The rapid expansion of financial technologies, increasing complexity of regulatory environments, and continuous evolution of compliance requirements have created significant challenges for financial institutions worldwide. Traditional regulatory compliance systems rely heavily on manual review processes, rule-based engines, and human expertise, which are no longer sufficient to handle the growing volume, diversity, and dynamism of financial regulations. In this context, Regulatory Technology (RegTech) has emerged as a transformative approach aimed at automating and enhancing compliance processes through advanced digital tools. Among these technologies, Natural Language Processing (NLP) plays a central role in enabling machines to interpret, analyze, and process large volumes of unstructured regulatory texts.This study proposes an intelligent RegTech framework based on NLP to automate financial regulatory compliance. The proposed framework integrates semantic text analysis, legal document classification, and policy matching mechanisms to identify inconsistencies between regulatory requirements and organizational policies. By leveraging advanced language models and transformer-based architectures, the system is capable of extracting key regulatory concepts, detecting compliance gaps, and providing automated alignment suggestions.





The research methodology is based on the analysis of regulatory documents issued by central banking authorities, international financial standards, and compliance guidelines. The results indicate that the proposed NLP-based RegTech framework significantly improves the speed and accuracy of regulatory interpretation compared to traditional compliance methods. Furthermore, the system reduces human workload, minimizes interpretation errors, and enhances the consistency of compliance decisions across financial institutions.The findings suggest that intelligent RegTech systems can play a critical role in strengthening financial governance, reducing regulatory risk, and improving operational efficiency. This study contributes to the growing literature on AI-driven compliance systems by presenting a structured and scalable framework for automated regulatory analysis in the financial sector.