Real-Time Online Payment Fraud Detection using Machine Learning Algorithms in Financial Systems

ABSTRACT: This work offers a complete end-to-end machine learning solution for payment fraud detection in an online scenario with the incorporation of real-time execution using a web interface developed with Streamlit, exploratory data analysis, and the training of models. The study incorporates addressing class imbalance, a prevalent issue in fraud discovery scenarios, using a vast data set of over six million transaction entries. In preprocessing, standardization, one-hot encoding, and Synthetic Minority Over-sampling Technique (SMOTE) have been employed in order to increase the sensitivity of models. Several supervised learning models have been trained as well as tested, e.g., XGBoost, Random Forest, and Logistic Regression. XGBoost delivered higher precision as well as recall, while logistic regression using a class-weight balancing setup delivered 94% accuracy. The project's final product is an easy-to-use web application with real-time fraud prediction based on transactional inputs, which is thus a useful element in financial monitoring systems. It bridges the gap between model construction and deployment and underscores the potential of machine learning in detecting digital fraud. KEYWORDS: Online Payment, Fraud Detection, Machine Learning, XGBoost, SMOTE, Classification, Supervised Learning, Transaction Monitoring.