Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud Detection. (arXiv:2108.02501v3 [cs.LG] UPDATED)

For the highly imbalanced credit card fraud detection problem, most existing
methods either use data augmentation methods or conventional machine learning
models, while neural network-based anomaly detection approaches are lacking.
Furthermore, few studies have employed AI interpretability tools to investigate
the feature importance of transaction data, which is crucial for the black-box
fraud detection module. Considering these two points together, we propose a
novel anomaly detection framework for credit card fraud detection as well as a
model-explaining module responsible for …

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