Reinforcement Learning in Credit Card Fraud Detection: The Power of Always Learning

Author(s)	Puneet Sharma
Country	United States
Abstract	Fraud detection systems are locked in an arms race against adversaries whose ingenuity knows no bounds. Traditional approaches—static rules, supervised models, and manual reviews—struggle to adapt to ever-evolving fraud tactics. Reinforcement Learning (RL), a paradigm rooted in reward-based optimization, revolutionizes the landscape by enabling systems that learn, evolve, and strategize in real time. Unlike conventional models constrained by historical data, RL thrives in uncertainty, exploring decision spaces with unparalleled agility. This paper delves into RL's application to credit card fraud detection, covering critical aspects such as policy optimization, reward engineering, state-space representation, and adversarial robustness. RL systems hold immense potential to autonomously decipher fraud patterns, adapt to emerging threats, and collaborate seamlessly across financial networks. With fraud losses projected to exceed $40 billion globally, RL's role is not merely a solution but a necessity.
Keywords	Reinforcement Learning, Credit Card Fraud Detection, Markov Decision Processes, Policy Optimization, Real-Time AI, Q-Learning, Neural Networks, Financial Cybersecurity, Behavioral Dynamics, Federated AI, Adversarial Training
Field	Engineering
Published In	Volume 3, Issue 2, March-April 2021
Published On	2021-03-11
DOI	https://doi.org/10.36948/ijfmr.2021.v03i02.23459
Short DOI	https://doi.org/g82h5z

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