Rift: a Real-Time Machine Learning Framework for Predicting Payment Gateway Transaction Failures

Payment gateway transaction declines remain among the most neglected friction points of e-commerce. They ruin your customer experience, charge you operational costs, and are mostly invisible due to the absence of predictive intelligence. This paper introduces a machine learning approach trained on a high-fidelity dataset with 6,000 transactions that was designed to emulate the failure behavior observed in practice. Our solution encodes other significant variables, fraud risk, transaction time, device type, and whether a previous transaction attempt failed, to obtain prediction accuracy more than 96 %. Ensemble models such as XGBoost and LightGBM not only inform us which transactions are likely to fail, but also why. This study provides the foundation for proactive, intelligent and lean payment systems.