用强化学习优化脉冲神经网络，实现公平可解释的金融欺诈检测
Reinforcement-Guided Hyper-Heuristic Hyperparameter Optimization for Fair and Explainable Spiking Neural Network-Based Financial Fraud Detection
- 用Q-learning动态选择优化策略，兼顾公平与召回率
- 在银行欺诈数据集上达90.8%召回率，误报率5%时仍保持98%预测公平性
- 结合脉冲活动分析与显著性图，让模型决策过程可解释
居家银行系统普及加剧网络欺诈风险，亟需兼具准确性、公平性与可解释性的检测模型。现有AI方法存在计算效率低、脉冲神经网络（SNN）可解释性差及强化学习（RL）超参数优化不稳定等问题。本文提出融合皮层脉冲网络与群体编码（CSNPC）和强化引导超启发式优化器（RHOSS）的框架。CSNPC利用群体编码提升分类鲁棒性，RHOSS通过Q-learning在公平性和召回率约束下自适应选择低级启发式策略。系统集成于MoSSTI框架，通过显著性图与脉冲活动分析实现可解释性。在银行账户欺诈（BAF）数据集上，模型在5%误报率下达到90.8%召回率，优于以往脉冲与经典模型，且跨人口群体预测公平性超98%。尽管RHOSS引入离线优化开销，但部署后可分摊。CSNPC的稀疏结构相较密集人工神经网络进一步降低能耗。结果表明，群体编码SNN结合强化学习引导的超启发式优化，可实现公平、可解释且高性能的欺诈检测。
原文摘要 · Abstract (English)
The growing adoption of home banking systems has increased cyberfraud risks, requiring detection models that are accurate, fair, and explainable. While AI methods show promise, they face challenges including computational inefficiency, limited interpretability of spiking neural networks (SNNs), and instability in reinforcement learning (RL)-based hyperparameter optimization. We propose a framework combining a Cortical Spiking Network with Population Coding (CSNPC) and a Reinforcement-Guided Hyper-Heuristic Optimizer (RHOSS). CSNPC leverages population coding for robust classification, while RHOSS applies Q-learning to adaptively select low-level heuristics under fairness and recall constraints. Integrated within the MoSSTI framework, the system incorporates explainable AI via saliency maps and spike activity profiling. Evaluated on the Bank Account Fraud (BAF) dataset, the model achieves 90.8% recall at 5% false positive rate, outperforming prior spiking and classical models while maintaining over 98% predictive equality across demographic groups. Although RHOSS introduces offline optimization cost, it is amortized at deployment. The sparse architecture of CSNPC further reduces energy consumption compared to dense ANNs. Results demonstrate that combining population-coded SNNs with RL-guided hyper-heuristics enables fair, interpretable, and high-performance fraud detection.
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