Fraud is Not Just Rarity: A Causal Prototype Attention Approach to Realistic Synthetic Oversampling
arXiv:2507.14706v2 Announce Type: replace Abstract: Detecting fraudulent credit card transactions remains a significant challenge, due to the extreme class imbalance in real-world data and the often subtle patterns that separate fraud from legitimate activity. Existing research commonly attempts to address this by generating synthetic samples for the minority class using approaches such as GANs, VAEs (Variational Autoencoders), or hybrid generative models. However, these techniques, particularly when applied only to minority-class data, tend to result in overconfident classifiers and poor late
The increasing sophistication of fraud attempts and the limitations of current AI-driven fraud detection methods necessitate more robust solutions that move beyond simple rarity-based approaches.
Improved fraud detection with more realistic synthetic data can significantly reduce financial losses for institutions and enhance trust in digital transactions.
The proposed 'causal prototype attention' method offers a more nuanced way to generate synthetic data for fraud detection, moving beyond simple class imbalance to incorporate causal relationships.
- · Financial institutions
- · E-commerce platforms
- · AI/ML researchers
- · Fraudsters
- · Legacy fraud detection systems
More accurate fraud detection leads to fewer successful fraudulent transactions and reduced financial losses.
Increased consumer confidence in digital payment systems due to lower fraud rates could stimulate further online economic activity.
The development of more sophisticated synthetic data generation techniques could find applications in other domains facing data imbalance and complex pattern recognition challenges, including cybersecurity and medical diagnostics.
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Read at arXiv cs.LG