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Real-time fraud detection with reinforcement learning: An adaptive approach
University of Hull, Uk.
Research Article
International Journal of Science and Research Archive, 2022, 06(02), 126-136.
Article DOI: 10.30574/ijsra.2022.6.2.0068
Publication history:
Received on 07 March 2022; revised on 22 August 2022; accepted on 24 August 2022
Abstract:
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.
Keywords:
Real-Time Fraud Detection; Reinforcement Learning (RL); Adaptive Fraud Detection; Financial Fraud Prevention; Markov Decision Processes (MDPs); Deep Q-Networks (DQNs); Actor-Critic Methods; Concept Drift
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Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
