ItemBook Chapter

AI in Financial Fraud Detection and Prevention

Title
AI in Financial Fraud Detection and Prevention
Creator
Haseena, Shaik Valli; Jasawani, Neha; Ayasha; Suresh, Gaikwad Beena; Suresh, Gaikwad Beena; Shanavas, Simna
Description
Fraud has always posed problems to financial institutions and with the rapid growth of digital transactions, and its complexity has increased beyond detection. Normal methods of fraud detection that depend on rules only are severely outdated and ineffective against newer types of schemes. It is now imperative to employ more sophisticated mechanisms for fraud detection considering the evolvement of financial crimes. The massive amounts of transnational data that need to be analyzed to detect fraudulent patterns can now be processed with medium to high levels of accuracy using AI with the help of machine learning, deep learning, and natural language processing (NLP), and fraud detection and prevention have been transformed for the better. Algorithms of machine learning like supervised, unsupervised, and reinforcement learning are central to the features of fraud detection. Suspicious transactions are detected during supervised learning by using already existing fraudulent data, whereas unsupervised learning detects all anomalies without any prior defined labels. Through real-time data input, reinforcement learning adjusts its detection methodologies. Deep learning models such as convolutional neural networks and recurrent neural networks identify and process fraud indicators hidden within messages or intricate datasets. Moreover, through intricate analysis of customer interactions, NLP techniques detect fraudulent activities by identifying phishing attempts and deceptive communications. The chapter touches upon the issues of implementing AI oriented fraud detection in realms like e-commerce and entertainment. Identifying fraud from e-commerce is complicated by factors like high volume of transactions, false positives, privacy issues, and the endless frameworks of fraud. Finally, the chapter provides a summary of the main insights and makes recommendations for further investigation like incorporating blockchain, federated learning, and higher explainability to bolster AI powered fraud detection systems. 2026 Scrivener Publishing LLC.
Source
Designing Inclusive Classrooms: Integrating Emerging Technologies for Equity and Social Justice;pp.295-327
Date
01-01-2026
Publisher
wiley
Subject
anomaly detection; Artificial intelligence (AI); behavioral biometrics; deep learning; fraud detection; machine learning; NLP; predictive analytics
Coverage
Haseena S.V., Christ University, Bengaluru, India, Department of Computer Applications, Presidency College, Bengaluru, India; Jasawani N., Department of Computer Applications, Presidency College, Bengaluru, India; Ayasha, Department of Computer Science, Christ College of Science and Management, Bengaluru, India; Suresh G.B., Department of Computer Applications, Presidency College, Bengaluru, India; Suresh G.B., Department of Computer Applications, CMR University, Presidency College, Bengaluru, India; Shanavas S., Department of Computer Applications, Presidency College, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-139438922-3; 978-139438919-3;
Format
online
Language
English
Type
Book chapter

Citation

Haseena, Shaik Valli; Jasawani, Neha; Ayasha; Suresh, Gaikwad Beena; Suresh, Gaikwad Beena; Shanavas, Simna, “AI in Financial Fraud Detection and Prevention,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 20, 2026, https://archives.christuniversity.in/items/show/23948.

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