What 50% faster AI-driven loan decisions mean for India’s lending sector

India has digitised the loan application process, but the process of deciding on them has not yet been fully digitised.

Over the past decade, the visible layers of lending have transformed. Technology-enabled features such as paperless onboarding, mobile-first journeys, account aggregators, and video KYC have made the credit landscape more accessible. Yet underneath this seamless digital journey lies a quieter constraint: underwriting.

In an economy where credit demand is expanding across micro, small, and medium enterprises (MSMEs), retail borrowers, and semi-urban markets, decisioning rather than acquisition is the primary bottleneck.


This is where artificial intelligence (AI) steps in to alter that equation at the core of the risk engine and not merely at the customer interface level.

Structural friction inside underwriting
Despite digitisation in customer journeys, underwriting workflows in many institutions remain operationally fragmented.

Bank statements, bureau reports, GST filings, collateral documentation, and field investigation notes often reside in separate systems. Underwriters must reconcile, validate, and interpret data before analytical evaluation even begins. Industry estimates suggest that as much as 30-50% of underwriting time may be spent on information gathering and cross-verification rather than risk assessment itself.

While this is manageable at a small scale, it becomes a structural drag at the national scale.

As loan volumes increase, these inefficiencies compound. That means extended turnaround times, elevating cost-to-serve, and introducing variability into decision outcomes. In a high-growth credit market, latency becomes a competitive liability.

credit market at an inflection point
India’s credit ecosystem continues to expand meaningfully. Industry data indicates that the country’s credit market reached approximately Rs 121 lakh crore by March 2025, reflecting roughly 21% year-on-year growth. At the same time, overall bank credit growth moderated to about 13% in early 2026 amid shifting macroeconomic conditions.

Segment dynamics are also evolving. Credit to industry has grown more gradually, while services and retail lending remain significant contributors to overall credit flows.

This divergence underscores a central reality: as access to credit widens, underwriting systems must scale without compromising discipline.

The next phase of credit growth will test not distribution capacity, but decision infrastructure.

The measurable impact of AI in underwriting
Global research offers early indicators of how AI may reshape underwriting economics.

Studies by firms such as Accenture and McKinsey & Company suggest automated underwriting can reduce processing times by 60-70% while lowering operational costs by 30-40%^{1 2}. Operational case studies across mortgage and retail lending segments report reductions in per-file underwriting time and increases in loans processed per underwriter.

Efficiency, however, is only part of the story.

A randomised study in auto lending found that algorithmic underwriting improved loan profitability by over 10% while modestly reducing default rates compared to traditional human-led processes. While outcomes vary across markets and asset classes, the direction of impact is notable: better calibration, not just faster throughput.

This suggests the shift is architectural rather than incremental.

Why manual models struggle at scale
Traditional underwriting was designed for a paper-first era. Today’s lending environment is data-dense and increasingly real-time.

Three structural challenges emerge as institutions scale:
1. Fragmented risk signals
Risk indicators remain siloed across systems, requiring manual triangulation. This introduces both time delays and interpretive variability.
2. Outcome variability
Human judgment is valuable, but inconsistency becomes costly at scale. Two experienced underwriters may interpret the same file differently: acceptable in boutique lending, problematic in mass retail credit.
3. Linear cost expansion
In manual-heavy systems, loan growth often requires proportional increases in operational headcount. Cost-to-serve scales linearly with volume.

In a market as large and diverse as India, linear models eventually constrain velocity.

From workflow automation to intelligence infrastructure
The evolution underway is not merely about automating workflows. It is about building an intelligence infrastructure.

Modern underwriting AI systems are capable of ingesting multi-source data, structuring unstructured inputs, identifying cross-document anomalies, and generating explainable outputs. In production environments, lenders report reductions in time-to-decision ranging from 40-60%, along with measurable declines in file rework and manual interventions.

Importantly, these systems do not eliminate human oversight. Rather, they shift the underwriter’s role from data assembler to risk adjudicator.

The human remains accountable even as the system absorbs the cognitive load.

Implications for India’s lending ecosystem
The implications extend beyond operational efficiency.

Faster credit access
Reduced turnaround times can materially improve borrower experience and reduce drop-offs, particularly in MSME and small-ticket retail segments.
Improved risk calibration
Consistent, data-driven evaluation may strengthen portfolio quality, especially as lending expands into thinner-file geographies.
Regulatory traceability
Structured decision outputs can enhance auditability and explainability, critical in an increasingly compliance-driven regulatory environment.
Scalable financial inclusion
As credit penetrates deeper into semi-urban and rural markets, scalable decision engines become enabling infrastructure rather than optional enhancements.

India’s next credit cycle will not be defined solely by digitised applications but by intelligent evaluation.

Redefining underwriting’s role in the credit cycle
More than 11 crore personal loans were reportedly disbursed in FY 2024-25. That scale is unlikely to contract structurally; if anything, demand complexity will deepen.

For decades, underwriting set the speed limit of lending. In the years ahead, it may determine competitive differentiation.

The institutions that rethink underwriting not as a back-office function, but as a strategic intelligence layer, will likely define the next phase of India’s credit expansion.

Underwriting has long been a lending constraint. It now has the potential to become its advantage.

References:
1. https://www.accenture.com/us-en/insights/banking/ai-lending
2. https://www.mckinsey.com/industries/financial-services/our-insights/the-ai-bank-of-the-future

This article has been contributed by Vignesh Krishnakumar, Co-Founder and CTO, HyperVerge.

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