Real-Time Fraud Detection Using Reinforcement Learning with Dynamic Feature Selection

Real-time fraud detection systems require sophisticated approaches capable of adapting to evolving fraud patterns while maintaining high accuracy and minimal false positive rates under strict latency constraints. Traditional fraud detection methods rely on static feature sets and rule-based systems that cannot adapt effectively to new fraud techniques or changing transaction patterns. The challenge lies in developing systems that can continuously learn optimal feature selection strategies while processing high-volume transaction streams in real-time environments where detection decisions must be made within milliseconds. This study proposes a novel Dynamic Feature Selection Reinforcement Learning (DFS-RL) framework that integrates reinforcement learning algorithms with adaptive feature selection mechanisms to enable real-time fraud detection with continuously evolving feature importance patterns. The framework employs Deep Q-Network (DQN) agents to learn optimal feature selection policies while utilizing ensemble detection models that adapt to selected feature subsets dynamically. The integrated approach enables real-time processing of transaction streams while maintaining detection accuracy through intelligent feature adaptation that responds to changing fraud patterns and system performance feedback. Experimental evaluation using large-scale financial transaction datasets demonstrates that the proposed framework achieves 43% improvement in fraud detection accuracy compared to traditional static feature approaches. The DFS-RL method results in 37% reduction in false positive rates while maintaining average transaction processing latency under 50 milliseconds for real-time requirements. The framework successfully combines adaptive feature selection with high-performance fraud detection, achieving 41% better adaptation to new fraud patterns while supporting real-time transaction processing at scale.