For decades, the story of credit in India was simple and unfair. If you did not have a salary slip, a PAN linked bank account with three years of history, or property to pledge, you did not get a loan. Roughly 87% of India's 63 million MSMEs still fall outside formal credit, leaving a funding gap estimated between ₹20 lakh crore and ₹25 lakh crore. AI-powered credit scoring was supposed to fix this. Instead of judging people by paperwork, algorithms would judge them by behaviour: how they spend, save, pay bills, and use their phones.
That promise is real. It is also incomplete. A growing body of research, including regulatory work inside India itself, shows that AI credit models can quietly rebuild the same walls they were meant to tear down, just with more data and less accountability.
Why AI Credit Scoring Matters for India’s MSMEs
Traditional credit scoring needs a credit file to exist first. No file, no score. No score, no loan. This locked out gig workers, street vendors, first-time earners, and anyone paid in cash.
Alternative data changes the input. Lenders using AI models now pull from UPI transaction patterns, GST returns, utility and rent payments, e-commerce behaviour, and Account Aggregator-linked bank statements instead of, or alongside, a Bureau score. For a small business making regular UPI collections but never touching a formal loan, this is genuinely useful. It converts informal cash flow into something a machine can score.
This is why digital lending grew so fast in India, and why RBI has spent the last two years trying to regulate it properly rather than shut it down. The Digital Lending Directions, 2025 formalised rules for Lending Service Providers and Regulated Entities, and mandated a public directory of approved apps on the CIMS portal. The intent was consumer protection first. Fairness of the underlying model came second, and that gap is where the exclusion story lives.
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How Alternative Data Is Changing Digital Lending
Nobody builds a credit model that says "reject women" or "reject rural applicants." Indian law already prohibits lenders from discriminating on sex, caste, or religion. The bias shows up sideways, through variables that look neutral but quietly stand in for protected traits.
Researchers studying Indian lending apps have documented models that treated frequent calls to parents, a large stored contact list, or even gameplay patterns like car racing apps as signals of "good" credit character. None of these variables mention gender, caste, or income directly. But they correlate heavily with a specific user: urban, digitally active, middle-class, and usually male, because that is who has had the smartphone, the data plan, and the digital history long enough to generate a rich footprint in the first place.
This is the paradox at the centre of AI fairness debates worldwide, and it applies just as much in India. Deliberately excluding gender or caste from a model's inputs, so-called "fairness through unawareness," does not stop discrimination. If income, phone brand, mobility patterns, or app usage already correlate with gender or caste, the algorithm reconstructs the excluded variable anyway through its proxies. Removing the label does not remove the pattern baked into the underlying data.
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Who Gets Left Out in Algorithmic Biasness
- Women. Microfinance data has long shown women repay loans at rates equal to or better than men. Yet several studies on Indian lending apps found models still implicitly favoured male-coded behaviour patterns. The unintended consequence is telling: researchers have found women taking loans in the names of male relatives specifically to avoid what they perceive as a lending algorithm that scores them lower. Instead of the technology closing a gender gap, some borrowers are routing around it.
- Rural applicants. Alternative data works best where there is a lot of data. Rural India has weaker digital payment density, patchier internet, and less consistent smartphone ownership than metro India. A thin digital footprint reads to a model as "insufficient signal," which functions the same as a rejection, even though the underlying repayment capacity may be perfectly sound.
- Informal and gig workers. Ironically, some of AI credit scoring's intended beneficiaries fall through a second net. Income that arrives in irregular cash bursts, or across multiple small UPI handles rather than one steady salary account, can look "unstable" to a model trained mostly on salaried, formal-sector patterns.
- Non-English, non-smartphone users. Many scoring apps, onboarding flows, and Video KYC systems assume comfort with English-language interfaces and a mid-range smartphone. Older applicants, first-generation earners, and vernacular-first users can be filtered out simply by friction in the application journey, before the credit model even runs.
What RBI Is Doing on AI and Digital Lending
RBI has not been silent on this. In August 2025, the FREE-AI committee, formed to study responsible and ethical AI adoption in finance, submitted its report. Building on that, RBI released a draft "Guidance on Regulatory Principles for Model Risk Management" in 2026, open for public comment until July 24, 2026, applying to banks, NBFCs, credit information companies, and any regulated entity using third-party or machine learning models. This is the first real attempt to formally bring AI and ML lending models under the same governance scrutiny as traditional financial models, covering the full model lifecycle rather than just outcomes at launch.
That is meaningful progress, but it is still principles-based guidance, not an enforceable fairness testing mandate. There is no requirement yet that lenders publish disaggregated approval rates by gender, geography, or income band, the kind of transparency that would let anyone outside the lender actually verify whether a model is excluding people systematically.
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What would actually close the gap
A few things would move this from good intentions to measurable fairness.
- Disaggregated disclosure. Lenders using AI-based scoring should be required to publish approval and interest-rate outcomes segmented by gender and geography, not just aggregate portfolio numbers, so patterns of exclusion become visible rather than anecdotal.
- Proxy audits, not just input audits. Checking whether gender is a direct input misses the point. Regulators and lenders need to test whether combinations of "neutral" variables reconstruct protected characteristics anyway.
- Human review for borderline rejections. A model's "insufficient data" rejection for a rural or informal applicant should trigger a manual review pathway rather than an automatic decline, especially for small-ticket loans where the cost of a second look is low.
- Financial and digital literacy investment alongside the tech. If digital footprint size determines creditworthiness, then expanding who has a rich, legitimate digital footprint, through UPI adoption drives, GST formalisation support for small vendors, and vernacular-first fintech apps, does more for inclusion than any model tweak.
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News4Bharat POV
AI credit scoring in India is not a story of good technology versus bad technology. It is a story of a real financial inclusion problem being tackled by tools that inherit the very inequalities they were built to solve. The RBI's model risk management push is a genuine step forward, but until fairness testing becomes mandatory and disaggregated rather than voluntary and aggregate, the people most in need of formal credit, women running small businesses, informal workers, rural applicants, remain the ones an algorithm is most likely to quietly pass over.
