The Reserve Bank of  India (RBI) is considering to widely use advanced analytics, artificial intelligence and machine learning to analyse its huge database and improve regulatory supervision on banks and Non-Banking Financial Companies. For this purpose, the RBI is also looking to hire external experts.

The RBI is already utilising Intelligence and Machine Learning in its supervisory procedures, but it now plans to build it up so that the Department of Supervision at the central bank can benefit from advanced analytics.

For supervisory exams, the department has been creating and utilising linear and a few machine-learnt models. The RBI has oversight authority over banks, NBFCs, payment banks, small finance banks, local area banks, credit information businesses, and a select group of financial institutions across all of India. With the use of on-site inspections and remote monitoring, it carries out ongoing oversight of such businesses.

To hire consultants who can employ advanced analytics, artificial intelligence, and machine learning to produce supervisory inputs, the central bank has put out a call for expressions of interest (EoI).

This project was created to leverage advanced analytics and AI/ML to broaden analysis of a large data repository with RBI and externally, through the engagement of external specialists, which is expected to considerably boost the efficacy and sharpness of supervision, according to the statement. The chosen consultant will be expected to investigate and profile data with a supervisory focus, among other things.

The EoI stated that the goal is to improve the Reserve Bank's data-driven surveillance capabilities. Machine learning techniques, often known as "Supertech" and "regtech," are being used by regulatory and supervisory organisations all across the world to support their work. Although the majority of these methods are still in the exploratory stage, they are fast expanding in scope and popularity.

AI and ML technologies are utilised for real-time data reporting, efficient data management, and data distribution on the data gathering side. These data analytics are being used for misconduct analysis, product misselling, and monitoring supervised firm-specific hazards, such as liquidity risks, market risks, credit exposures, and concentration concerns.

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