UPI Online Transaction Fraud Detection

The proliferation of Unified Payments Interface (UPI) transactions has transformed the landscape of digital payments in India, offering unparalleled convenience and efficiency for instant money transfers. However, this surge in adoption has been accompanied by a significant rise in fraudulent activities, posing critical security challenges to users and financial institutions. This paper presents an intelligent fraud detection framework leveraging machine learning methods for detecting and topping fraudulent UPI transactions in real time. By analyzing key transaction features such as transaction type, amount, and account balances, a Decision Tree Classifier is trained to distinguish between legitimate and suspicious activities. The proposed system is implemented as a web-based platform, providing distinct functionalities for both users and administrators, including secure user registration, real-time fraud assessment, FAQ management, and feedback mechanisms. Results from experiments show how well the machine learning model works to identify fraudulent transactions, improving the security and dependability of UPI-based financial systems. This approach offers a scalable and automated solution to safeguard digital payment ecosystems against evolving fraud threats, fostering greater trust and adoption among users.