Abstract: A computer system for identifying merchant category code misclassifications includes at least one processor in communication with a transaction database and a merchant database. The transaction database stores transaction records by a plurality of account holders. The processor generates a first MCC profile including at least one transaction characteristic representative of merchants properly classified as the first MCC and comparing the first MCC profile to a second set of transaction records. If the comparison satisfies a comparison threshold for the first MCC the processor identifies the corresponding selected merchant as being MCC misclassified.

Abstract: A computer system for identifying merchant category code misclassifications includes at least one processor in communication with a transaction database and a high-risk merchant database. The transaction database stores transaction records by a plurality of account holders. The high-risk merchant database stores high-risk merchant records each associated with high-risk merchants. The processor queries the transaction database for transaction records and calculates a high-risk cardholder metric for each of the account numbers. The at least one processor further queries the transaction database for transaction records including (i) the account number of high-risk cardholders, and (ii) a merchant identifier associated with other than the plurality of high-risk merchants, to retrieve a second set of transaction records. The at least one processor further calculates a high-risk merchant metric for each of the merchant identifiers, identifying a MCC misclassified merchant.

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: A computer system for identifying merchant category code misclassifications includes at least one processor in communication with a transaction database and a high-risk merchant database. The transaction database stores transaction records by a plurality of account holders. The high-risk merchant database stores high-risk merchant records each associated with high-risk merchants. The processor queries the transaction database for transaction records and calculates a high-risk cardholder metric for each of the account numbers. The at least one processor further queries the transaction database for transaction records including (i) the account number of high-risk cardholders, and (ii) a merchant identifier associated with other than the plurality of high-risk merchants, to retrieve a second set of transaction records. The at least one processor further calculates a high-risk merchant metric for each of the merchant identifiers, identifying a MCC misclassified merchant.

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: 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 predicting a probability of a problem in a service at a service provider based on implementation of a change to the service. One exemplary method includes a risk engine accessing change records for historical changes in services associated with the service provider where each record includes a text description of the implemented change and a problem/no problem result for the change. For each record, the risk engine normalizes the text description of the implemented change and generates a word-count matrix based on the normalized text description. The risk engine then performs a regression analysis of the generated word-count matrices for the records and the corresponding problem/no problem results, thereby providing a regression model, and generates a predictive algorithm based on a score provided from the regression model and at least one change factor associated with the change records.