By Bhavin Patel, Co-Founder & CEO, Vartis Platforms
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.
This is where India’s lending ecosystem is entering a new phase. 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. As the country’s credit infrastructure grows, lending is shifting from rule-based automation to continuous, context-aware credit intelligence. Agentic AI represents not just a technology upgrade, but the next evolution of India’s credit architecture.
The timing could not be more important. India continues to face a significant credit gap, particularly among MSMEs, first-time borrowers, and underserved segments. Traditional underwriting frameworks were designed for a different era and often struggle to assess emerging borrowers with limited formal credit histories. As lending volumes grow and financial participation deepens, the challenge is no longer just expanding credit supply; it is building systems that can assess risk more accurately, more fairly, and at scale.
The difference this time is that the foundational layers of India’s credit infrastructure are finally in place. Digital Public Infrastructure, such as UPI, Aadhaar and the Account Aggregator framework have 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.
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. In effect, they enable lenders to move from isolated decision-making to connected intelligence across underwriting, monitoring, fraud prevention, and servicing.
One of the most significant applications is underwriting. Today, 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. The opportunity is not simply to approve more loans, but to improve the quality of credit assessment while responsibly expanding access.
Another domain where AI-powered technologies are becoming critical is fraud detection. Today, fraudsters are operating at digital speed and often use strategies that are evolving regularly. It becomes even complex for traditional rule-based approaches 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.
Risk management is simultaneously undergoing transformation. 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 on and resolve them before they escalate, ultimately leading to a larger stable lending environment. This represents an important shift from point-in-time underwriting to continuous risk intelligence.
Loan origination and risk evaluation are not the only areas that get impacted. Collections, borrower interaction, and portfolio servicing can all get an advantage 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 lenders as well as borrowers. As a result, credit and risk assessment become more adaptive, transparent, and sustainable across the borrower lifecycle.
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.
Maintaining the right balance is critical in a highly regulated industry like lending. 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. Responsible AI adoption will ultimately be judged not by efficiency gains alone, but by whether it strengthens trust in the credit system.
India’s unique advantage lies in the combination of digital public infrastructure, consent-driven data frameworks, and a rapidly maturing financial ecosystem. Together, they provide the foundation for an intelligent credit infrastructure that can scale inclusion without compromising discipline.
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. Organisations that combine strong data foundations, responsible AI governance, and disciplined execution will be best positioned to lead this transition.
India’s credit system has already undergone a remarkable digital transformation. The next chapter is not about digitisation, it is about intelligence. As Agentic AI moves from experimentation to implementation, the opportunity extends far beyond automating decisions. It lies in building a new generation of credit infrastructure that is more adaptive, transparent, and inclusive. 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. The institutions that balance innovation, governance, and credit discipline will help define the future of lending in India.