AI-DRIVEN COMPLIANCE SYSTEMS IN MORTGAGE BANKING: TECHNICAL ARCHITECTURE AND PERFORMANCE ANALYSIS

Abstract

Recent developments in artificial intelligence have transformed regulatory compliance within the mortgage sector, shifting traditional manual methods to advanced automated solutions. This article explores the technological advancements and the rollout of AI-based compliance tools, emphasizing their incorporation into current mortgage functions and their effects on regulatory compliance. By analyzing machine learning techniques and natural language processing uses, this article illustrates how AI systems can effectively understand and implement intricate regulations like TRID, RESPA, ECOA, and HMDA throughout lending activities. It presents structural frameworks for AI compliance systems, assesses their performance indicators, and investigates new trends in predictive analytics for managing risk. This article is backed by industry case studies, showing that AI-enhanced compliance platforms markedly decrease human error while increasing audit precision and operational productivity. Based on recent implementations at prominent financial organizations, this article offers strategic recommendations for lenders embracing AI compliance technologies within a growingly regulated landscape. This article uptakes regulatory technology and expands the theoretical model (2024) about AI integration into financial compliance systems.