Online Banking Fraud Detection Using Machine Learning Techniques

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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