Transforming Banking with AI-Driven Data Engineering: Real-World Applications and Industry Impact

Abstract

The banking sector is being transformed by artificial intelligence in combination with modern data engineering practices, which enable better customer experience, effective risk management, and more efficient operations. The use of lakehouse architectures, real-time streaming technologies, and embedded machine learning workflows allows for the use of important applications such as fraud detection and prevention, credit risk assessment models, personalized financial services, and automated compliance reporting, which are used in everyday banking scenarios. AI predictive models can process real-time streamed transaction data to identify and label fraud with unprecedented accuracy; meanwhile, alternate data and feature engineering techniques are redefining how credit underwriters assess applicants' risk. Personalized recommender systems and dynamic customer segmentation can yield better retention and cross-sales by leveraging intelligent platforms in digital banking. Automated compliance systems powered by generative AI can limit the manual reporting effort while reducing the number of human errors in the same reporting. Adoption of AI with modern data engineering can be inhibited by issues such as data privacy, system integrations, and the need for transparent and fair algorithmic decisions. The growth of AI-augmented data engineering solutions across leading financial institutions has delivered measurable business benefits for many, while providing a competitive advantage in reducing fraud, improving loan turnaround times, and increasing customer satisfaction and loyalty. The convergence of artificial intelligence and modern data engineering presents an opportunity for banks to establish future-proof digital, client-facing capabilities as part of a coordinated strategic direction and a sustainable competitive advantage.