Chapter 15: Reinforcement Learning in Fraud Detection: A Next-Generation Banking Defense

With digital transactions on the rise, fraud continues to increase almost unabated. These numbers require more advanced techniques for fraud detection. Existing detection techniques are all plagued with higher rates of false positives, low adaptability, and slow response times. This chapter investigates whether reinforcement learning (RL) could be used as an entirely new framework for redefining discovery and monitoring in the context of fraud detection through dynamic decision-making in real-time environments. An RL-based framework is designed to continuously learn and adapt over different evolving fraud patterns, minimizing false positives while boosting the accuracy of abnormal transaction detection. Components include state-and-action representations, reward function optimization, and near-real-time updates of policies. The model was benchmarked against traditional methods in terms of precision, recall, F1-score, and efficiency. The results show increased adaptability, fewer false alarms, and enhanced scalability of RL that made it highly viable in fraud prevention. However, implementation challenges entail regulatory compliance, model explainability, and computationally intensive implementations. This chapter therefore will leverage RL as an important contribution that shall trigger financial security efforts while pooling in the elements that will resolve some of the challenges raised within the industrial scenario.