Optimizing Regulatory Compliance Checking With Ai: A Methodological Exploration

Regulatory Technology (RegTech) has emerged as a transformative field aimed at enhancing the efficiency and precision of regulatory compliance processes across industries. Originating from the financial sector, it leverages advanced technologies to address challenges in monitoring, reporting, and compliance. This paper explores state-of-the-art AI-based methodologies for compliance checking and proposes a comprehensive Compliance Management Framework (CMF) that integrates tasks such as compliance modeling, mapping, enactment, and assessment. Central to this study is the introduction of a unified Regulatory Compliance Checking Workflow, comprising four key stages: rule interpretation, rule mapping, rule execution, and compliance reporting. Emphasizing the complexity of regulatory compliance, the study identifies critical challenges, including computational infeasibility, semantic variability, and the dynamic nature of regulations. State-of-the-art compliance approaches are categorized into logic-based, NLP, ML, and DL techniques, with their application demonstrated in real-world contexts such as GDPR compliance and Building Information Modeling (BIM). The findings highlight the role of advanced technologies in improving compliance efficiency, accuracy, and cost-effectiveness, while identifying remaining challenges and opportunities for innovation. Methodologically, the proposed unified Regulatory Compliance Checking Workflow offers an AI-driven, scalable compliance pipeline integrating rule interpretation, mapping, execution, and reporting via logic-based reasoning, NLP, ML, and DL. The proposed Compliance Management Framework (CMF) introduces a unified compliance perspective by integrating compliance modeling, mapping, execution, assessment, and reporting within a continuous operational lifecycle. This integration enables a process-centric view that connects AI-based techniques with practical compliance operations, thereby extending existing RegTech survey studies toward a more structured methodological framework.