Patent ID: 11954685
Assignee: SONY GROUP CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G  H

Claim 9:
10. A non-transitory computer-readable storage medium storing instructions thereon which, when executed by a computer, cause the computer to perform a method, the method comprising:
selecting a subset of training transactions from a plurality of training transactions, wherein each training transaction comprises an indication whether the training transaction is fraudulent or genuine, the selected subset of training transactions being used for training a machine-learning model, the machine-leaning model being used for classifying transactions as either fraudulent or genuine, wherein selecting a subset of training transactions includes
clustering the plurality of training transactions into a plurality of clusters based on a similarity measure;
determining the number of clusters to be used in the clustering of the plurality of training transactions based on the plurality of training transactions, wherein the number of clusters to be used in the clustering of the plurality of training transactions is determined by
using an iterative process that is performed over a range of numbers of clusters resulting in a plurality of results each comprising a plurality of clusters;
comparing at least one property of the plurality of clusters of the plurality of results, wherein the number of clusters to be used is based on a trade-off between intra-cluster variance and inter-cluster variance of the clustering of the plurality of training transactions; and
selecting a result and the associated number of clusters for which the at least one property is improved compared to other results of the plurality of results;

selecting a subset of training transactions from the plurality of training transactions, wherein the method for selecting the subset of training transactions includes
selecting a first proportion of the training transactions of a cluster of training transactions for the subset of training transactions in response to the cluster including at least one fraudulent training transaction;
selecting a second proportion of the training transactions of a cluster of training transactions for the subset of training transactions in response to the cluster being free of fraudulent training transactions, the second proportion being less than the first proportion, wherein the training transactions that are selected for the second proportion of the subset of training transactions are selected based on a proximity of said training transactions to a centroid of the respective cluster;

providing the selected subset of training transactions for training a machine-learning model, wherein the selected subset of training transactions improves accuracy in classifying transactions as either fraudulent or genuine, increases the amount of useful information and reduces the number of data points used for training, and provides a reduced training set that contains a high variety of transaction types that leads to highly discriminative information for effective modeling, and
operating a fraud detection system using the trained machine-learning model to classify a transaction as either fraudulent or genuine.