Architecting Intelligent Financial Infrastructure: Scalable Machine Learning Systems for Real-Time Data Engineering in FinTech Applications

Abstract

The increasing complexity and velocity of financial data in modern FinTech ecosystems necessitate a shift toward intelligent, scalable, and real-time infrastructures. This study proposes an integrated architecture that combines scalable machine learning systems with real-time data engineering to enable adaptive and high-throughput FinTech applications. Leveraging microservices, distributed processing frameworks, and MLOps practices, the architecture is designed to support diverse use-cases such as fraud detection, high-frequency trading signal prediction, and personalized credit risk profiling. Performance benchmarks demonstrate that the system can sustain over 100,000 transactions per second under peak load, while maintaining sub-50 millisecond latency across streaming data pipelines. Machine learning models achieved high predictive accuracy (AUC up to 0.97 and RMSE as low as 0.028), validated through rigorous statistical analyses including PCA, VIF, t-tests, and ANOVA. Real-time stream processing engines ensured timely and accurate data transformation with >97% window completeness. The integration of MLOps further enhanced model lifecycle management and deployment automation. Overall, this study offers a robust, scalable, and intelligent framework for powering next-generation FinTech platforms capable of delivering real-time, data-driven financial intelligence.