The Reserve Bank of India intends to heavily utilize advanced analytics, artificial intelligence, and machine learning to analyze its massive database and enhance regulatory supervision of banks and NBFCs.
The Indian central bank is also seeking to recruit outside specialists. While the RBI is already using AI and ML in supervisory processes, it now intends to scale it up so that the benefits of advanced analytics can be realized by the central bank’s Department of Supervision.
The department has already developed and used linear and a few machine-learnt models for supervisory examinations. The RBI has supervisory authority over banks, urban cooperative banks (UCB), NBFCs, payment banks, small finance banks, community banks, credit information companies, and certain all-India financial institutions.
It conducts continuous oversight of such entities through on-site inspections and off-site monitoring.
The central bank has issued an expression of interest (EoI) for consultants to generate supervisory inputs using Advanced Analytics, Artificial Intelligence, and Machine Learning.
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“Taking note of the global supervisory applications of AI & ML applications, this Project has been conceived for the 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 enhance the effectiveness and sharpness of supervision greatly,” it said.
The chosen specialist will be asked to investigate and profile data with a supervisory focus, among several other things.
The EoI stated that the goal is to improve the Reserve Bank’s data-driven surveillance capabilities. It added that regulatory and supervisory authorities worldwide use machine learning techniques (commonly referred to as ‘Supertech’ and ‘regtech’) to assist supervisory and regulatory activities. Most of these techniques are still in their early stages but rapidly gaining popularity and scale.
AI and ML technologies are used in data collection for real-time data reporting, effective data management, and dissemination. These are used for monitoring supervised firm-specific risks, such as liquidity, market, credit exposures, and concentration risks; misconduct analysis; and product mis-spelling.