This review highlights the adoption of automation technologies for compliance monitoring and risk assessment in financial institutions, indicating future research directions.
The financial sector is under increased pressure, conforming to a growing number of complex, dynamic regulations during the accomplishment of myriad risks ranging from credit and operational risk to market or compliance threats. Contrarily, transaction volumes are quite high, data flows too fast in real time, and regulatory environments too complex for the traditional manual processes to perform effectively in compliance monitoring and risk assessment. As such, financial institutions are now avidly embracing advanced automation technologies-an example includes AI, ML, RPA, NLP, and blockchain-to make compliance and risk-related functions more efficient, accurate, and scalable. This review intended to explore automation for compliance monitoring and risk assessment in the financial industry, along with the evolution, effectiveness, and challenges in RegTech and SupTech. It discusses model interpretability, ethics considerations, and regulatory acceptance while highlighting gaps in research and suggesting future directions toward responsible innovation.
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Ilakiya Ulaganathan (2025) studied this question.
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