The RBI is planning to extensively use advanced analytics, AI, and ML for generating supervisory Inputs

The Reserve Bank is planning to extensively use advanced analytics, AI, and ML for generating supervisory Inputs to analyze its huge database and improve regulatory supervision of banks and NBFCs. The RBI central bank is also considering appointing external experts. The use of AI & ML is popular for data collection and data analytics. While the RBI is already using AI and ML in supervisory processes, it now intends to upscale it to ensure that the benefits of advanced analytics can accrue to the Department of Supervision in the central bank.

This Project has been conceived for use of advanced analytics and artificial intelligence/machine learning to expand the analysis of a huge data repository with RBI and externally, through the engagement of external experts, which is expected to greatly enhance the effectiveness and sharpness of supervision. The department uses linear and a few ML models for supervisory examinations, with the supervisory jurisdiction of the RBI extending over banks, urban cooperative banks (UCB), local area banks, small finance banks, payment banks, NBFCs, credit information companies, all India financial institutions, among others.

RBI to extensively use AI and ML:

The RBI undertakes continuous supervision of Supervised Entities (SEs) with the help of on-site inspections and off-site monitoring. With the goal of generating supervisory inputs, the central bank has given an expression of interest (EoI) for engaging consultants in the use of AI and machine learning, and advanced analytics for generating supervisory inputs. No contractual obligation on part of the Bank whatsoever shall arise based on this EOI process. The Bank reserves the right to accept or reject any or all the EOI offers without assigning any reason thereof.

Supervisory authorities have augmented their formal data sources with unstructured data sources. Taking note of the global supervisory applications of AI & ML applications, this project has been conceived for use of advanced analytics and AI/ML to expand the analysis of the huge data repository with RBI and externally, through the engagement of external experts, which is expected to greatly enhance the effectiveness and sharpness of supervision.

It undertakes continuous supervision of such entities with the help of on-site inspections and off-site monitoring. Among other things, the selected consultant will be required to explore and profile data with a supervisory focus. It stated that the selected consultant will be required to profile and explore data with a supervisory focus, with an 'objective to enhance the data-driven surveillance capabilities of the Reserve Bank'.

Regulatory and supervisory bodies globally are using commonly referred machine learning techniques such as 'Supertech' and 'Regtech' for assisting supervisory activities. Most of these techniques are still exploratory but are rapidly gaining popularity and scale. The objective is to enhance the data-driven surveillance capabilities of the Reserve Bank. Most of these techniques are still exploratory, however, they are rapidly gaining popularity and scale.

On the data collection side, artificial intelligence and machine learning technologies are used for real-time data reporting, effective data management, and dissemination. The information is provided on the basis that it is non–binding on the Bank or any of its authorities or agencies or officers, employees, agents, or advisors. For data analytics, these are being used for monitoring supervised firm-specific risks, including liquidity risks, market risks, credit exposures, and concentration risks; misconduct analysis; and mis-selling of products.