DriftShield: Autonomous Fraud Detection via Actor-Critic Reinforcement Learning With Dynamic Feature Reweighting

Abstract

Financial fraud detection systems confront the persistent challenge of concept drift, where fraudulent patterns evolve continuously to evade detection mechanisms. Traditional rule-based methods and static machine learning models require frequent manual updates, failing to autonomously adapt to emerging fraud strategies. This article presents DriftShield, a novel adaptive fraud detection framework that addresses these limitations through four key technical innovations: (1) the first application of Soft Actor-Critic (SAC) reinforcement learning with continuous action spaces to fraud detection, enabling simultaneous fine-grained optimization of detection thresholds and feature importance weights; (2) a dynamic feature reweighting mechanism that automatically adapts to evolving fraud patterns while providing interpretable insights into changing fraud strategies; (3) an adaptive experience replay buffer combining sliding windows with prioritized sampling to balance catastrophic forgetting prevention with rapid concept drift adaptation; and (4) an entropy-driven exploration framework with automatic temperature tuning that intelligently balances exploitation of known fraud patterns with discovery of emerging threats. Experimental evaluation demonstrates that DriftShield achieves 18% higher fraud detection rates while maintaining lower false positive rates compared to static models. The system demonstrates 57% faster adaptation times, recovering optimal performance within 280 transactions after significant concept drift compared to 650 transactions for the next-best reinforcement learning approach. DriftShield attains a cumulative detection rate of 0.849, representing a 7.7% improvement over existing methods and establishing the efficacy of continuous-action reinforcement learning for autonomous adaptation in dynamic adversarial environments.