Credit card fraud detection - Classifier selection strategy

TL;DR

This paper proposes a data-driven classifier selection strategy for credit card fraud detection that effectively handles highly imbalanced and diverse datasets, outperforming existing models in realistic scenarios.

Contribution

It introduces a novel classifier selection approach tailored for imbalanced fraud detection data, considering dataset diversity and temporal drift.

Findings

The proposed strategy outperforms peer models in accuracy.

Sampling methods improve detection on imbalanced datasets.

The approach remains effective under realistic, dynamic conditions.

Abstract

Machine learning has opened up new tools for financial fraud detection. Using a sample of annotated transactions, a machine learning classification algorithm learns to detect frauds. With growing credit card transaction volumes and rising fraud percentages there is growing interest in finding appropriate machine learning classifiers for detection. However, fraud data sets are diverse and exhibit inconsistent characteristics. As a result, a model effective on a given data set is not guaranteed to perform on another. Further, the possibility of temporal drift in data patterns and characteristics over time is high. Additionally, fraud data has massive and varying imbalance. In this work, we evaluate sampling methods as a viable pre-processing mechanism to handle imbalance and propose a data-driven classifier selection strategy for characteristic highly imbalanced fraud detection data sets. The model derived based on our selection strategy surpasses peer models, whilst working in more realistic conditions, establishing the effectiveness of the strategy.