How AI Is Reshaping India's Banking and Financial Services Sector

The Scale of Adoption

A 2024 NASSCOM-BCG report found that 76 per cent of Indian financial services firms are actively deploying AI solutions, making the sector one of the fastest adopters of the technology in the country. Public sector banks, traditionally slower to innovate, are no longer sitting out. State Bank of India's 

AI-driven platform, YONO, clocked over 65 million registered users by Q1 2024. Bank of Baroda's fraud detection systems, powered by machine learning models, reportedly flagged and prevented fraudulent transactions worth over ₹900 crore in the 2022-23 financial year. 

HDFC Bank has deployed conversational AI across its customer service channels, handling over 80 percent of routine queries without human intervention — a statistic that would have been considered outlandish just five years ago.

Credit Where Credit Is Due: Alternative Lending

Perhaps the most socially significant application of AI in Indian finance is in credit access. India has an estimated 190 million credit-underserved individuals — people with little or no formal credit history who are effectively shut out of the banking system by conventional underwriting models. AI is rewriting this equation. 

Fintechs like Perfios, CreditVidya (now part of TransUnion), and Lendingkart have built alternative credit scoring models that draw on non-traditional data: utility bill payment histories, mobile phone usage patterns, e-commerce purchase behaviour, and even app usage frequency. Early results suggest these models outperform traditional CIBIL-based scores in predicting default risk for thin-file borrowers — a finding that has significant implications for financial inclusion. 

The RBI's Account Aggregator framework, which allows banks to access consented financial data across institutions, has given these AI models an even richer data universe to work with.

The Regulatory Tightrope

The Reserve Bank of India has been watching this transformation with a mixture of encouragement and caution. The RBI's guidance note on operational risk and resilience, updated in 2023, specifically flagged AI model risk as a concern — particularly the opacity of deep learning models used in credit decisioning. How do you explain to a rejected loan applicant why an algorithm said no? 

The RBI has indicated that explainability requirements for AI-driven credit models will be a regulatory priority. This is not merely a compliance headache. It is a genuine ethical challenge. AI models trained on historical lending data risk encoding and amplifying existing biases — against women, against agricultural workers, against certain geographies. Several civil society groups have already raised alarms about discriminatory outcomes in AI-driven lending.

RBI is moving toward a responsible AI framework for finance - The Economic Survey 2025–26 highlights RBI’s FREE-AI approach — a framework for the responsible and ethical enablement of AI in the financial sector. This is important for your article because it shows India’s AI-banking story is not just about innovation, but also about governance, bias, explainability, accountability, and risk controls. 

RBI is linking AI adoption to customer service and grievance handling.

In March 2025, RBI Governor Sanjay Malhotra urged banks to use AI to improve complaint handling. Reuters reported that 95 commercial banks received more than 10 million customer complaints in FY2023-24. This makes AI valuable not only in risk and lending but also in service quality, complaint prediction, and multilingual support. 

Fraud is becoming the biggest AI justification in digital banking: PIB said just this week that NPCI provides an AI/ML-based fraud monitoring solution to all banks for generating alerts and declining suspicious transactions. That gives you a clear, current line: AI is not a futuristic add-on anymore; it is already embedded in India’s payments defence infrastructure. 

RBI is tightening consumer protection in response to digital fraud: In February 2026, RBI said it would propose a framework to compensate customers hit by small-value digital frauds, with coverage up to 85 per cent of the loss or ₹25,000, whichever is lower. Reuters reported draft guidelines followed on March 6, 2026. This is a major policy update because it reflects the new reality of AI-era digital banking: faster payments require smarter safeguards. 

What the Next Five Years Look Like

Generative AI is the next wave rolling in. Several Indian banks are already piloting GenAI-powered relationship managers — chatbots that go far beyond scripted responses, offering personalised financial planning conversations, investment suggestions based on life stage, and proactive nudges about tax saving opportunities. 

Axis Bank and Kotak Mahindra Bank have both announced dedicated GenAI labs as of early 2024. The risk of job displacement in banking is real and significant, particularly in back-office functions like document processing, reconciliation, and compliance reporting. 

Industry estimates suggest 15-20 percent of current BFSI roles could be automated or substantially altered within five years. The transformative power of AI in Indian banking is undeniable. The task now is to ensure that this transformation is equitable, explainable, and resilient — not just efficient.