Volume 10 | Issue 1 | March 2025

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 Abstract

Financial fraud has become a growing threat in the modern digital economy, causing substantial financial losses and eroding trust in financial systems. Fraudulent activities, including credit card fraud, money laundering, and insurance fraud, have become more sophisticated, making traditional rule-based fraud detection methods increasingly ineffective. Machine learning (ML) techniques have emerged as a powerful tool to combat financial fraud by detecting patterns, anomalies, and suspicious transactions in real-time. This paper provides a comprehensive review of ML techniques used in financial fraud detection, including supervised learning (decision trees, random forests, neural networks), unsupervised learning (clustering, anomaly detection), and hybrid models. The study discusses key challenges such as data imbalance, adversarial fraud tactics, explainability, and computational efficiency. Additionally, recent advancements such as federated learning, blockchain-based fraud detection, and deep learning innovations are explored. The review highlights the advantages of ML-based fraud detection systems over conventional approaches and outlines potential future research directions to improve fraud detection accuracy, real-time processing, and regulatory compliance.

 Keywords

Financial Fraud, Machine Learning, Supervised Learning, Unsupervised Learning, Anomaly Detection, Fraud Prevention

 Authors and Affiliations

Ms. Prabha Yadav
M.Tech. Scholar, Department of Computer Science & Engineering, IMEC, Sagar, M.P, India
Mr. Sarvesh Singh Rai
Assistant Professor, Department of Computer Science & Engineering, IMEC, Sagar, M.P, India

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Creative Commons Attribution 4.0 and The Open Definition

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