Online Payment Fraud Detection Model Using Machine Learning

The dependability and safety of UPI-based financial transactions are vital for maintaining public confidence, facilitating seamless digital payments, and ensuring economic stability. This paper introduces a software model aimed at identifying and predicting fraudulent behavior in UPI transactions through the use of advanced technologies, which encompass machine learning algorithms, real-time monitoring, and behavioral analysis. The Use of the software is to reduce financial risks by implementing a fraud assessment system that relies on historical transaction data and current information. By utilizing machine learning algorithms along with rule-based methods, the system is able to recognize abnormal transaction patterns, evaluate user behavior, and identify irregularities. The Prediction of model find the risk of transactions based on factors such as the amount, frequency, location, and behavioral patterns of users to highlight potentially suspicious activities. The suggested system functions by implementing four main phases: user engagement, analysis of historical data, continuous monitoring of real-time data, and fraud prediction analysis. This methodology produces comprehensive reports on the risks associated with transactions and offers proactive measures to identify and thwart fraudulent actions within UPI transactions