reserve Bank of India Seeks to extensively use advanced analytics, artificial intelligence and machine learning to analyze its huge database and improve regulatory supervision over banks and NBFCs. For this purpose, the central bank is planning to hire external experts.
In September this year, the RBI invited expressions of interest (EOI) Hiring consultants for the use of advanced analytics, artificial intelligence and machine learning to generate supervisory inputs.
Based on the scrutiny/evaluation laid down in the EOI document, the central bank has shortlisted seven applicants to participate in the Request for Proposal Process (RFP) for selection of consultants, according to a RBI document. The seven firms are Accenture Solutions Pvt Ltd; Boston Consulting Group (India) Private Limited; Deloitte Touche Tohmatsu India LLP; Ernst & Young LLP; KPMG Assurance & Consulting Services LLP; McKinsey & Company; and Pricewaterhouse Coopers Pvt. Ltd.
While RBI is already using AI and ML in supervisory processes, it now intends to ensure that the benefits of advanced analytics can be extended to the supervision department in the central bank.
The Department of Supervision is developing and using linear and some machine-learning models for supervisory examinations.
The EOI released in September states that the interest is now in exploring the data to identify its features that can be leveraged to generate new and improved supervisory inputs.
The supervisory jurisdiction of RBI extends to banks, urban co-operative banks, NBFCs, payments banks, small finance banks, local area banks, credit information companies and select all Indian financial institutions.
It carries out supervision with the objective of assessing the financial soundness, solvency, asset quality, governance structure, liquidity and operational viability of these institutions to protect depositors’ interests and financial stability.
The EOI said that RBI continuously monitors SE with the help of on-site inspection and off-site monitoring.
Around the world, regulatory and supervisory authorities are increasingly using machine learning techniques (commonly referred to as ‘suptech’ and ‘regtech’) to aid supervisory and regulatory activities.
Most of these techniques are still exploratory, 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 to monitor supervised firm-specific risks, including liquidity risk, market risk, credit risk and concentration risk; malpractice analysis; and mis-selling of products.