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Financial Services · Credit risk scoring

Your bureau score rejects borrowers your data would approve

Bureau scores were built for salaried borrowers with long credit histories. Digital lenders and NBFCs serving gig workers, self-employed customers, and first-time borrowers need a different model — one built on signals that actually predict repayment for that segment.

Banao builds custom ML credit-risk scoring on alternative data — UPI transaction patterns, GST filings, bank-statement cash flows, and employment verification — with an explainability layer so every decline carries a reason your compliance team can defend.

What a Banao credit-risk deployment includes

A credit model is not useful without integration, explainability, and a monitoring layer. We build all four — and hand you the data pipeline as a deliverable, not a prerequisite.

Alternative-data feature engineering

We extract repayment-predictive signals from UPI history, GST filings, bank statements, and employment data — inputs that tell you whether a thin-file applicant is creditworthy without relying on bureau scores that have nothing to say about that segment.

Custom scorecard for your segment

The model is trained on your own loan book — your product type, your borrower segment, your default definition. A generic industry model applied to your portfolio is optimised for someone else's risk profile.

Explainability layer for compliance

Every decision — approval, decline, or referral — carries a reason code traceable to model inputs. Your compliance team can defend each call, and the RBI audit trail is part of the build, not an afterthought.

Bureau integration and blended scoring

Where bureau data exists, we blend it with alt-data signals rather than replacing it. For thick-file applicants the bureau score anchors the prediction; for thin-file applicants the alt-data carries it.

Model drift monitoring and retraining triggers

Credit populations shift faster than most teams expect. We build Gini and PSI monitoring that flags when predictive power is degrading, with automated alerts before default rates move in your book.

Loan origination system integration

The score plugs directly into your LOS — whether a major platform or a custom-built system — with a decisioning API your product team can call in milliseconds at application time.

Where credit-risk models are already running

Metrics shown dotted (··) are being finalised in our case-study metrics pack — we publish numbers only once verified. Some clients in this vertical are described without identification, as their contracts require.

A digital NBFC lender

Alt-data scoring opened a thin-file segment the bureau could not reach

  • ··%approval rate on previously unscored applicants
  • ··%lower default rate vs. previous cut-off policy
  • ··daysreduction in average decision time

The lender's bureau-only policy declined or referred the majority of its target segment — gig workers and self-employed applicants with thin credit files. Banao built a scoring model on UPI transaction history and bank-statement cash-flow signals, with an explainability layer for each decline. The pipeline runs inside the lender's VPC.

We run our own company on the AI we sell

Banao operates a ~300-person engineering company on its own AI products before any client sees them. InterviewGod screens our own hires. Vikaas runs our own demand generation — and those systems carry the same audit logs, access controls, and monitoring we build into every BFSI engagement.

A credit-risk model that cannot survive production pressure is not production-ready. We know what it takes to keep a model honest under real traffic, because we depend on our own every working day.

  • InterviewGodScreens Banao's own engineering hires every week.
  • VikaasRuns Banao's own demand-gen pipeline end to end.

When AI credit scoring is the wrong call

Credit scoring is one of the highest-stakes AI decisions a lender makes. We will tell you when it is not the right approach before you build:

  • Insufficient default history: a model trained on fewer than a few thousand resolved loans in your segment will generalise poorly. If your book is young, the Discovery Sprint establishes whether your data is sufficient or whether staged collection is the right first step.
  • Segment mismatch: a model trained on unsecured personal loans does not transfer cleanly to MSME or vehicle lending. If you want to cover multiple segments, each needs its own training set — we will scope that before quoting.
  • Regulatory uncertainty: if your regulator has not yet published guidance on alt-data credit scoring for your product type, deploying first can create compliance debt. We raise this in week one.
  • Existing bureau coverage is sufficient: for thick-file urban salaried segments where bureau scores are accurate and widely available, a custom model may not improve approval rates enough to justify the build cost. We will say so.

How we start — prove predictive lift before you build

We do not quote a credit model build from a product description. We test predictive lift on your own loan book first.

  1. AI Discovery Sprint2 weeks · fixed price

    We analyse a sample of your resolved loans, test whether alt-data signals add predictive lift over your current cut-off policy, and hand back a Gini improvement estimate and ROI model in basis points — yours to keep either way. If you proceed, the Sprint fee is credited against the build.

  2. Build

    Feature engineering on your alt-data sources, model training on your loan book, compliance architecture review with your CISO, and a decisioning API integrated with your LOS. The data pipeline is a deliverable, not a prerequisite.

  3. Production & monitoring

    Deployed scoring pipeline with Gini and PSI drift monitoring, an audit trail for every decision, and a model-performance dashboard your credit head and compliance team actually open. Retraining is scheduled on your data cadence, not ours.

Frequently asked questions

Typically a few thousand resolved loans — approved and declined, with repayment outcomes — in your target segment. The Discovery Sprint tests whether your existing book is sufficient or whether augmentation can bridge the gap.

UPI transaction history, bank-statement cash-flow patterns, GST filing regularity, employer and payroll verification, and device or app-usage signals where available and consented. The Discovery Sprint identifies which signals are predictive for your specific borrower segment.

Every decline carries the top reason codes traceable to model inputs — not just a score. We build this into the LOS integration so your compliance teams have the documentation regulators and appellants ask for.

Yes. Blended scoring — bureau score where available, alt-data model for thin-file applicants, combined signal for borderline cases — is the standard architecture. We do not ask you to replace existing policy; we extend it to the segments it cannot currently reach.

We build Gini and Population Stability Index monitoring into the deployment. When predictive power or score distribution drifts past set thresholds, the system flags for review before default rates move in your book. Retraining cadence is set by your data volume and the volatility of your borrower mix.

Find out whether your loan book supports a better scoring model

In two weeks we will test whether alt-data signals add predictive lift on your resolved loans — and hand you the ROI model in basis points, yours to keep regardless.

Book a 45-min scoping call