PaySeqGen: Interpretable Inverse Reinforcement Learning for Fraud Sequence Generation
PaySeqGen is an interpretable inverse reinforcement learning framework that reconstructs fraud behavior policies from observed payment trajectoriesholistically, generating new fraud sequences for detector augmentation. It learns interpretable behavioral rewards capturing fraudsters' preferences across transaction contexts)Skip, and experiments at Ant International show it outperforms state-of-the-art generative augmentation methods. The framework also enables reward-based trajectory analysis to identify multistep fraud tactics in real payment systems.
Why it matters: This paper presents a novel application of inverse RL to payment fraud, with real-world deployment and interpretability benefits, offering practical value for fraud detection and analysis.