Real-time fraud detection with reinforcement learning: An adaptive approach

Detecting financial transaction fraud in real-time presents extensive difficulty because fraud patterns continue evolving and concept drift occurs. Adapting traditional rule-based and supervised learning remains difficult because new techniques are needed to support dynamic decision-making processes. This investigation presents reinforcement learning (RL) as an autonomous answer to handle real-time fraud discovery by removing the need for market information. The research investigates Markov Decision Processes (MDPs), Deep Q-networks (DQNs), and Actor-Critic methods through an assessment of their ability to modify fraud detection policies automatically. Objecting systems based on reinforcement learning perform better than regular models when evaluating fraudulent behavior while developing lower error rates. The application of simulation environments and authentic financial information helps prove this superiority. The research reveals that a combination of RL with federated learning techniques and explainable AI and adversarial training can improve the potential of fraud detection abilities. The new fraud prevention technology delivers scalable and robust solutions offering financial institutions privacy protection. The article sets future research aims to make RL algorithms more efficient while overcoming technical obstacles in financial fraud detection systems.