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Informist, Wednesday, Jul. 2, 2025

 

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--RBI Rao: Use of AI, machine learning could simplify disbursement process
--CONTEXT: RBI Deputy Governor Rajeshwar Rao's comments in speech Tue 
--RBI Rao: Microfin to benefit most from AI, machine learning advancement
--RBI Rao: Rules must address issues of data accuracy, security, model risk 
--RBI Rao: Must move towards a unique borrower identifier across system 
--RBI Rao: Credit data refreshed fortnightly, should be done more frequently 

 

NEW DELHI – Microfinance and microloans are likely to benefit the most from advancements in artificial intelligence and machine learning technologies in the credit space, Reserve Bank of India Deputy Governor M. Rajeshwar Rao said. These algorithms can evaluate "alternative" data from diverse sources to better determine a borrower's creditworthiness, he said in a speech uploaded on the central bank's website Wednesday. 

 

"Use of AI/ML could simplify the disbursement process by automating credit assessments and risk evaluations, which not only accelerates fund distribution but also cuts administrative costs, making it practical to offer small loans even in remote regions," Rao said at TransUnion CIBIL's Credit Conference in Mumbai Tuesday. "Moreover, AI models excel at uncovering previously hidden insights in data, enabling financial institutions to more precisely forecast their clients' funding requirements and creditworthiness."

 

The new technologies will enable better credit delivery to individuals who were once ineligible for formal credit due to the absence of credit history, as the data sources assessed become more mainstream, the deputy governor said. Lenders can also bring down operational costs and increase speed of lending by streamlining compliance workflows, including know-your-customer procedures, he said.

 

Credit to the microfinance sector peaked in March 2024 and has been on a declining quarterly trend since, down 13.9% on year by March, according to the RBI's Financial Stability Report for June. Adoption of tighter underwriting standards by the lenders was the primary driver behind the deceleration in credit growth, which also resulted in a decrease in total active borrowers by 4 million, the report said. Banks and non-bank financial companies have an even split of credit to the sector as of March.

 

Tokenisation and the RBI's central bank digital currency can also enhance credit disbursement and delivery, Rao said. A token is a digital representation of financial or real assets on a programmable platform, and could be seen as the next step after dematerialisation and digitisation, he said. Small and medium enterprises could get better access to credit through tokenisation, by narrowing the information gap and tokenising their assets or trade receivables as collateral. Tokenisation could also reduce counter-party risk by simultaneous transfer of assets and payment against them, reducing the need for collateral.

 

Test programmes for programmable e-rupee were also promising, where a bank had run a pilot to extend e-rupee through Kisan Credit Cards for end-use monitoring, Rao said. This pilot gives credit to tenant farmers without land records by livelihood activity tracking, a model which can be replicated for loans to micro-enterprises, street vendors and artisans, he said. The RBI launched its retail central bank digital currency pilot in December 2022, and outstanding volumes of the retail e-rupee rose fourfold on year, as of March, to INR 10.17 billion.

 

The rise of financial technology companies and platforms like Open Credit Enablement Network and Open Network for Digital Commerce should also enhance credit disbursal. The central bank's aim is to create a regulatory environment that fosters innovation and to maintain the financial system's integrity. At the same time, Rao said that the RBI must be aware of the need to address issues around data accuracy, security, and model risk. 

 

"Inaccurate or incomplete data can undermine the reliability of analytical outputs and decision-making processes, while poor data security can expose organisations to breaches, resulting in legal liabilities and reputational damage," Rao said. "Additionally, the use of complex AI and machine learning models introduces concerns around model risk, especially when these models are not thoroughly tested, validated, or monitored for biases and performance drifts."  

 

Credit information companies must also help bridge the credit gap through enhancing data freshness and improving data quality, Rao said. The deputy governor aspires for more frequent credit data updates than the current fortnightly ones, and said real-time or near-real-time reporting will improve underwriting precision while benefitting customer experience. These companies should provide a data quality index score to credit institutions they get identification data from, to improve the data quality.

 

"CICs (credit information companies) rely on credit institutions to provide accurate and validated IDs. Without this, duplication and misreporting remain risks," Rao said. "We must move towards a unique borrower identifier, which is secure, verifiable, and consistent across the system."  End

 

Reported by Aaryan Khanna

Edited by Tanima Banerjee

 

 

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