AI-Driven Financial Fraud Detection System in Savings Account Using Rule-Based Logic and Random Forest Algorithm

The threats of fraud in savings accounts are increasingly escalating in recent years due to the rise of digital banking, prompting the need for advanced security measures. This research addresses this challenge by developing a hybrid fraud detection system which combines rule-based logic with machine learning. This system aims to detect unauthorized transactions in real-time while minimizing re-occurrences and ensuring rapid response to emerging threats with key features suh as transaction pattern analysis and anomaly. The methodology used leverages Python for backend development, Scikit-learn for Machine Learning (ML) models, PHP (Hypertext-Preprocessor) for both frontend and also server-side scripting to create an interactive dashboard, and MySQL for database management to store and retrieve transaction data efficiently. Over 40,000 transactions was processed with 5% labeled as fraudulent and test results metrics with 0.8819 accuracy, 0.8805 precision, 0.9633 recall, 0.8810 ROC-AUC, 0.8518 PR-AUC and 0.9203 F1-Score. The high accuracy and recall suggest the hybrid approach effectively detects fraud in savings accounts. This work contributes to the field of financial cybersecurity by bridging the gap between static rule-based systems and adaptive machine learning approaches, offering a robust framework for safeguarding savings accounts against evolving fraudulent activities.