How Alternative Data is Transforming Credit Underwriting in India

By Matthew Flannery, CEO & Founder, Branch International

For decades, formal lending systems have relied on a fundamental premise: prior borrowing behaviour is the strongest indicator of creditworthiness. In practice, this has meant that individuals without a documented credit history despite demonstrating stable and responsible financial behaviour, have remained excluded from formal credit access.

In India, this exclusion is particularly pronounced. A large share of the workforce operates within informal or semi-formal income structures, limiting their ability to present conventional income proofs or credit records. As a result, segments such as gig workers, small merchants, and self-employed professionals despite consistent cash flows are often underserved by traditional underwriting frameworks.

However, the rapid evolution of India’s digital public infrastructure (DPI) is fundamentally altering this paradigm. The availability of consent-based, real-time financial data is enabling lenders to move beyond static, backward-looking assessments towards more dynamic and behaviour-driven credit evaluation models. In this context, alternative data is emerging as a critical enabler of both risk precision and financial inclusion.

Structural Limitations of Traditional Underwriting

Conventional underwriting models are anchored in documented income, stable employment, and established credit histories. While effective in highly formalised economies, these models are inherently restrictive in markets like India, where informal income generation is widespread.

Credit bureau scores, a central pillar of traditional assessment, provide insights into past borrowing and repayment behaviour. However, they offer limited visibility into individuals with no prior credit exposure. The absence of a bureau score is frequently interpreted as elevated risk, despite the presence of stable income streams and disciplined financial behaviour outside formal systems.

This creates a structural inefficiency. Digitally active individuals such as platform based workers or merchants transacting via digital payment systems generate consistent economic activity, yet remain outside the purview of formal credit evaluation. For lenders, this results in a trade-off between rejecting potentially creditworthy applicants and underwriting with incomplete information.

DPI as a Catalyst for Underwriting Transformation

India’s DPI framework represents a differentiated approach to financial digitisation – one that is interoperable, consent-driven, and scalable.

The proliferation of UPI has created high-frequency transaction data, offering granular insights into cash flow behaviour. The Account Aggregator ecosystem facilitates secure, user-consented sharing of financial information across institutions. Additionally, GST data enables MSMEs to demonstrate business performance, while Aadhaar-based eKYC streamlines identity verification and onboarding.

Collectively, these systems enable a more comprehensive and real-time view of borrower profiles. Rather than digitising legacy processes, DPI is enabling a re-architecture of underwriting itself shifting from documentation-led to data-led decision-making.

Transitioning from Static to Dynamic Risk Models

The integration of alternative data into underwriting frameworks marks a transition from static risk assessment to dynamic, multi-dimensional evaluation.

Unlike traditional credit scores, which are inherently retrospective, alternative data sources provide forward-looking indicators of financial behaviour. Transaction consistency, income regularity, and cash flow patterns offer a more contextual understanding of repayment capacity.

This enables lenders to enhance decisioning accuracy, reduce turnaround times, and extend credit to segments with thin or non-existent bureau histories. Additionally, more granular data inputs allow for improved risk-based pricing, aligning credit costs more closely with actual borrower risk.

Advancing Financial Inclusion Through Data

The adoption of alternative data has significant implications for financial inclusion. By enabling first-time borrowers to access formal credit based on demonstrated financial behaviour, lenders can facilitate entry into the formal credit ecosystem.

Over time, this supports the creation of verifiable credit histories, improving borrowers’ access to larger and more affordable credit products. Importantly, this approach reframes the perception of “unbanked” populations – not as high-risk segments, but as individuals lacking representation within traditional data systems.

Evidence from emerging markets consistently indicates that such segments, when assessed through broader data lenses, exhibit strong repayment behaviour, reinforcing the viability of alternative underwriting approaches.

Governance, Transparency, and Responsible Innovation

The expansion of data-driven underwriting necessitates a parallel focus on governance and accountability.

The effectiveness of alternative data models is contingent on transparency, explainability, and continuous validation against observed outcomes. Lenders must ensure that model design and data usage do not introduce unintended biases or exclusionary practices.

Regulatory developments, such as India’s Digital Personal Data Protection Act (DPDPA), provide a critical framework for responsible data usage. By emphasising user consent, data minimisation, and security, such regulations underpin trust in digital financial ecosystems.

Outlook: Towards a More Inclusive Credit Ecosystem

As DPI continues to evolve, India is well-positioned to lead the next phase of innovation in credit underwriting. The convergence of digital identity, payments infrastructure, and consent-based data sharing is creating a scalable model for inclusive and efficient lending.

This model is increasingly being observed by other emerging markets seeking to balance financial inclusion with prudent risk management. The ability to integrate alternative data into underwriting frameworks at scale will be a key determinant of competitive advantage in the coming years.

Ultimately, the shift towards data-driven, behaviour-based credit assessment represents a structural transformation. By moving beyond legacy assumptions and embracing more holistic evaluation frameworks, lenders can expand access, improve portfolio quality, and contribute to a more resilient financial system.