Method, apparatus and computer programs for generating a machine-learning system and for classifying a transaction as either fraudulent or genuine

Abstract

Embodiments of the present disclosure relate to a method, an apparatus and a computer program for providing a machine-learning system to be used for classifying transactions as either fraudulent or genuine, and a method, an apparatus and a computer program for classifying transactions as either fraudulent or genuine. The method for generating a machine-learning system for classifying transactions as either fraudulent or genuine based on a plurality of training transactions, each training transaction being associated with labelling information that indicates whether the training transaction is either genuine or fraudulent, comprises clustering the plurality of training transactions into a plurality of clusters based on a similarity measure. The method comprises determining, for each of the plurality of clusters, whether the cluster is homogeneous or heterogeneous. A heterogeneous cluster includes both fraudulent and genuine training transactions. A homogeneous cluster includes either only fraudulent or only genuine training transactions. The method comprises, for each of the heterogeneous clusters, training an associated machine-learning model using the training transactions belonging to the respective heterogeneous cluster.