Intelligent AI Agents for Fraud and Abuse Detection: Leveraging Machine Learning, NLP, and Behavioural Analytics for Enhanced Security

International Journal of Computer Science and Engineering

© 2025 by SSRG - IJCSE Journal

Volume 12 Issue 4

Year of Publication : 2025

Authors : Anirban Majumder

10.14445/23488387/IJCSE-V12I4P103

How to Cite?

Anirban Majumder, "Intelligent AI Agents for Fraud and Abuse Detection: Leveraging Machine Learning, NLP, and Behavioural Analytics for Enhanced Security," SSRG International Journal of Computer Science and Engineering , vol. 12,  no. 4, pp. 17-22, 2025. Crossref, https://doi.org/10.14445/23488387/IJCSE-V12I4P103

Abstract:

Fraud and abuse in financial transactions, healthcare claims, and digital interactions pose significant challenges to organizations worldwide. The conventional rule-based detection approaches are often limited in adapting to evolving fraudulent tactics. This paper explores the development of intelligent AI agents for fraud and abuse detection, leveraging Machine Learning (ML), Natural Language Processing (NLP), and Behavioural Analytics to enhance security and risk mitigation. To generate models, the AI solution incorporates supervised and unsupervised Machine learning models for finding deviations through anomaly detection, NLP approaches for text-based fraud identification and behavioural analytics to permit recognition of deviations in user activity. Leveraging these state-of-the-art approaches helps the system to enable real-time detection and prevention of fraudulent activities, enhancing accuracy and reducing false positives.
       This approach has effectively identified sophisticated instances of fraud across various industries , including financial services, healthcare, and e-commerce. Furthermore, AI-driven fraud detection significantly improves operational efficiency, lowers losses, and enhances cybersecurity protections.

Keywords:

Fraud detection, Artificial intelligence, Machine learning, Natural language processing, Behavioural analytics, Cybersecurity.

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