Method for real-time enhancement of a predictive algorithm by a novel measurement of concept drift using algorithmically-generated features

A computer program product comprising a non-transitory machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations comprising:

• selecting at least one attribute of one or more input data records associated with payment card transaction activity;
• partitioning one or more datasets according to a partitioning scheme that is based on the at least one attribute to generate a summary statistic of historical data records, the one or more datasets having at least one input data record;
• generating a set of features based on historical data records, the set of features based on the historical data records being hidden nodes of a feed-forward neural network and at least one of the historical data records having a known class membership, the set of features being generated according to different class labels for the known class membership;
• calculating a set of microscopic derived features for a new input data record or previous input data records, based on the partitioning and the set of features, the set of microscopic derived features being related to the selected at least one attribute;
• generating a set of macroscopic derived features based on the aggregated set of microscopic derived features calculated for the new and previous input data records based on the partitioning and the set of features, the set of macroscopic derived features representing a concept drift of the one or more datasets, the concept drift comprising at least a systematic shift in a property of the one or more data records;
• generating a first score representing a likelihood of an extent and magnitude of the concept drift representing a systematic shift in a property of the one or more input data records over a course of time;
• generating, using the set of microscopic derived features and the set of macroscopic derived features for previous input data records, a second score representing a likelihood that a new input data record is subject to the extent and magnitude of the concept drift representing by the first score;
• adjusting a class membership for the new input data record using the feed-forward neural network according to the partitioning scheme based on the second score and the magnitude of the concept drift, including a binary classification for fraud detection;
• and
• determining the degree with which the payment card transaction activity for a cardholder is unusual with respect to historical cardholder behavior.

A system comprising:

• at least one programmable processor;
• and
• a non-transitory, machine-readable medium storing instructions that, when executed by the at least one processor, cause the at least one programmable processor to perform operations comprising:
• select at least one attribute of one or more input data records associated with payment card transaction activity;
• partition one or more datasets according to a partitioning scheme that is based on the at least one attribute to generate a summary statistic of historical data records, the one or more datasets having at least one input data record;
• generate a set of features based on historical data records, the set of features based on the historical data records being hidden nodes of a feed-forward neural network and at least of the historical data records having a known class membership, the set of features being generated according to different class labels for the known class membership;
• calculate a set of microscopic derived features for a new input data record or previous input data records, based on the partitioning and the set of features, the set of microscopic derived features being related to the selected at least one attribute;
• generate a set of macroscopic derived features based on the aggregated set of microscopic derived features calculated for the new and previous input data records based on the partitioning and the set of features, the set of macroscopic derived features representing concept drift of the one or more datasets;
• generate a first score representing a likelihood of an extent and magnitude of the concept drift;
• generate, using the set of microscopic derived features and the set of macroscopic derived features for previous input data records, a second score representing a likelihood that a new input data record is subject to the extent and magnitude of the concept drift representing by the first score;
• and
• adjust a class membership for the new input data record using the feed-forward neural network according to at least the magnitude of the concept drift, including a binary classification for fraud detection, the feed-forward neural network being trained using backpropagation and stochastic gradient decent on tagged input data records involving at least a first transaction and a second transaction associated with a cardholder, the second transaction being prior in time to the first transaction.