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All Journal International Journal of Business, Law and Political Science
Bala, S.
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Economics, Econometrics & Finance Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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CREATION OF MACHINE LEARNING-BASED FINANCIAL FRAUD DETECTION SYSTEMS TO ENHANCE THE SECURITY AND RELIABILITY OF DIGITAL FINANCIAL TRANSACTIONS Bala, S.; Vijay, T.; Thirusangu, K.
International Journal of Business, Law and Political Science Vol. 2 No. 12 (2025): International Journal of Business, Law and Political Science
Publisher : PT. Antis International Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61796/ijblps.v2i12.462

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.
Co-Authors Thirusangu, K. Vijay, T.