PAYSECURE: Machine Learning-Based Online Fraud Detection

Abstract

Online payment fraud poses a significant threat to financial transactions, resulting in substantial economic losses. This project proposes a machine learning-based system to predict and detect fraudulent transactions using the Decision Tree Classifier algorithm. Online payment fraud involves unauthorized access and manipulation of financial transactions, including identity theft, phishing, card skimming, and transaction tampering. The Decision Tree Classifier algorithm trains on historical transaction data (fraudulent and non-fraudulent) to build a classifier model. This model predicts transactions as fraudulent or non-fraudulent based on feature extraction and splitting. The process begins with user registration, where customers provide their bank details in the Website, which are then securely stored in an SQL database. Next, transaction details are input into the system, allowing for real-time monitoring. A Decision Tree Classifier-based machine learning model is employed to predict potential fraud, analyzing the collected data to identify patterns and anomalies. The prediction results are then displayed on the website, alerting users to potential fraud or confirming legitimate transactions. To ensure timely notification, an Email API is integrated, sending alerts to users and administrators when suspicious activity is detected. This comprehensive system provides a robust defense against online payment fraud, safeguarding users' financial information and maintaining trust in e-commerce transactions.