Credit card fraud detection - Classifier selection strategy
This work addresses the challenge of inconsistent classifier performance across diverse and imbalanced fraud datasets for financial institutions, though it is incremental in nature.
The paper tackled the problem of selecting effective classifiers for highly imbalanced credit card fraud detection datasets by proposing a data-driven strategy that includes sampling methods for imbalance handling. The resulting model outperformed peer models under realistic conditions, demonstrating the strategy's effectiveness.
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.