RBI mulls scaling up AI and ML for effective supervision and regulation

The Reserve Bank of India (RBI) is planning to use advanced analytics, artificial intelligence (AI) and machine learning (ML) extensively to analyse its huge database and improve regulatory supervision on banks and non-banking financial companies (NBFCs). For this purpose, the central bank is also looking to hire external experts. 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. 

The department has been developing and using linear and a few machine-learnt models for supervisory examinations. The supervisory jurisdiction of the RBI extends over banks, urban cooperative banks (UCBs), NBFCs, payments banks, small finance banks, local area banks, credit information companies and select all-India financial institutions. It undertakes continuous supervision of such entities with the help of on-site inspections and off-site monitoring. 

The central bank has floated an expression of interest (EoI) for engaging consultants in the use of advanced analytics, AI and M L for generating supervisory inputs. 

“Taking note of the global supervisory applications of AI & ML applications, this project has been conceived for use of advance analytics and AI/ML to expand analysis of 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 EoI has said. Among other things, the selected consultant will be required to explore and profile data with a supervisory focus. 

The objective is to enhance data-driven surveillance capabilities of the RBI, the EoI adds. Across the world, regulatory and supervisory authorities are using machine learning techniques (commonly referred to as supertech and regtech) for assisting supervisory and regulatory activities, it has said. Most of these techniques are still exploratory in nature. However, they are rapidly gaining popularity and scale. 

On the data collection side, AI and ML technologies are used for real-time data reporting, effective data management and dissemination. For data analytics, these are being used for monitoring supervised entity-specific risks, including liquidity risks, market risks, credit exposures and concentration risks; misconduct analysis and mis-selling of products.

Report By