Credit is, at its core, a measure of trust. For decades, lenders have leaned heavily on bureau scores, financial statements and collateral as signals of creditworthiness. These traditional models have, no doubt, brought structure and efficiency to lending. However, they are also somewhat narrow in their scope.
The result is that a large part of the population—especially new-to-credit individuals, gig workers, and small businesses—remains outside their field of vision.
According to the World Bank’s Global Findex Report, only 8% of Indian adults access formal borrowing which is less than half the global average of 17%. This underscores a massive credit gap, where millions of financially responsible individuals remain excluded from organized lending channels simply because they lack traditional credit histories. In situations like these, it is alternate data that can provide a way to bridge this divide by expanding access without diluting risk standards.
The Business Case for Alternate Data
The financial life of a borrower is not limited to loan repayments or credit card bills.
It plays out in everyday obligations like electricity bills, rent transfers, society maintenance outgoings, mobile recharges, GST filings and even digital wallet usage. These activities are consistent, verifiable signals of repayment discipline and financial behaviour. When incorporated responsibly, they can enrich credit scoring models and enable lenders to make much more nuanced decisions.
In a country like India, their significance is even more amplified thanks to the sheer diversity of borrower profiles. For instance, a salaried employee in Bengaluru, a kirana shop owner in Lucknow, and a gig worker in Mumbai may all have different data footprints. Traditional credit scoring systems are not designed to capture this diversity. Alternate data, however, brings these varied signals into the fold and allows lenders to move from exclusionary, one-size-fits-all models toward inclusionary, contextual ones.
Types of Alternate Data to Drive Inclusion & Risk Precision
Alternate data is not a monolith. Different sources provide different kinds of insights, and their relevance often depends on the borrower segment. A few stand out for their impact, especially when we talk in context of a country like India:
Utility and Rental Payments
For thin-file borrowers, utility and rental payments are often the most consistent indicators of repayment discipline. A person who pays their electricity bill on time for years demonstrates financial reliability, even if they have never taken a loan before.
Bank Transactions and GST data
For small businesses, particularly MSMEs, transaction history often tells the truest story of financial health. Patterns in cash inflows and outflows, supplier payments or GST returns are dynamic indicators of sustainability. These are invaluable for underwriting where audited statements or bureau data are sparse.
Mobile and Behavioral Data
Today, smartphones are proxies for financial identity. App usage patterns, mobile wallet transactions and even geolocation stability provide behavioral signals. While such data must be handled carefully to avoid bias, when combined with traditional inputs, it strengthens risk models significantly.
Digital Footprint and Transaction Data
Another growing source of insight comes from digital payment activity, for example, QR code–based transactions at small retail stores, local markets, and neighborhood kiranas. These data points reflect both consumer spending capacity and merchant sales/ income patterns. Many lenders now use such aggregated transaction data (often shared through open data platforms) to supplement credit scoring, particularly for micro-entrepreneurs.
Psychometric and Socio-economic Indicators
Structured questionnaires and socio-economic profiles based on location intelligence are increasingly used to assess intent and capability. While they cannot replace transactional data, they complement it well, particularly for first-time borrowers in rural or semi-urban areas.
Government and Public Records
Digitization of land records, transport registrations, and tax filings adds another layer of verifiable information. With frameworks like the Account Aggregator ecosystem and the Unified Lending Interface (ULI) in pilot stage, the availability of such consented data will only expand.
Opportunities and Risks In Alternate Data Use
The opportunity is obvious: alternate data makes credit more inclusive, accurate, and responsive. But it comes with caveats. Data quality is inconsistent across geographies and providers. Informal arrangements such as cash-based rentals, may never show up.
More critically, alternate data can encode socio-economic biases if used carelessly. For example, smartphone brand or app usage may correlate with income in ways that unintentionally discriminate.
This is why lenders must approach alternate data with a strong framework of consent, fairness, and explainability. Borrowers should know what data is being used and why. Models should be auditable, and decision outcomes explainable, not opaque black boxes.
Role of Modern Lending Platforms as a Service (LPaaS) in Mainstreaming Alternate Data
Now, here’s the thing: Identifying useful alternate data is surely a challenge. But integrating it into the lending process is a bigger one. Modern LPaaS platforms are transforming this space by making alternate data practical, scalable, and responsible.
Unlike legacy systems, these platforms are built for flexibility and speed. They come with out of the box integration capability through pre-built APIs, allowing lenders to fetch and validate information from multiple sources including traditional (bureau scores, KYC, bank statements etc) and alternate (GSTN, utilities, mobile intelligence, socio-economic data etc). This not only reduces manual effort but also ensures accuracy and real-time access.
Another enabler is the Credit Business Rules Engine (BRE) of LPaaS platforms which allows credit teams to configure scorecards with multiple elements and varying weightages,, set thresholds, and experiment with new data inputs without heavy IT dependence. Plus, a no-code or low-code approach offered by these platforms means underwriting models evolve continuously, adapting to new borrower segments and regulatory requirements.
Additionally, the ability of LPaaS platforms to balance STP (Straight-Through Processing) with NSTP (Non-STP) flows is equally important. With richer data, many applications can be fully automated, reducing turnaround times drastically. For cases where confidence levels are lower, the workflow seamlessly routes to manual or supervisory review. This hybrid approach ensures both efficiency and prudence.
Finally, workflow flexibility is a critical differentiator. LPaaS platforms built on microservices architectures can support multiple, tailored loan journeys instead of locking lenders into monolithic processes. Whether serving salaried professionals in metros or small traders in semi-urban clusters, lenders can configure workflows that reflect the realities of each micro-market.
Credit Access as a Driver of Growth and Mobility
Lending is not simply a financial transaction, but rather an enabler of opportunity. In India, access to credit directly impacts entrepreneurship, education, home ownership, and social mobility. By expanding the definition of creditworthiness, alternate data helps lenders unlock these opportunities for millions who were previously excluded.
On the other hand, for lenders, the advantages go beyond inclusion. Richer data reduces delinquency risk, enables product innovation, and builds trust with regulators through transparent and auditable processes. What’s more, the ability to design customized journeys, leverage alternate data, and deliver faster approvals is becoming a strategic differentiator.
Final Words
The trajectory is clear: as digital footprints grow, alternate data will become a mainstream component of credit scoring and not just an experiment at the margins. The question is not whether lenders should adopt it, but how responsibly and effectively they do so.
Modern LPaaS platforms enable mainstreaming of alternate data in credit scoring seamlessly by integrating new data sources as they emerge, configuring credit policies in real time and enabling design of loan journeys that reflect the realities of diverse borrower segments.
This is precisely the philosophy behind IncrediHub, WonderLend Hub’s LPaaS platform built around a GrowthOps principle. IncrediHub with its adaptive infrastructure combines deep integration capabilities with configurable workflows and intelligent decisioning, ensuring that credit decisions are not just faster, but also more resilient.
With platforms like these, lenders can not only keep pace with the changing credit landscape, but lead it by making credit truly inclusive.
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