The Reserve Bank intends to make considerable use of artificial intelligence, machine learning, and advanced analytics to analyse its massive database and enhance regulatory supervision of banks and NBFCs. The central bank is attempting to engage outside specialists for this purpose as well.
While the RBI already employs AI and ML in supervisory procedures, it now plans to build it up so that the Department of Supervision at the central bank may gain from sophisticated 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.
In order 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).
“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,” it said.
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.
