Digital payment card fraud: new vectors and detection

Abstract

This research explores gross losses from payment card fraud worldwide and considers the main modern methods of fraud. The issue of payment fraud will be exacerbated by the digitalization of economic relations, in particular the introduction by banks of the concept of “Bank-as-a-Service,” which will increase the burden on payment services. The aim of this study is to show effective methods for detecting fraud in digital payment systems using automated machine learning and big data analysis algorithms. Approaches to expanding the information base to detect fraudulent transactions are proposed and systematized. The choice of performance metrics for building and comparing models is substantiated. The use of automatic machine learning algorithms is proposed to resolve the issue, which makes it possible in a short time to go through a large number of variants of models, their ensembles, and input data sets. As a result, our experiments allow us to obtain a quality of classification based on the AUC metric at a level that exceeds the effectiveness of the classifiers developed by traditional methods, even as the time spent on the synthesis of the models is much less and is measured in hours. The ensemble of models makes it possible to detect up to 85.7% of fraudulent transactions in the sample. The accuracy of fraud detection is also high. The results of our study confirm the effectiveness of using automatic machine learning algorithms to synthesize fraud detection models in digital payment systems. In this case, efficiency is manifested not only by the resulting classifiers’ quality but also by the reduction in the cost of their development, as well as by the high potential of interpretability. Implementing the study results could enable financial institutions to reduce the financial and temporal costs of developing and updating active systems against payment fraud, as well as improve the effectiveness of monitoring financial transactions.