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INTERNATIONAL JOURNAL OF GLOBAL RESEARCH INNOVATIONS & TECHNOLOGY (IJGRIT) [ Vol. 4 | No. 2(II) | April - June, 2026 ]

Machine Learning in Financial Fraud Detection: Analyzing Data and Measuring Business Outcomes

Prof Tirth N Patel & Dr. Naresh Patel

Digital fraud poses a growing threat to the fintech and banking industries, with global payment card fraud losses reaching $338 billion in 2023 (globenewswire.com). Machine learning plays a vital role in identifying fraudulent transactions in real time, offering both financial protection and operational efficiency. This paper examines logistic regression and random forest models on a benchmark credit card fraud dataset to evaluate their technical effectiveness and business benefits. We achieve high predictive performance (e.g., ROC AUC ≈ 0.95, precision ≈ 0.84, recall ≈ 0.82 for our random forest model). The study highlights how ML-based fraud detection can significantly cut financial losses and operational costs. By transforming raw transaction data into strategic risk scores, these models enable data-driven decision-making on customer monitoring, resource allocation, and compliance. The study shows that properly designed ML fraud prevention systems can swiftly justify their expense by reducing fraud and enhancing operational efficiency. itexus.com.

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