Real-Time Data Stream Analytics and Artificial Intelligence for Enhanced Fraud Detection and Transaction Monitoring in Banking Security (American Journal of Innovation in Science and Engineering, 2025)
Expanding rapidly as a result of the expansion in digital banking transactions, modern modes of financial fraud have grown more complex, and traditional rule-based systems of detecting fraud have proved inadequate because of high false-positive rates (normally 15-20 percent), slow response times (greater than 30 seconds), and unchanging detection signatures. It has been shown that this is a key weak point in all financial systems around the world, leading to multi-billion dollar losses every year, with the expeditious creation of dynamic detection frameworks that can occur in real-time being needed. We mitigated this basic challenge by developing and deploying the first end-to-end data analytics and ensemble AI models solution to produce streaming data analytics applications entirely optimized to detect fraud in high-velocity transaction spaces (produces >3,000 transactions/second). We tested seven machine learning architectures including new temporal convolutional network (TCN) and gradient-boosted LSTM hybrids by conducting extreme experimentation with synthetic (PaySim) and real-world (n=2.1 million transaction records) data. The optimized system demonstrated record performance levels: 98.7 percent in detection accuracy (p