RBI plans to extensively use artificial intelligence, machine learning to improve regulatory supervision, CIO News, ET CIO

The Reserve Bank plans to make extensive use of advanced analytics, artificial intelligence and machine learning to analyze its vast database and improve regulatory oversight of banks and NBFCs.

For this purpose, the central bank is also looking for external experts.

While RBI is already using AI and ML in supervisory processes, it now intends to expand it to ensure central bank supervision can reap the benefits of advanced analytics.

The department develops and uses linear and some machine learned models for regulatory testing.

RBI’s supervisory jurisdiction extends to banks, municipal cooperative banks (UCB), NBFCs, payment banks, small financial banks, local banks, credit information firms and selected all Indian financial institutions.

It takes over the continuous monitoring of these companies with the help of on-site inspections and off-site monitoring.

The Central Bank has published an Expression of Interest (EoI) for engaging consultants in the use of advanced analytics, artificial intelligence and machine learning to generate supervisory data.

‚ÄúConsidering the global oversight applications of AI and ML applications, this project was designed to use Advance Analytics and AI/ML to augment the analysis of huge datasets with the RBI and externally by involving external experts, which is expected to to significantly improve the effectiveness and sharpness of supervision,” it said.

Among other things, the selected consultant must examine and profile data with a regulatory focus.

The aim is to improve the Reserve Bank’s data-driven monitoring capabilities, the EoI said.

Around the world, regulators and supervisors are using machine learning techniques (commonly referred to as “supertech” and “regtech”) to support oversight and regulatory activities, she added.

Most of these techniques are still exploratory but are rapidly gaining popularity and scope.

On the data collection side, AI and ML technologies are used for real-time data reporting, effective data management and dissemination.

For data analysis, these are used to monitor supervised company-specific risks, including liquidity risk, market risk, credit exposure and concentration risk; misconduct analysis; and mis-selling of products.