Online Banking Fraud Detection Using Machine Learning Techniques

• Author(s):

  Nagarajan V | Nandakumaran M | Naveen Kumar C | Mrs. S. Ramalakshmi

• Keywords:

  Fraud Detection, Machine Learning, Online Banking, Cyber Security, Data Mining

• Abstract:

  The Rapid Growth Of Online Banking Has Significantly Increased Fraudulent Activities Such As Unauthorized Transactions, Phishing Attacks, Identity Theft, And Account Takeovers. Traditional Rule-based Systems Fail To Detect Evolving Fraud Patterns. This Paper Proposes A Machine Learning-based Fraud Detection System That Analyzes Transaction Behavior And Identifies Anomalies In Real Time. The System Employs Logistic Regression, Decision Tree, Random Forest, And K-Nearest Neighbors Algorithms, Achieving Up To 95% Detection Accuracy. Results Demonstrate Significant Improvement In Accuracy, Reduction In False Positives, And Enhanced Banking Security Compared To Conventional Approaches.

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Other Details

• Paper id:

  IJSARTV12I3104806

• Published in:

  Volume: 12 Issue: 3 March 2026

• Publication Date:

  2026-03-29

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