A Smart AI Framework for BFSI Fraud Prediction

The exponential growth of digital payment infrastructures has intensified the complexity and frequency of financial fraud, necessitating intelligent and automated detection mechanisms. This project proposes a smart machine learning–based fraud detection framework for real-time monitoring of Unified Payments Interface (UPI) transactions within the BFSI sector. A heterogeneous dataset containing both legitimate and fraudulent transactions is utilized for advanced feature engineering, normalization, and dimensionality reduction to extract high-impact discriminative features. The model is trained using historical transaction data and evaluated using standard performance metrics such as precision, recall, F1-score, ROC-AUC, and accuracy. Key attributes including transaction amount, temporal behavior, payer–payee relationships, geospatial indicators, and device fingerprints are incorporated, with higher emphasis on time-series features to capture sequential fraud patterns. The optimized hybrid model is integrated into the UPI framework for real-time inference and transaction scoring. An automated alert and response mechanism is deployed to enable prompt intervention, risk mitigation, and effective fraud containment.