Online Payment Fraud Detection Model Using Machine Learning
1Shivam Shinde, 2Bhuvnesh Rane, 3Shravani Jadhav , 4Ruchita Ugalmugle,5Aishwarya Sanap
1,2,3,4 Student, Department of Information Technology ,MAP College , Nashik.
5 Lecturer , Department of Information Technology , MAP College , Nashik .
6 Mahesh P. Bhandakkar , HOD , Deaprtment of Information Technology ,MAP College , Nashik.
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1. Abstract
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
Key Words UPI Transactions, Fraud Detection, Machine Learning, Real-Time Monitoring, Behavioral Analysis, Risk Assessment,
