Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. A computing device receives return data identifying the return of at least one item. The computing device obtains modified strategy data identifying at least one rule of a modified strategy. The rule may be based on the application of at least one discrete stochastic gradient descent algorithm to an initial strategy. The computing device applies the modified strategy to the received return data identifying the return of the at least one item, and determines whether the return of the at least one item is fraudulent based on the application of the modified strategy. The computing device generates fraud data identifying whether the return of the at least one item is fraudulent based on the determination, and may transmit the fraud data to another computing device to indicate whether the return is fraudulent.

Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. A computing device receives return data identifying the return of at least one item. The computing device obtains modified strategy data identifying at least one rule of a modified strategy. The rule may be based on the application of at least one dimensionality reduction algorithm to an initial strategy. The computing device applies the modified strategy to the received return data identifying the return of the at least one item, and determines whether the return of the at least one item is fraudulent based on the application of the modified strategy. The computing device generates fraud data identifying whether the return of the at least one item is fraudulent based on the determination, and may transmit the fraud data to another computing device to indicate whether the return is fraudulent.

Abstract: This application relates to apparatus and methods for identifying anomalies within a time series. In some examples, a computing device receives sales data identifying a sale of at least one item, and aggregates the received data in a database. The computing device may generate a plurality of time series based on the aggregated sales data. The computing device may extract features from the plurality of time series, and generate an alerting algorithm that is based on clusters of the extracted features. The computing device may apply the alerting algorithm to a time series generated from received sales data to determine whether the time series is an anomaly. Based on the determination, the computing device may generate and transmit anomaly data identifying whether the time series is an anomaly, such as to another computing device.

Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. A computing device receives return data identifying the return of at least one item. The computing device obtains modified strategy data identifying at least one rule of a modified strategy. The rule may be based on the application of at least one discrete stochastic gradient descent algorithm to an initial strategy. The computing device applies the modified strategy to the received return data identifying the return of the at least one item, and determines whether the return of the at least one item is fraudulent based on the application of the modified strategy. The computing device generates fraud data identifying whether the return of the at least one item is fraudulent based on the determination, and may transmit the fraud data to another computing device to indicate whether the return is fraudulent.

Abstract: This application relates to apparatus and methods for identifying anomalies within a time series. In some examples, a computing device receives sales data identifying a sale of at least one item, and aggregates the received data in a database. The computing device may generate a plurality of time series based on the aggregated sales data. The computing device may extract features from the plurality of time series, and generate an alerting algorithm that is based on clusters of the extracted features. The computing device may apply the alerting algorithm to a time series generated from received sales data to determine whether the time series is an anomaly. Based on the determination, the computing device may generate and transmit anomaly data identifying whether the time series is an anomaly, such as to another computing device.

Abstract: A method and system for identifying and labeling fraudulent store return activities includes receiving, by a server, retailer events from an online transaction system of a retailer, the retailer events comprising records of transactions between customers and the retailer, including sale, exchange and return activities across multiple stores. The retailer events are processed to build a network that associates stores, transactions, payment instruments, and customer identification over related activity sequences of transactions.

Abstract: This application relates to apparatus and methods for identifying anomalies within a time series. In some examples, a computing device receives sales data identifying a sale of at least one item, and aggregates the received data in a database. The computing device may generate a plurality of time series based on the aggregated sales data. The computing device may extract features from the plurality of time series, and generate an alerting algorithm that is based on clusters of the extracted features. The computing device may apply the alerting algorithm to a time series generated from received sales data to determine whether the time series is an anomaly. Based on the determination, the computing device may generate and transmit anomaly data identifying whether the time series is an anomaly, such as to another computing device.

Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. A computing device receives return data identifying the return of at least one item. The computing device obtains modified strategy data identifying at least one rule of a modified strategy. The rule may be based on the application of at least one dimensionality reduction algorithm to an initial strategy. The computing device applies the modified strategy to the received return data identifying the return of the at least one item, and determines whether the return of the at least one item is fraudulent based on the application of the modified strategy. The computing device generates fraud data identifying whether the return of the at least one item is fraudulent based on the determination, and may transmit the fraud data to another computing device to indicate whether the return is fraudulent.

Abstract: This application relates to apparatus and methods for identifying fraudulent transactions. A computing device receives return data identifying the return of at least one item. The computing device obtains modified strategy data identifying at least one rule of a modified strategy. The rule may be based on the application of at least one discrete stochastic gradient descent algorithm to an initial strategy. The computing device applies the modified strategy to the received return data identifying the return of the at least one item, and determines whether the return of the at least one item is fraudulent based on the application of the modified strategy. The computing device generates fraud data identifying whether the return of the at least one item is fraudulent based on the determination, and may transmit the fraud data to another computing device to indicate whether the return is fraudulent.

Abstract: This application relates to apparatus and methods for identifying anomalies within a time series. In some examples, a computing device receives sales data identifying a sale of at least one item, and aggregates the received data in a database. The computing device may generate a plurality of time series based on the aggregated sales data. The computing device may extract features from the plurality of time series, and generate an alerting algorithm that is based on clusters of the extracted features. The computing device may apply the alerting algorithm to a time series generated from received sales data to determine whether the time series is an anomaly. Based on the determination, the computing device may generate and transmit anomaly data identifying whether the time series is an anomaly, such as to another computing device.

Abstract: A method and system for identifying and labeling fraudulent store return activities includes receiving, by a server, retailer events from an online transaction system of a retailer, the retailer events comprising records of transactions between customers and the retailer, including sale, exchange and return activities across multiple stores. The retailer events are processed to build a network that associates stores, transactions, payment instruments, and customer identification over related activity sequences of transactions.

Abstract: The present invention is a method and apparatus that uses a plurality of predetermined segments to group credit applicants to evaluate each applicant's credit risk. The segments are based on at least one of reported trades, reported delinquency, bank card utilization, and length of said credit history. A score is generated for each applicant based on a unique scorecard designed for each segment The unique scorecards allow more accurate credit risk assessment by evaluating each applicant in view of that segment's tendency to be bad credit risks.