SBI uses AI to underwrite nearly ₹1 trillion of MSME loans in FY26

State Bank of India (SBI) used artificial intelligence (AI) and digital data to underwrite nearly ₹1 trillion of loans of up to ₹5 crore each to medium, small and micro enterprises (MSMEs) in fiscal year 2026 (FY26), reducing the workload of relationship managers. The bank has also seen lower delinquency in the portfolio underwritten through its business rule engine (BRE), managing director Rama Mohan Rao Amara said. The scale of AI-assisted underwriting marks a shift in how the country’s largest lender is processing smaller business loans, with technology taking on much of the information-gathering and preliminary analysis that relationship managers previously handled. “We were able to, using AI, underwrite a large number of loans,” Amara said at the #FIBAC 2026 event in Mumbai on Wednesday. The bank underwrote loans of up to ₹5 crore each through FY26, covering both new-to-bank and existing customers, he said. SBI’s underwriting process draws on the digital footprint available for MSMEs, including GST Network and filing data, bank-account information, credit-bureau scores and other structured and unstructured information. The bank combines these datasets through its business rule engine (BRE) to assess borrowers. That has shifted work away from relationship managers, who previously spent significant time collecting information and conducting preliminary analysis. “It has actually freed the bandwidth of the people who were otherwise the relationship managers, particularly, who were otherwise spending a lot of time in terms of gathering the data, doing some analysis, et cetera,” Amara said. SBI has also seen lower delinquency in the portfolio underwritten through the BRE, he said. SBI is extending AI into other parts of lending and portfolio management. In unsecured lending, it uses AI to assess thin-file customers such as small businesses and proprietorships, where limited information may be available beyond account and UPI data. This is helping the bank expand financial inclusion while meeting priority-sector lending requirements. For existing borrowers, SBI uses AI to identify vulnerable exposures before conventional warning signs such as days past due (DPD) emerge. The models can ingest market- and sector-specific information and other publicly available data to generate early-warning signals, allowing the bank to intervene proactively. The technology is also being applied to routine banking operations. SBI is using AI, including Large Language Models (LLMs), to automate processing of cheques up to ₹10,000. Such cheques account for about 25% of the bank’s cheque volumes. The AI model checks mandatory fields and compliance requirements, allowing these transactions to be processed through straight-through processing with minimal human intervention. Human oversight remains in place. SBI’s control risk unit regularly samples cheques processed by AI to identify errors and determine whether the models need further training. AI is also being used in fraud and technology risk management. SBI’s Security Operations Centre uses AI to process large volumes of IT infrastructure logs, while its Resiliency Operations Centre uses the technology to identify and predict potential breakdowns. The bank is now moving some generative AI experiments into full-fledged applications. Its customer-care operations, for instance, have evolved from using AI to assist human agents to deploying AI bots that can handle customer calls end-to-end, with only complex cases transferred to employees. This has helped reduce handling time and improve customer satisfaction, Amara said. In corporate banking, SBI is piloting an agentic AI-based digital assistant called YonoG, embedded in Yono Business. The tool can collect and read financial and other unstructured documents, populate loan lifecycle management systems and conduct risk analysis. The shift is also changing the role of relationship managers, Amara said. “Now the role of the relationship manager is more in terms of validating that, looking at even the stress testing, whatever is coming out of that model,” he said. SBI has yet to quantify the overall impact of AI through a defined reduction in its cost-to-income ratio. Amara said the benefits were already visible through improved customer satisfaction, better risk management and the release of employee bandwidth for higher-value work. For the quarter ended June (Q1FY27), SBI’s gross advances rose nearly 19% on year to ₹50 trillion. Domestic corporate advances grew over 18% on year to ₹14 trillion, while its SME book rose over 22% on year to ₹6.46 trillion. Subhana Shaikh is a business journalist at Mint, where she covers the Reserve Bank of India, monetary policy, and India’s bond markets. She has seven years of experience in reporting on financial markets, with a focus on banking and the broader financial system.<br><br>She began her career after completing her postgraduate diploma at the Indian Institute of Journalism and New Media, Bengaluru. She then spent five years at Informist Media, a news wire agency, where she closely tracked bond markets and the BFSI sector, developing a strong foundation in market reporting. She later moved to NDTV Profit, where she expanded her coverage across a wide range of business and economic stories.<br><br>At Mint, Subhana focuses on explaining central bank decisions, bond market movements, and banking trends for her readers. Her reporting combines on-ground inputs with careful analysis to help audiences understand complex financial developments.<br><br>Based in Mumbai, she is interested in exploring stories across the business landscape. Outside of work, she enjoys reading and spending time with her three cats. Catch all the Business News, Market News, Breaking News Events and Latest News Updates on Live Mint. Download The Mint News App to get Daily Market Updates. Oops! Looks like you have exceeded the limit to bookmark the image. Remove some to bookmark this image.