Transforming Financial Intelligence: How AI Reshapes Document Analysis and Compliance Workflows

Financial intelligence functions operate under complex regulatory frameworks, demanding substantial resources for Anti-Money Laundering (AML) and Know-Your-Customer (KYC) compliance. Traditional document review processes rely heavily on manual effort and rule-based systems, resulting in scalability and accuracy limitations, particularly given the predominance of unstructured data. This study examines the integration of artificial intelligence (AI) techniques—such as natural language processing (NLP), machine learning, and deep learning—in automating financial document analysis and compliance workflows. The focus includes document classification, named entity recognition, and content summarization, which enhance operational efficiency and regulatory adherence. Empirical evidence indicates that AI-driven solutions reduce processing time, improve data extraction accuracy, and enable continuous transaction monitoring for fraud detection and risk assessment. Challenges related to model interpretability, data privacy, and legacy system integration remain critical considerations. The research contributes a framework for deploying explainable, scalable AI architectures to support financial intelligence and compliance operations, with implications for regulatory reporting, audit readiness, and institutional risk management.