Abstract

Fraudsters adapt and get over increasingly complex obstacles put up by public or private organisations. Financial institutions are among those that need to act swiftly to avoid losses while keeping legitimate customers happy.

Effective banking in the face of a growing volume of activities requires data analytics to back up traditional risk control measures, and a deeper insight into the mechanisms at work in fraudulent behaviour.

In addition, the procedure of gathering evidence is important because fraud is a criminal and huge offence. Constraints imposed by legal, operational, and strategic constraints necessitate adjusting approaches to combating fraud. The initial part of this research is dedicated to figuring out how to evaluate a fraud detection model's effectiveness in terms of real-world issues encountered by banks at each level of the fraud prevention and management process. Second, it examines several different machine learning strategies that consider these peculiarities and workarounds the gap between fraudulent and nonfraudulent transactions, the dearth of fully trusted labels, the concept-drift phenomenon, and the inevitable compromise between detection accuracy and interpretability.

This state-of-the-art review throws light on a technological conflict between intrinsically interpretable models that have been improved for accuracy and black box machine learning models that have been augmented by post-hoc interpretation.

The paper concludes with a discussion of how genuine and promising hybrid sampling and machine learning approaches might give financial institutions and regulators real, short-term solutions without enveloping stakeholders with financial and moral interests in this technological race.

The accuracy versus interpretability trade-off in the application of machine models for predicting fraudulent credit card transactions