Article
UPI FRAUD DETECTION USING MACHINE LEARNING FOR SECURE DIGITAL PAYMENTS
The rapid adoption of Unified Payments Interface (UPI) systems has revolutionized digital transactions by enabling instant, secure, and convenient money transfers. However, the exponential growth of digital payments has also led to a significant rise in fraudulent activities, posing serious threats to financial security. Traditional fraud detection mechanisms rely on manual verification or rule-based systems, which are often inefficient and incapable of identifying sophisticated fraud patterns in real time. This research presents a Machine Learning-based UPI Fraud Detection System designed to identify fraudulent transactions efficiently and accurately before completion. The proposed system analyzes transaction data such as transaction amount, time, location, account behavior, and device information to detect anomalies. By leveraging machine learning algorithms, the system learns patterns from historical transaction data and classifies transactions as either normal or fraudulent. The system is implemented using Python, with Flask for web application development and SQLite for database management. The real-time processing capability ensures that fraudulent transactions are detected instantly, minimizing financial losses. The architecture of the system consists of data collection, preprocessing, feature extraction, model training, and prediction modules. The system continuously monitors transaction behavior and identifies suspicious patterns using trained models. Upon detection of a fraudulent transaction, the system generates alerts and stores transaction details in the database for further analysis. Experimental results demonstrate that the system achieves high accuracy and low error rates, making it suitable for real-world applications. The system was tested with multiple transaction datasets and achieved an accuracy of 94% with minimal response time. The proposed approach significantly enhances transaction security and reduces the dependency on manual monitoring. This research contributes to the development of intelligent fraud detection systems by integrating machine learning techniques into digital payment platforms. Future work includes the incorporation of deep learning models, realtime cloud deployment, and advanced behavioral analytics to further improve detection accuracy and scalability. Overall, the system provides a robust and efficient solution for secure digital transactions.
Full Text Attachment
