data, sees consistent filings since she started the business and spots steady exports. It identifies seasonal spikes in her banking transactions and cross-maps the invoices with the payments. It notices she’s stocking up ahead for a festival and recognizes her as a regular vendor for a well-reputed lifestyle brand in the U.S. Playing by the rules of the old world, her file would have landed on an underwriter’s desk, waiting for a decision: Should we give her a loan?
Instead, the new playbook empathizes. An AI sales agent nudges a personalized smart product fit offer. An underwriter agent kicks in, reviewing the risk markers. An operations agent assures regulatory compliance, data validation and workflow orchestration. A GenAI assistant turns all of this into a clean, simple decision rationale, not just for credit officers but also for compliance, operations and sales teams.
The shift from
automation to embedding intelligence to now, driving agentic autonomy is evident. With data at its core, the era of decision-capable AI is here.
The last decade saw financial institutions digitise processes, interfaces, and data capture. For example, one large public sector bank in India adopted a digital lending platform and was able to launch 15 products across retail, corporate, MSME, and agriculture. In just four years, they disbursed over ₹35,000 crores, processed more than 10 million digital transactions, and achieved 3x growth in their digital loan book. That was automation – the first phase of digitization. Necessary, but in today’s context not enough. According to a BCG Report, productivity in mature markets has plateaued at just 1% CAGR – largely because digitization only automates the “happy paths”. It’s great for straight through journeys but when complexity kicks in – which is often – the system fails to deliver.
As digital channels gained popularity financial institutions got access to a wealth of digitally sourced data. However, the challenge was in harnessing its full potential to drive growth effectively. Mukesh Ambani famously described data as “the new oil” – but like oil, it’s only valuable when refined. Traditional digitization ensured speed and efficiency, but it lacked one crucial element: insights. That’s where AI flipped the script shifting the focus from automation to intelligence.
A loan application isn’t the end – it’s the start of a relationship. Every transaction, pattern or moment of friction is a signal. The pressing question is: how do we decode these signals? That’s where behavioural scoring steps in, especially when the borrower is new-to-credit or when their financial footprint doesn’t fit neatly into legacy models. This is the reality for millions of individuals and MSMEs across emerging markets.
By analyzing various factors like cashflow patterns, liquidity, collections efficiency, customer concentration, loan behaviour, governance quality, anomalous behaviour, etc., for individuals as well as for MSMEs - financial institutions are building richer, more dynamic borrower profiles. It’s a data to DNA approach – every insight distilled in one powerful score that helps lenders drive growth across the customer lifecycle - acquisition, credit-decisioning, monitoring, portfolio analytics and cross-sell/up- sell.
Leading Indian banks and NBFCs are adopting behavioural scores as part of their detailed customer assessment. One of the top private banks was able to build a ₹15,000 crore MSME loan book, propelling a substantial 40% growth within a year with an enhanced “Go-No-Go” in under 2 minutes, all while maintaining a Gross Non-Performing Asset (GNPA) of less than 1%. A large NBFC has assessed 50,000+ applications in five months using a behavioural scoring approach and disbursed loans of upto ₹50,00,000 based on banking transactions alone.
The MSME sector is complex. Cashflow fluctuates due to seasonality or long, uncertain credit periods, and it is particularly vulnerable to macroeconomic shocks. A commodity price surge in China or a tariff shift in the US can ripple through supply chains and destabilize small enterprises overnight. That’s why better signals are critical for everyone in the ecosystem: lenders, policymakers, and the MSMEs themselves.
AI layered on multiple consent-led digitally sourced data points is a game changer. It distils deep insights into a single, adaptive behavioural score - one that evolves with the business. For MSMEs, it serves as a compass to reflect, and course correct. For lenders, it provides unbiased insights into the customers’ business performance, credit behaviour and overall persona. Additionally, it provides entity- level, portfolio-level and macro-level insights highlighting potential risks and opportunities. For sectors facing challenges due to market dynamics, these insights help policymakers recalibrate schemes and deliver support when it’s needed the most.
One score tells many tales, as it holds different meanings for different stakeholders, each viewing it through their own lens. GenAI translates complex insights into contextual and actionable conversations for every stakeholder.
Trained on hundreds of thousands of lending interactions, portfolio trends, and sectoral signals, AI including GenAI can answer questions for all the stakeholders. For a lender the question might be: “Which segment in my portfolio poses the highest risk under the current macroeconomic conditions?”. An MSME might ask: “How can I boost my business performance through supply-chain optimization or an effective pricing strategy? And which government schemes am I eligible for?” A policymaker might ask: “Which scheme or sectoral intervention will deliver the most on ground impact?”
These valuable AI-driven insights that are accessible via systems, platforms, chatbots or voice assistants help underwriters, borrowers, and policymakers make decisions with confidence.
Agentic AI is setting a new standard by introducing autonomous agents into the mix. It is redefining lending by making independent decisions, swiftly adapting to changing environments and acting purposefully to meet specific goals. Think of them as your digital coworkers. Crucially, this isn’t about removing humans from the lending equation, it’s about empowering them with intelligence. At every stage of the lending lifecycle, from sales to underwriting to operations – AI agents are stepping in. Whether it is for identifying smart product recommendations, generating credit decisions backed by behavioural scores or eliminating process bottlenecks, these agents unlock speed, scale and efficiency with precision.
Lenders continue to maintain a “human in the loop” approach. By starting in co-pilot mode and graduating to autopilot in low-risk cases, they can scale without compromising governance.
Ironically, AI powered decisioning isn’t here to replace humans, it’s here to restore the human touch. We’re returning to a time when our bank truly knew us. Earlier, they knew us by our faces and our stories – they still know us through our stories but told by data. As we transition from automation to augmented intelligence, lenders are empowered to say yes more often to the right customers, at the right time and for the right reasons.
2.20 a.m. Application approved. Decisioned not by automation but by augmented intelligence.
The writer is Founder & Managing Director at Jocata.
