Reinforcement-Guided Hyper-Heuristic Hyperparameter Optimization for Fair and Explainable Spiking Neural Network-Based Financial Fraud Detection

• RHOSS uses Q-learning to guide fairness-aware SNN hyperparameter optimization • CSNPC improves recall at 5% FPR using structured population-coded spiking outputs • MoSSTI framework enables dual-path explainability via saliency and spike profiling • Achieves 90.8% recall and >98% fairness on real-world financial fraud benchmarks • Outperforms classical and SNN baselines under fairness and transparency constraints The growing adoption of home banking systems has heightened the risk of cyberfraud, necessitating fraud detection mechanisms that are not only accurate but also fair and explainable. While AI models have shown promise in this domain, they face key limitations, including computational inefficiency, the interpretability challenges of spiking neural networks (SNNs), and the complexity and convergence instability of hyper-heuristic reinforcement learning (RL)-based hyperparameter optimization. To address these issues, we propose a novel framework that integrates a Cortical Spiking Network with Population Coding (CSNPC) and a Reinforcement-Guided Hyper-Heuristic Optimizer for Spiking Systems (RHOSS). The CSNPC, a biologically inspired SNN, employs population coding for robust classification, while RHOSS uses Q-learning to dynamically select low-level heuristics for hyperparameter optimization under fairness and recall constraints. Embedded within the Modular Supervisory Framework for Spiking Network Training and Interpretation (MoSSTI), the system incorporates explainable AI (XAI) techniques, specifically, saliency-based attribution and spike activity profiling, to increase transparency. Evaluated on the Bank Account Fraud (BAF) dataset suite, our model achieves a 90.8% recall at a strict 5% false positive rate (FPR), outperforming state-of-the-art spiking and non-spiking models while maintaining over 98% predictive equality across key demographic attributes. Although RHOSS introduces an offline search cost, this is amortized over deployment, where inference dominates. The architectural sparsity of CSNPC ensures lower energy per transaction compared to dense ANNs, as supported by neuromorphic benchmarks. The explainability module further confirms that saliency attributions align with spiking dynamics, validating interpretability. These results demonstrate the potential of combining population-coded SNNs with reinforcement-guided hyper-heuristics for fair, transparent, and high-performance fraud detection in real-world financial applications.