, S. Bala and , T. Vijay and , K. Thirusangu (2025) CREATION OF MACHINE LEARNING-BASED FINANCIAL FRAUD DETECTION SYSTEMS TO ENHANCE THE SECURITY AND RELIABILITY OF DIGITAL FINANCIAL TRANSACTIONS. International Journal of Business, Law and Political Science, 2 (12). pp. 658-665. ISSN 3032-1298

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Abstract

Objective: This research proposes a novel machine learning-based framework for financial fraud detection that combines ensemble learning techniques with real-time transaction monitoring. Method: Our hybrid approach integrates Random Forest, Gradient Boosting, and Neural Network classifiers to achieve superior detection accuracy while minimizing false positives. Results: Experimental evaluation on real-world datasets demonstrates a fraud detection rate of 97.8% with a false positive rate of only 0.3%, significantly outperforming existing methods. The proposed system offers a scalable solution for enhancing the security and reliability of digital financial transactions. Novelty: The rapid digitization of financial services has created unprecedented opportunities for fraudulent activities, necessitating advanced detection mechanisms.

Item Type:	Article
Subjects:	A General Works > AI Indexes (General)
Depositing User:	admin eprints
Date Deposited:	07 May 2026 09:46
Last Modified:	07 May 2026 09:46
URI:	http://eprints.umsida.ac.id/id/eprint/16464

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