How Reinforcement Learning Keeps Fraud Detection Smart and Quick: Adapting to New Fraud Tricks

Author(s)	Puneet Sharma
Country	United States
Abstract	The growing sophistication of fraudulent activities challenges traditional fraud detection systems that rely on static rules and historical data. Fraudsters continuously evolve their techniques, necessitating smarter, real-time solutions capable of learning and adapting. Reinforcement Learning (RL), a branch of machine learning, has emerged as a game-changing approach for detection of fraud. RL systems continually optimize detection strategies through trial-and-error learning, adapting to new fraud patterns as they emerge. This paper explores how RL keeps fraud detection smart and efficient by enabling adaptive decision-making, real-time anomaly identification, and proactive fraud prevention. It highlights RL’s ability to handle evolving fraud schemes, optimize detection accuracy, and improve response times across industries like healthcare, banking, and e-commerce. The paper further addresses challenges such as limited fraud data and computational complexity and discusses innovations that will shape RL’s future role in fraud prevention.
Keywords	Reinforcement Learning, Fraud Detection, Adaptive Learning, Anomaly Detection, Real-Time Analytics, Machine Learning, Digital Transformation, Fraud Prevention
Field	Engineering
Published In	Volume 6, Issue 2, March-April 2024
Published On	2024-03-05
DOI	https://doi.org/10.36948/ijfmr.2024.v06i02.35255
Short DOI	https://doi.org/g82hq3

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