Architecting Intelligent Financial Infrastructure: Scalable Machine Learning Systems for Real-Time Data Engineering in FinTech Applications
The evolution of financial technology (FinTech) has precipitated a paradigm shift in how financial services are architected, delivered, and optimized. As real-time transactions, algorithmic trading, and personalized financial products grow in complexity and scale, FinTech enterprises must develop robust, intelligent infrastructures that enable real-time data engineering and scalable machine learning (ML). This paper explores the design and deployment of intelligent financial infrastructures by analyzing how scalable ML systems can be integrated within the data engineering fabric of modern FinTech applications. Through a systems-level investigation, we propose a reference framework for developing resilient, low-latency, and adaptive platforms capable of supporting the fluid demands of contemporary digital finance ecosystems.
Keywords: FinTech, Machine Learning, Infrastructure, Data Engineering
