Current Volume 9
Abstract
The rapid rise in online banking fraud—encompassing phishing, account takeover, and unauthorized transactions—poses critical financial threats to both institutions and customers, while traditional rule-based detection systems suffer from high false positive rates, inability to adapt to evolving fraud patterns such as small-value test transactions and location spoofing, and delayed batch processing that permits fraudulent withdrawals before intervention. This paper proposes a hybrid machine learning-based fraud detection system that integrates Random Forest classification with real-time behavioral analytics to evaluate each transaction under 500 milliseconds. By analyzing multiple risk indicators including transaction amount deviation from user spending norms, unusual login locations, rapid successive transfers (velocity checks), device fingerprint mismatches, and time-based anomalies, the system generates a dynamic fraud score (0–100) triggering automated approval, OTP verification, admin review, or blocking. The solution encompasses Admin and User dashboards, a Transaction module for metadata capture, an Alert & Notification module for real-time SMS/email alerts, and an Analytics & Reporting module for fraud trend visualization. Continuous learning from new transaction patterns significantly reduces false positives while detecting sophisticated real-time fraud attacks, making the system highly effective for securing modern online banking platforms.
Keywords
Online Banking Fraud Detection, Random Forest Classification, Behavioral Analytics, Real-Time Transaction Monitoring, Dynamic Fraud Scoring, Adaptive Thresholding
Citations
IRE Journals:
M. Barath Kesavan, T. Manoj, M. Bhuvanesh Kumar, M. Prem Kumar "Online Banking Fraud Detection" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 2248-2255 https://doi.org/10.64388/IREV9I11-1717890
IEEE:
M. Barath Kesavan, T. Manoj, M. Bhuvanesh Kumar, M. Prem Kumar
"Online Banking Fraud Detection" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1717890
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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