud scores output by a fraud score model generated by a machine learning system. The method includes determining, by a computing device, divergence values for multiple segments of payment accounts between baseline distributions of fraud scores and current distributions of fraud scores for the segments and detecting, by the computing device, at least one of the divergence values for at least one of the multiple segments as an anomaly. The method also includes categorizing, by the computing device, the detected anomaly into one of multiple categories, whereby the one of the multiple categories is indicative of a type of issue associated with the detected anomaly.

Abstract: Systems and methods are provided for use in identifying recurring local entities for a target region. One exemplary method includes receiving a request for identifying recurring local entities for a target region and accessing data for the target region for multiple accounts. The data is representative of multiple transactions and multiple accounts, with each transaction having an account number associated with a user and a region identifier associated with an entity involved in the transaction. The method also includes determining a local region for each of the accounts included in the data and, for each determined local region that is the same as the target region, identifying one or more recurring entity from the transactions to said accounts having the determined local region. The method then includes compiling a listing of recurring local entities including the identified one or more recurring entities.

Abstract: Systems and methods are provided for evaluating aggregate merchant sets, which are often generated by a payment network. One exemplary method includes accessing, by a computing device, a monitor score and a volatility score for an aggregate merchant set representative of multiple merchants having at least one disparate parameter in a transaction data structure, fuzzy sets for the monitor score and the volatility score comprising linguistic values, and inference rules that use the linguistic values in logical operations. The method also generally includes determining degrees of membership of the monitor score and volatility score to the associated fuzzy sets and generating an evaluation index based on the inference rules and the degrees of membership to the fuzzy sets, thereby providing an indication of a propriety of the aggregation of said multiple merchants.

Abstract: Systems and methods are provided for processing messages from users, via interactive interfaces, requesting support relating to features of applications available to the users. In connection therewith, the systems and methods normalize and score the user messages, and use the scores, in combination with scores for historical messages or scores for crowdsourced messages, to identify appropriate response messages for transmittal to the users. In various aspects, the crowdsourced solutions can be voted by both users and domain experts, and also verified by the domain experts. If a solution is verified it will be flagged and it can be included into a reference solution database as a standard response. However, when the scores assigned to the messages do not produce suitable results, a request for additional information relating to the issue described by the user is transmitted via the interactive interface.

Abstract: Systems and methods are provided for use in performing data quality checks on input variables to machine learning systems. One exemplary method includes calculating a first moment associated with a long term variable (LTV), based on the value of the LTV and historical values of the LTV over a defined interval; and calculating a second moment associated with the LTV, based on the value of the LTV and the historical values of the LTV over the defined interval. The first moment and the second moment provide a moment pair. An isolation forest analysis is performed based on the moment pairs. And, a flag is generated for the LTV, when a check value of the LTV is different than the value of the LTV, and/or when the isolation forest analysis indicates the calculated moment pair is an anomaly.

Abstract: Systems and methods are provided for performing anomaly detection. One exemplary method relates to transaction data including fraud scores output by a fraud score model generated by a machine learning system. The method includes accessing fraud scores for a segment of payment accounts for a target interval and for a series of similar intervals, generating a baseline distribution and a current distribution based on the fraud scores. A divergence value is then determined based on the baseline distribution and the current distribution. An activeness of the segment of payment accounts is also determined, and the operations are repeated for one or more other segments of payment accounts. The method further includes clustering the multiple divergence pairs and designated one or more of the multiple divergence pairs as abnormal based on the clustered divergence pairs.

Abstract: A computer-implemented method for associating a merchant with an aggregate merchant uses a computing device having a processor and a memory. The method includes identifying an association rule for the aggregate merchant, including one or more antecedents. Each antecedent includes a model value for the antecedent associated with the aggregate merchant. The method also includes identifying one or more merchant data values associated with the merchant. Each of the one or more merchant data values correspond to one of the one or more antecedents. The method further includes applying the association rule to the one or more merchant data values by comparing the model value for each antecedent with a merchant data value associated with the corresponding antecedent, thereby generating a confidence score for the merchant. The confidence score represents a likelihood the merchant is associated with the aggregate merchant. The method also includes outputting the confidence score.

Abstract: Systems and methods are provided for evaluating aggregate merchant sets, which are often generated by a payment network. One exemplary method includes accessing, by a computing device, a monitor score and a volatility score for an aggregate merchant set representative of multiple merchants having at least one disparate parameter in a transaction data structure, fuzzy sets for the monitor score and the volatility score comprising linguistic values, and inference rules that use the linguistic values in logical operations. The method also generally includes determining degrees of membership of the monitor score and volatility score to the associated fuzzy sets and generating an evaluation index based on the inference rules and the degrees of membership to the fuzzy sets, thereby providing an indication of a propriety of the aggregation of said multiple merchants.

Abstract: A computer-implemented method for associating a merchant with an aggregate merchant uses a computing device having a processor and a memory. The method includes identifying an association rule for the aggregate merchant, including one or more antecedents. Each antecedent includes a model value for the antecedent associated with the aggregate merchant. The method also includes identifying one or more merchant data values associated with the merchant. Each of the one or more merchant data values correspond to one of the one or more antecedents. The method further includes applying the association rule to the one or more merchant data values by comparing the model value for each antecedent with a merchant data value associated with the corresponding antecedent, thereby generating a confidence score for the merchant. The confidence score represents a likelihood the merchant is associated with the aggregate merchant. The method also includes outputting the confidence score.

Abstract: Systems and methods are provided for evaluating aggregate merchant sets, which are often generated by a payment network. One exemplary method includes accessing, by a computing device, a monitor score and a volatility score for an aggregate merchant set representative of multiple merchants having at least one disparate parameter in a transaction data structure, fuzzy sets for the monitor score and the volatility score comprising linguistic values, and inference rules that use the linguistic values in logical operations. The method also generally includes determining degrees of membership of the monitor score and volatility score to the associated fuzzy sets and generating an evaluation index based on the inference rules and the degrees of membership to the fuzzy sets, thereby providing an indication of a propriety of the aggregation of said multiple merchants.

Abstract: A computer-based method for managing a profile for a cardholder is provided. The cardholder having an account associated with a payment card. The method includes electronically receiving, at the computer, transaction information for a cardholder for transactions with at least a first business entity and a second business entity, the transaction information including data representing each transaction initiated by the cardholder using the payment card and at least one subdivision within the first business entity, the transaction information further including data representing each transaction initiated by the cardholder using the payment card and the second business entity.

Abstract: Systems and methods are provided for modeling transaction data associated with merchant category codes (MCCs) assigned to merchants. An example method includes accessing a transaction data structure having transaction data for a plurality of merchants, and accessing a merchant data structure including multiple merchants and a category descriptor for each of the multiple merchants. The method also includes combining data in the transaction data structure and the merchant data structure based on a merchant identifier for a merchant common thereto, and assigning a label to the common merchant based on at least one category descriptor for the common merchant in the merchant data structure. Further, the method includes generating a model for a MCC assigned to the common merchant, based on transaction data for the common merchant and the assigned label, whereby the model can be used to verify assignment of the MCC to other merchants.

Abstract: Systems and methods are provided for use in distributing code testing tasks among multiple testers, and incentivizing testing with rewards. One exemplary method includes testing, by a computing device, a code set, where the testing is performed by a tester associated with a profile, and receiving, by the computing device, peer tester feedback based on the testing of the code set, where the peer tester feedback is associated with at least one peer tester profile. The method also includes receiving, by the computing device, a developer decision based on the testing of the code set and/or the peer tester feedback, and rewarding, by the computing device, the profile of the tester and/or the at least one peer tester profile when the developer decision agrees with the testing of the code set and/or the tester feedback associated with the at least one peer tester profile.

Abstract: Systems and methods are provided for use in distributing code testing tasks among multiple testers, and incentivizing testing with rewards. One exemplary method includes testing, by a computing device, a code set, where the testing is performed by a tester associated with a profile, and receiving, by the computing device, peer tester feedback based on the testing of the code set, where the peer tester feedback is associated with at least one peer tester profile. The method also includes receiving, by the computing device, a developer decision based on the testing of the code set and/or the peer tester feedback, and rewarding, by the computing device, the profile of the tester and/or the at least one peer tester profile when the developer decision agrees with the testing of the code set and/or the tester feedback associated with the at least one peer tester profile.

Abstract: Systems and methods are provided for evaluating aggregate merchant sets, which are often generated by a payment network. One exemplary method includes accessing, by a computing device, a monitor score and a volatility score for an aggregate merchant set representative of multiple merchants having at least one disparate parameter in a transaction data structure, fuzzy sets for the monitor score and the volatility score comprising linguistic values, and inference rules that use the linguistic values in logical operations. The method also generally includes determining degrees of membership of the monitor score and volatility score to the associated fuzzy sets and generating an evaluation index based on the inference rules and the degrees of membership to the fuzzy sets, thereby providing an indication of a propriety of the aggregation of said multiple merchants.