Volume 14 | Issue 6

 Abstract

The rapid growth of digital payment systems and online financial transactions has increased the risk of fraudulent activities, resulting in significant financial losses for customers, businesses, and financial institutions. Detecting fraudulent transactions in real time has become a major challenge due to the large volume of online payments processed every day. This project presents an intelligent Fraud Detection in Online Payment System using Machine Learning techniques to identify and prevent fraudulent transactions effectively. The system utilizes historical transaction datasets containing attributes such as transaction ID, transaction time, transaction amount, account balance details, and other relevant features to analyze transaction behavior and identify suspicious patterns. Data preprocessing techniques including data cleaning, feature selection, normalization, and dataset transformation are applied to improve data quality and model performance. The Logistic Regression algorithm is employed as the core classification technique due to its simplicity, efficiency, and suitability for binary classification problems involving fraudulent and genuine transactions. The developed system is integrated with a web-based application that enables real-time fraud detection. Whenever a new transaction is initiated, the system analyzes the transaction details using the trained machine learning model and predicts whether the transaction is fraudulent or genuine based on learned patterns from historical data. The prediction result is instantly displayed through the web interface, allowing administrators and users to take immediate action when suspicious activities are detected. This approach improves fraud detection accuracy, reduces manual monitoring efforts, minimizes financial risks, and provides a scalable and cost-effective solution for enhancing the security of online payment systems.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fraud detection in online payment, scam online payment, fraud transaction

  License

Creative Commons Attribution 4.0 and The Open Definition