The Reserve Bank of India (RBI) has contracted global consultancy powerhouses, McKinsey and Company India LLP and Accenture Solutions Pvt Ltd India, to implement artificial intelligence (AI) and machine learning (ML) into its supervisory functions.
With the objective of enhancing its regulatory oversight of banks and Non-Banking Financial Companies (NBFCs), the RBI aims to deploy advanced analytics, AI, and ML to decipher its expansive database. This strategy led to the decision of enlisting external expertise.
Last September, the central bank released an expression of interest (EoI) to secure consultants adept in advanced analytics, AI, and ML, targeting the generation of supervisory insights.
The EoI screening phase saw the RBI shortlist seven potential contenders: Accenture Solutions Private Limited, Boston Consulting Group (India) Pvt Ltd, Deloitte Touche Tohmatsu India LLP, Ernst and Young LLP, KPMG Assurance and Consulting Services LLP, McKinsey and Company, and Pricewaterhouse Coopers Pvt Ltd. From this list, McKinsey and Accenture have emerged victorious, securing a contract valued at approximately Rs 91 crore.
Although the RBI has previously integrated AI and ML into its supervisory mechanisms, this new collaboration seeks to expand this integration. The central objective is to enrich the capabilities of the Department of Supervision by harnessing advanced analytics.
The EoI issued last year highlighted that the Department of Supervision has been dabbling in linear and selected machine-learnt models for supervisory examinations. The current interest revolves around dissecting the data to identify features that can provide enriched supervisory insights.
The supervisory purview of the RBI spans across banks, urban cooperative banks, NBFCs, and various other financial entities. This supervision seeks to evaluate their financial stability, asset quality, governance structures, and more, aiming to safeguard depositors’ interests and ensure financial steadiness.
The central bank leverages a blend of on-site inspections and off-site monitoring for continuous supervision of these entities, as mentioned in the EoI.
Globally, regulatory authorities are turning to machine learning techniques, often termed ‘suptech’ and ‘regtech’, to bolster supervisory and regulatory actions. While many of these methods are in the exploratory phase, their adoption is expanding at a rapid pace.
In the realm of data, AI and ML technologies have found utility in real-time reporting, efficient data management, and distribution. Additionally, they’re being utilised for analysing firm-specific risks, overseeing liquidity risks, market risks, and credit exposures; and detecting product mis-selling and misconduct.
