CREATION OF MACHINE LEARNING-BASED FINANCIAL FRAUD DETECTION SYSTEMS TO ENHANCE THE SECURITY AND RELIABILITY OF DIGITAL FINANCIAL TRANSACTIONS

• S. Bala

  Department of Mathematics, S.I.V.E.T. College, Gowrivakkam, Chennai-73

• T. Vijay

  Department of Mathematics, S.I.V.E.T. College, Gowrivakkam, Chennai-73

• K. Thirusangu

  Department of Mathematics, S.I.V.E.T. College, Gowrivakkam, Chennai-73

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.

S. Begum et al., “AI-Driven Fraud Detection in Real-Time Financial Transactions: A Deep Learning Approach,” Well Test. J., vol. 34, no. S3, pp. 727–748, 2025.

S. Begum, “AI at Scale: Predictive Analytics as a Strategic Engine for National Competitiveness in U.S. Startup and Small Business Financing,” Int. J. Res. Publ. Rev., vol. 5, no. 12, pp. 6129–6137, 2024, doi: 10.55248/gengpi.6.1025.3664.

S. Begum, “Optimizing Capital Deployment in Post-Pandemic America: AI-Powered Predictive Analytics for Startup Resilience and Growth,” Int. J. Comput. Appl. Technol. Res., vol. 11, no. 12, pp. 700–710, 2022, doi: 10.7753/IJCATR1112.1030.

K. P. Mishu, M. T. Ahmed, M. M. U. A. M. S. Billah, M. D. H. Gazi, S. Begum, and M. M. Hasan, “AI-Driven Supply Chain Management in the United States: Machine Learning for Predictive Analytics and Business Decision-Making,” Cuest. Fisioter., vol. 53, no. 3, pp. 5755–5768, 2024, doi: 10.48047/s7cc5r20.

M. I. Jobiullah, S. Begum, J. Sarwar, V. Kumar, and A. B. Gupta, “Reimagining U.S. Cyber Defense Through Intelligent Automation,” Int. J. Sci. Res. Mod. Technol., vol. 3, no. 12, 2024, doi: 10.38124/ijsrmt.v3i12.1196.

S. Begum, “Artificial Intelligence and Economic Resilience: A Review of Predictive Financial Modelling for Post-Pandemic Recovery in the United States SME Sector,” Int. J. Innov. Sci. Res. Technol., vol. 10, no. 7, 2025, doi: 10.38124/ijisrt/25jul1726.

S. Begum et al., “Robotic AI Systems for Fake News Detection in IoT-Connected Social Media Platforms Using Sensor-Driven Cross-Verification,” J. Posthumanism, vol. 5, no. 11, pp. 391–405, 2025, doi: 10.63332/joph.v5i11.3688.

A. R. Talukder, F. Shahrear, S. Begum, and M. I. Jobiullah, “Underwater Image Enhancement and Restoration with YOLO-Based Object Detection and Recognition,” Well Test. J., vol. 34, no. S3, pp. 727–748, 2025.

S. Begum et al., “AttenGene: A Deep Learning Model for Gene Selection in PDAC Classification Using Autoencoder and Attention Mechanism for Precision Oncology,” Well Test. J., vol. 34, no. S3, pp. 705–726, 2025.

E. W. Ngai, Y. Hu, Y. H. Wong, Y. Chen, and X. Sun, “Application of Data Mining Techniques in Financial Fraud Detection,” Decis. Support Syst., vol. 50, no. 3, pp. 559–569, 2011, doi: 10.1016/j.dss.2010.08.006.

J. West and M. Bhattacharya, “Intelligent Financial Fraud Detection: A Comprehensive Review,” Comput. & Secur., vol. 57, pp. 47–66, 2016, doi: 10.1016/j.cose.2015.09.005.

Y. Sahin and E. Duman, “Detecting Credit Card Fraud by Decision Trees and Support Vector Machines,” Int. J. Inf. Electron. Eng., vol. 1, no. 4, pp. 315–319, 2011.

A. Dal Pozzolo, G. Boracchi, O. Caelen, C. Alippi, and G. Bontempi, “Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy,” IEEE Trans. Neural Networks Learn. Syst., vol. 29, no. 8, pp. 3784–3797, 2018, doi: 10.1109/TNNLS.2017.2736643.

T. Pourhabibi, K.-L. Ong, B.-J. Kam, and Y.-H. Boo, “Fraud Detection: A Systematic Literature Review of Graph-Based Anomaly Detection Approaches,” Decis. Support Syst., vol. 133, p. 113303, 2020, doi: 10.1016/j.dss.2020.113303.

S. Begum, “AI at Scale: Predictive Analytics as a Strategic Engine for National Competitiveness in US Startup and Small Business Financing,” Int. J. Progress. Res. Eng. Manag. Sci. Dev., p. 7421, 2025.

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