The Reserve Bank plans to extensively use advanced analytics, artificial intelligence and machine learning to analyze its huge database and improve regulatory supervision of banks and NBFCs.
To this end, 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 scale it up to ensure that the benefits of advanced analytics can accrue to the central bank’s Supervisory Department.
The department has been developing and using linear models and some machine learning models for supervisory examinations.
The RBI’s supervisory jurisdiction extends to banks, urban cooperative banks (UCBs), NBFCs, payments banks, small finance banks, local banks, credit reference companies and all financial institutions in India.
Carry out continuous supervision of these entities with the help of on-site inspections and off-site monitoring.
The central bank has submitted an expression of interest (EoI) to engage consultants in using advanced analytics, artificial intelligence and machine learning to generate supervisory inputs.
“With global application monitoring applications of AI and ML in mind, this project has been designed to use Advance Analytics and AI/ML to extend the analysis of a large data repository with
RBI and externally, by engaging external experts, which is expected to greatly enhance the effectiveness and acuity of supervision,” he said.
Among other things, the selected consultant will need to explore and profile data with a supervisory approach.
The objective is to enhance the Reserve Bank’s data-driven surveillance capabilities, the EoI said.
Around the world, regulatory and supervisory authorities are using machine learning techniques (commonly referred to as “Supertech” and “regtech”) to assist in supervisory and regulatory activities, he added.
Most of these techniques are still exploratory, but are rapidly gaining popularity and scale.
In terms of data collection, AI and ML technologies are used for real-time data reporting, effective data management and dissemination.
For data analysis, they are used to monitor the specific risks of the supervised company, including liquidity risks, market risks, credit exposures and concentration risks; misconduct analysis; and the poor sale of products.