An approach to benchmark fraud detection algorithms in the COVID-19 era

Abstract

To address the challenges in the fight against financial crime, particularly in the COVID-19 pandemic context, this paper focuses on financial synthetic data and the use of a reliable benchmark tool to test fraud detection algorithms. Compliance departments at financial institutions face the challenge of reducing the number of innocent people erroneously accused of fraud. To cope with this problem financial institutions are exploring the application of machine learning fraud detection algorithms and data analysis technologies to develop a more accurate and precise fraud detection system. However, approaches to streamlining and automating banks’ monitoring and testing processes is challenging as there is no consensus on a benchmark. We explore the relevance of measuring the applicability of a financial crime benchmark in the presence of a growing digital financial sector, such as in the case of Mexico. This study is particularly important due to serious threats that are faced by a rapidly developing financial system (2019 Mexican Central Bank Report). These risks have been further exacerbated as a result of the COVID-19 pandemic accelerating the shift towards digital payments.