用双路径生成框架实现银行零日欺诈的低延迟检测与可解释性。
A Dual-Path Generative Framework for Zero-Day Fraud Detection in Banking Systems
- VAE构建合法交易流形，结合重构误差实现实时检测
- WGAN-GP生成高熵欺诈场景，提升模型抗攻击能力
- 仅对高不确定性交易触发SHAP解释，兼顾效率与可解释性
高频银行环境面临低延迟欺诈检测与GDPR要求的可解释性之间的关键矛盾。传统基于规则和判别式模型在零日攻击下表现不佳，主要受限于极端类别不平衡及缺乏历史先例。本文提出一种双路径生成框架，将实时异常检测与离线对抗训练分离。架构采用变分自编码器（VAE）基于重构误差建立合法交易流形，确保推理延迟低于50ms。同时，异步Wasserstein GAN带梯度惩罚（WGAN-GP）生成高熵欺诈场景，用于压力测试检测边界。针对银行数据（如商户分类码）的离散性导致的不可导问题，引入Gumbel-Softmax估计器。此外，设计基于触发的可解释机制，仅对高不确定性交易激活SHAP（Shapley Additive Explanations），在满足实时吞吐量的同时缓解XAI计算开销。
原文摘要 · Abstract (English)
High-frequency banking environments face a critical trade-off between low-latency fraud detection and the regulatory explainability demanded by GDPR. Traditional rule-based and discriminative models struggle with "zero-day" attacks due to extreme class imbalance and the lack of historical precedents. This paper proposes a Dual-Path Generative Framework that decouples real-time anomaly detection from offline adversarial training. The architecture employs a Variational Autoencoder (VAE) to establish a legitimate transaction manifold based on reconstruction error, ensuring <50ms inference latency. In parallel, an asynchronous Wasserstein GAN with Gradient Penalty (WGAN-GP) synthesizes high-entropy fraudulent scenarios to stress-test the detection boundaries. Crucially, to address the non-differentiability of discrete banking data (e.g., Merchant Category Codes), we integrate a Gumbel-Softmax estimator. Furthermore, we introduce a trigger-based explainability mechanism where SHAP (Shapley Additive Explanations) is activated only for high-uncertainty transactions, reconciling the computational cost of XAI with real-time throughput requirements.
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