Credit risk assessment in Indian banking has historically relied on a straightforward formula: check the applicant’s CIBIL score, verify income documents, review existing liabilities, and make a decision. For salaried applicants with formal employment records, this works reasonably well. For the vast majority of India’s population, it doesn’t.

An estimated 300-350 million Indians are credit-eligible but lack the formal documentation that traditional scoring models require. They’re self-employed, gig workers, small business owners, agricultural workers, or recently formalised employees. Traditional credit scoring can’t see them.

AI-based credit risk models are changing this. And the shift is already measurable.

What’s Actually Different About AI-Based Scoring

Traditional credit scoring is rules-based. If your CIBIL score exceeds 750, you’re a good risk. Below 650, you’re not. The model considers a limited set of variables — credit history, current debt, repayment record — and applies fixed thresholds.

AI-based models work differently. They analyse hundreds or thousands of data points simultaneously, identifying patterns that predict repayment behaviour without relying on a single credit score. These data points can include:

• Transaction patterns. UPI transaction history, bank account cash flows, spending regularity, and savings behaviour provide a detailed picture of financial stability. A person with steady monthly inflows and consistent savings patterns is a lower risk than their credit score might suggest.

• Digital footprint. Mobile recharge patterns, utility bill payment history, e-commerce purchase behaviour, and app usage data all contain signals. Someone who consistently pays their mobile bill on time and maintains steady utility payments demonstrates financial discipline.

• Business cash flows. For MSME lending, AI models can analyse GST filing data, point-of-sale transaction volumes, and supply chain payment patterns to assess business health more accurately than annual financial statements.

Where Indian Banks Stand Today

Adoption is uneven. Private sector banks and new-age fintech lenders are furthest ahead. HDFC Bank, ICICI Bank, and Kotak Mahindra Bank have all deployed AI-driven credit assessment for specific loan products, particularly personal loans and credit cards.

Fintech lenders like KreditBee, MoneyTap, and Navi have built their entire underwriting processes around AI models. Their approval times — often under five minutes for small-ticket personal loans — would be impossible with traditional assessment methods.

Public sector banks are moving more slowly. State Bank of India has piloted AI-based scoring for MSME loans, and several other PSBs are running trials. But the core lending infrastructure at most public sector banks still runs on rules-based systems, with AI models operating as supplementary inputs rather than primary decision-makers.

Team400 has observed similar patterns in banking systems globally — the technology to deploy AI-based risk models exists and works, but institutional readiness often lags behind technical capability.

The Regulatory Framework

The RBI has taken a measured approach. Its 2024 guidelines on model risk management require banks to document, validate, and regularly audit any AI/ML models used in credit decisions. Banks must demonstrate that their models don’t discriminate based on protected characteristics and that outcomes can be explained to applicants.

This explainability requirement is significant. Many AI models, particularly deep learning models, function as black boxes — they produce accurate predictions but can’t easily explain why a specific applicant was approved or rejected. Banks are gravitating toward more interpretable model architectures, such as gradient-boosted decision trees, that balance accuracy with transparency.

The RBI also requires that AI models not be the sole basis for credit rejection. A human review mechanism must exist for declined applications. This prevents fully automated systems from denying credit without accountability.

What the Data Shows

Early results from banks that have deployed AI-based scoring are encouraging.

HDFC Bank reported a 15% improvement in default prediction accuracy for personal loans after deploying ML-based scoring in 2025. More importantly, the bank approved roughly 12% more applicants who would have been rejected under the old model — applicants who went on to demonstrate good repayment behaviour.

Among fintech lenders, the results are even more dramatic. Lenders using alternative data and AI scoring report that they’ve extended credit to millions of borrowers who had no prior credit history, with non-performing asset rates comparable to or better than traditional unsecured lending portfolios.

The Risks Worth Watching

AI credit models aren’t without risk. Three concerns deserve particular attention.

Data quality. AI models are only as good as their training data. In India, where digital financial history is relatively short for much of the population, models trained on limited data may not perform well during economic stress periods they haven’t observed.

Proxy discrimination. Even without using protected characteristics directly, AI models can learn proxies. A model that penalises applicants from certain pin codes may effectively discriminate by caste or religion. Regular bias auditing is essential.

Model drift. Economic conditions change. A model trained during a growth period may misjudge risk during a downturn. Banks need robust monitoring systems that detect when model performance degrades and trigger recalibration.

Where This Goes Next

The direction is clear. Within five years, AI-based credit assessment will likely be standard across Indian banking, not just for tech-forward private banks and fintechs. The Account Aggregator framework, which enables consent-based data sharing across financial institutions, will provide richer data inputs for AI models.

The banks that invest now in building robust AI scoring infrastructure — with proper governance, bias monitoring, and human oversight — will have a structural advantage in serving India’s enormous underbanked population. The ones that treat AI scoring as optional will find their lending portfolios increasingly limited to the same small pool of formally documented borrowers.

India’s credit gap isn’t a technology problem anymore. The models exist. The data is increasingly available. What remains is the institutional willingness to trust and govern these systems properly.