India's Credit Infrastructure Enters Agentic Era: From Rule-Based Lending to Intelligent Credit Orchestration

India’s next credit revolution will not be built on more branches, bigger balance sheets, or even faster apps. It will be built on a more intelligent credit infrastructure . Every loan application, repayment, digital payment, and financial interaction generates valuable signals about a borrower's financial behaviour. With over 20 billion UPI transactions processed in February 2026 alone, India has created one of the world's most dynamic financial data ecosystems .

The opportunity now lies not in generating extra data, but in transforming these signals into better credit decisions, stronger risk management, and wider access to responsible credit .

After years of digitisation and process automation, the focus is now shifting to Agentic AI – intelligent systems that can orchestrate decisions and actions across the lending lifecycle . Unlike traditional automation systems that execute predefined tasks, agentic systems can coordinate multiple functions across the credit and risk lifecycle. They can gather information, evaluate risk, detect anomalies, recommend actions, and continuously learn from outcomes .

The Foundation Is Finally in Place

The difference this time is that the foundational layers of India's credit infrastructure are finally in place. Digital Public Infrastructure, including UPI, Aadhaar, and the Account Aggregator framework, has created a trusted ecosystem for consent-based data exchange at scale . Combined with advances in AI and evolving governance standards, these foundations make it possible to build intelligent credit systems that are both scalable and accountable .

The Reserve Bank of India's Unified Lending Interface (ULI) is being developed to transform credit delivery by integrating access to both financial and non-financial data such as digitised land records, GST data, property records, and satellite data, along with services like e-KYC and the Account Aggregator framework . As of December 2025, 64 lenders were onboarded onto the ULI platform, utilising over 136 data services across 12 different loan journeys .

How Agentic AI Is Reshaping Lending

Underwriting and Credit Access

Credit evaluation is increasingly powered by consent-based financial data, including transaction histories, repayment patterns, GST records, banking behaviour, and other financial signals. Agentic AI can analyse these signals in real time and develop a more comprehensive understanding of borrower risk . This is particularly important in India, where millions of creditworthy individuals and businesses remain underserved due to limited formal credit histories .

AI-powered credit models have the potential to unlock an estimated USD 130-170 billion in economic value, reducing reliance on informal lending by MSMEs .

Fraud Detection and Risk Management

Fraudsters are now operating at digital speed and often use strategies that are evolving regularly. Traditional rule-based approaches struggle to cope with such dynamic risks. Agentic AI systems can observe actions on multiple fronts, recognise irregularities, and react instantly . This approach enables lenders to shift from reactive fraud management to proactive risk infrastructure, strengthening trust across the lending ecosystem .

MuleHunter.AI, launched by the RBI Innovation Hub, is an AI-powered tool designed to identify "mule" bank accounts used in cybercrimes. It uses AI/ML to analyse transaction patterns in real-time, detecting anomalies that indicate money laundering or illegal betting .

Continuous Monitoring

Traditionally, risk assessment has focused on the time of loan origination. But borrower conditions can change over the life of the loan. Agentic systems provide continuous monitoring of the portfolio, including repayment behaviour, cash flow trends, transactional activity, and emerging stress indicators . This allows lenders to identify potential issues early and resolve them before they escalate, representing an important shift from point-in-time underwriting to continuous risk intelligence .

Loan Servicing and Collections

Collections, borrower interaction, and portfolio servicing can all benefit from context-aware, need-anticipating, and action-enabling systems. By identifying warning signs of repayment stress, providing reasonable repayment options, and augmenting service operations, agentic systems can be beneficial to both lenders and borrowers .

Real-World Deployment

The shift from theory to practice is already underway.

• FinBox has launched Atlas, an AI-native lending infrastructure suite that helps lenders reduce loan processing timelines from three weeks to 24 hours . The platform replaces manual lending workflows with AI agents that manage borrower onboarding, document validation, and operational decision-making across the credit journey . In early deployments, Atlas has delivered application completion rates of 85% and reduced credit turnaround time from 21 days to as little as one day .
• GoCredit has launched India's first AI-powered Loan Agent, a conversational system that enables borrowers to discover, match, and apply for personal loans through a single chat-based interaction . Early production data shows a 25 per cent improvement in conversion from offer click to KYC completion .
• Finnable has deployed Fiya, an AI agent designed for "Fintelligence" support, handling complex ticket resolutions at scale and dramatically reducing operational overhead . The company's fraud detection system has already stopped over 450 fraudulent matches .
• AU Small Finance Bank has deployed an AI-native loan origination system built on Dailoqa's Broccoli platform, a multi-agent AI orchestration system designed for regulated financial institutions .

Adoption and Investment Trends

The momentum is clear. According to the Lenovo CIO Playbook 2026, 70% of banking and financial organisations are either piloting or systematically implementing AI across their operations . Confidence remains high, with 93% expecting positive returns and reporting an average return of $2.48 for every dollar invested. In India, organisations project returns of **$2.99 for every dollar invested in AI** .

Agentic AI adoption is projected to grow 104% across the banking sector globally over the next year, with India expected to see even stronger growth at 139% . Already, 27% of banks report deploying agentic AI at meaningful scale .

Indian organisations are planning significant investment increases, with nearly 60% having adopted AI systematically and 99% planning to increase AI investments over the next year. Overall spending is expected to rise by 19% .

Governance and Responsible AI

As AI takes on a greater role across the credit lifecycle, governance frameworks must evolve alongside technological advancements. Explainability, transparency, fairness, data privacy, security, and regulatory compliance need to remain at the core of every AI-driven decision . India's regulatory ecosystem, including the Digital Personal Data Protection Act, the Account Aggregator framework, and evolving RBI guidelines, is helping establish the guardrails required for responsible and trustworthy AI adoption .

However, governance maturity continues to evolve. Only 34% of banking and financial organisations have established comprehensive governance, risk and compliance frameworks for AI oversight .

The Human Element

Importantly, Agentic AI does not reduce the importance of human judgment. Credit remains fundamentally a business of trust, accountability, and responsible decision-making . The strongest lending institutions will combine machine intelligence with human oversight, using technology to enhance judgment rather than replace it .

The future of lending will not be defined by faster workflows alone. It will be defined by the ability to build institutions that continuously improve risk assessment, strengthen trust, and allocate credit more efficiently across the economy . Just as digital public infrastructure transformed payments, intelligent credit infrastructure has the potential to transform how credit is assessed, distributed, and monitored across the economy .