Machine Learning for Fraud Detection and Financial Crimes

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Author(s): Manthan S. Manavadaria (Chandubhai S. Patel Institue of Technology, Charotar University of Science and Technology Gate, India), Rohit Kumar Sharma (MIT World Peace University, India), Anuradha Parasar (Galgotias University, India), Amit Chauhan (Parul Institute of Applied Sciences, Parul University, India), N. Jeyaprakash (St. Joseph's College of Engineering, India), A. Mohaideen (Anna University, Chennai, India)and V. Bhoopathy (Sree Rama Engineering College, India)
Copyright: 2025
Pages: 24
Source title: Forensic Intelligence and Deep Learning Solutions in Crime Investigation
Source Author(s)/Editor(s): Christian Kaunert (Dublin City University, Ireland), Anjali Raghav (Sharda University, India), Kamalesh Ravesangar (Tunku Abdul Rahman University of Management and Technology, Malaysia)and Bhupinder Singh (Sharda University, India)
DOI: 10.4018/979-8-3693-9405-2.ch010

Keywords: Criminal Science and Forensics / Digital Crime & Forensics / Information Science Reference / Security & Forensics

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Abstract

Personal, commercial, and economic impacts of financial fraud are significant. Traditional rule-based fraud detection systems often generate false positives and struggle to adapt to new fraud methods. ML algorithms are examined as an alternative to traditional financial fraud detection methods in this chapter. It explores how supervised, unsupervised, and hybrid models may identify financial data abnormalities and fraud patterns. Money laundering, payments fraud, and other financial crimes are covered in this chapter. Fraud detection is complicated by data imbalance, privacy concerns, and model scalability. It also shows how AI, blockchain, and predictive analytics will fight fraud and secure financial institutions. This chapter reviews how new technology is changing financial crime prevention, using examples from credit card and online retail fraud.

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