Fraud Shield-Payment Protection using Machine Learning

Abstract

Online payment fraud has been become a significant concern in financial sector, posing challenges for real-time detection and mitigation. This study gives us a machine learning-based fraud detection system designed for identifying fraudulent transactions both before and after their execution. A large transactional dataset is processed and filtered to focus on high-risk transaction types. A Random Forest classifier is implemented for fraud detection due to its robustness and high accuracy in handling imbalanced financial data using standard evaluation metrics. The proposed approach gives high accuracy, precision, and recall, particularly with ensemble models, indicating its effectiveness in enhancing fraud detection systems. The research contributes a deployed, user-interactive solution in Streamlit web interface.

Keywords: machine learning, Random Forest, Online Payment Fraud, Fraud Detection, Real-time Prediction, Streamlit Interface, Financial Security, Imbalanced Dataset, Transaction Monitoring, Ensemble Models.