A Multi-Context AI Framework for Real-Time Detection and Prevention of Transaction Fraud

Abstract

The research introduces a Multi-Context AI Framework that is capable of identifying and preventing transaction fraud in the financial sector in real-time, even where the fraud is evolving quickly. The traditional methods of detecting fraud through rules and using machine learning are hard as the context gets changed rapidly to detect new frauds. The framework is able to combine behavioral context, temporal context, geospatial cues, device intelligence, and transactional risk indicators to create a unified fraud score. It uses adaptive learning, drift-aware updating, and a hybrid ensemble which consists of deep learning and tree-based models to constantly improve the predictions. The framework is equipped with a streaming architecture that allows for immediate anomaly flagging with extremely low latency, which makes it ideal for large-scale digital payment ecosystems. The results of the experiments show that the new method is more accurate, resilient against changing fraud patterns, and has a much lower rate of false positives than the traditional methods of fraud detection. The framework thus provides a modern, real-time, scalable, and smart solution for financial security systems.