Sampling framework for imbalanced transactions

Abstract

A server computer that implements an efficient sampling technique for imbalanced transactions. Transaction data comprising unlabeled transaction data and fraudulent transaction data is encoded to form (i) a first set of encoded data associated with fraudulent transactions and unlabeled transactions that are similar to fraudulent transactions, and (ii) a second set of encoded data associated with unlabeled transactions. A first sampling process is executed with respect to the first set of encoded data to obtain a first sampled set of encoded data, and a second sampling process is executed with respect to the second set of encoded data to obtain a second sampled set of encoded data. An optimal sampling size of the transaction data is determined based on whether a performance of a machine learning model that classifies the first sampled set of encoded data and the second sampled set of encoded data satisfies a condition.