The AI credit-scoring engine that adapts to every borrower.
A governed, explainable probability-of-default engine that reads 5,000+ variables from bureau and alternative data — and models thin-file and thick-file borrowers differently, because they carry risk differently.
Most rejections aren't risk calls. They're blind spots.
Whether a borrower is thick-file or thin, a bureau-only score sees a fraction of the story — and the parts it misses are where good customers and hidden risk both hide.
The thin-file blind spot
New-to-credit and thin-file applicants have little bureau history — so good borrowers get declined for missing data, not real risk.
The thick-file plateau
Even for well-documented borrowers, a bureau score alone plateaus — it can't see current behaviour, stress, or intent.
One model for everyone
New, repeat, prime, sub-prime — scored by a single static model that decays quietly until losses appear.
A PD engine that sees the whole borrower
Enterprise PD Score blends bureau data with consent-based behavioural signals, models each segment on its own terms, and stays explainable end to end. Not a scorecard — a living credit-risk system.
Sees more
Bureau plus device, SMS, telecom, watchlist and cash-flow signals — for every file, thick or thin.
Decides in seconds
Real-time scoring built for straight-through approval at onboarding.
Never goes stale
Monitored for drift and recalibrated, so performance holds in production.
Explains itself
A defensible reason behind every decision — ready for audit and adverse-action.
Six sources beyond the bureau
The bureau is the starting point, not the whole picture. We layer consent-based signals that reveal how a borrower actually behaves — the data that predicts repayment when history alone can't.
Where the edge comes from
Read the borrower, not just the record
Income stability, cash-flow health, distress signals and repayment discipline — drawn from 5,000+ variables across bureau and alternative data. For thin files it builds a picture the bureau lacks; for thick files it adds the live behaviour a static score can't see.
A model per population, not one for all
New-to-credit, repeat, dormant, top-up — each cohort gets its own PD model, because each defaults for different reasons. Prime-to-sub-prime tiers fall out of the score; they're never a crude input.
Stop what the score can't see
Device intelligence and curated watchlist screening — sanctions, PEP, defaulter lists — run before scoring. They catch fraud and ineligible applicants a PD model was never designed to find, matched on PAN + DOB, not name alone.
Many algorithms compete. The best one ships.
We don't bet on a single technique. Enterprise PD Score benchmarks a full suite of machine-learning algorithms on your portfolio, then deploys the one that performs best for your book — under a champion–challenger framework that keeps improving it.
Benchmark
Every candidate algorithm is trained and scored on your data, on identical footing.
Champion selected
The strongest performer on your metrics — Gini, KS, calibration — becomes the live model.
Challengers keep running
New models run in the background and take over only when they beat the champion.
Every model stays explainable — reason codes and transparent logic behind each decision, whichever algorithm wins.
How a PD model earns its place
Every Enterprise PD Score deployment is validated on your own book against the four benchmarks below, then monitored for population stability once it goes live.
One engine. A model for each borrower type.
A single scorecard treats a first-time borrower, a loyal repeat customer, and a dormant one as if they were the same person. They aren't — each defaults for different reasons and shows different signals. Enterprise PD Score builds a dedicated probability-of-default model for every population you can identify at the point of application, so each borrower is scored against the people who actually resemble them — not a one-size-fits-all average.
Little or no bureau history. Scored on consent-based behavioural and cash-flow signals that reveal repayment ability the bureau can't see.
Drivers · alternative data, income stabilityRich bureau history, but a static score plateaus. We add live behavioural signals to sharpen risk ranking beyond what history alone captures.
Drivers · bureau depth + behaviourBorrowed and repaid with you before. Their own repayment track record — often your most predictive signal — drives a sharper, lower-friction score.
Drivers · internal repayment historyA past borrower returning after a long gap. Re-underwritten on current behaviour, because circumstances change while a relationship sits idle.
Drivers · recent activity, fresh signalsAn existing borrower repaying well and ready for more credit. Behavioural repayment patterns identify who to grow — and who to hold.
Drivers · on-book repayment behaviourScreened before scoring. Device intelligence and watchlist checks stop synthetic identities, stacking and sanctioned applicants at the gate.
Drivers · device, watchlist, PAN + DOBPrime, near-prime and sub-prime aren't inputs — they're outputs. Risk tiers fall out of the cohort models rather than being assigned up front, so a borrower is never pre-labelled before the score has actually looked at them.
Built for regulated lending
Consent-first
Every alternative-data signal is permissioned, aligned with RBI digital-lending guidelines and the DPDP Act.
Explainable by design
Reason codes and transparent logic for every decision — ready for audit and adverse-action requirements.
Governed
Versioned, documented and monitored — so your model-risk team and your regulator stay comfortable.
Probability of default scoring, explained
What is a probability of default (PD) model?
A probability of default model estimates how likely a borrower is to default on a loan over a given period. Enterprise PD Score produces this probability using machine learning across 5,000+ variables from bureau and alternative data — giving lenders an explainable, real-time measure of credit risk for every application.
How does alternative data improve credit scoring?
Alternative data — SMS, device, telecom, cash-flow and watchlist signals — reveals repayment behaviour the credit bureau can't see. For thin-file and new-to-credit applicants it builds a risk picture where bureau history is missing; for thick-file borrowers it adds live behaviour a static score can't capture.
Is Enterprise PD Score suitable for NBFCs and digital lenders?
Yes. It's built for banks, NBFCs, credit unions, microfinance institutions and digital lenders, with real-time API decisioning for straight-through loan approval — and it's aligned with RBI digital-lending guidelines and the DPDP Act.
Is the PD model explainable and compliant?
Every decision comes with reason codes and transparent logic, ready for model governance, audit and adverse-action requirements. Alternative-data signals are consent-based and aligned with RBI digital-lending guidelines and the DPDP Act.
How is Enterprise PD Score different from a bureau score?
A bureau score relies on credit history alone. Enterprise PD Score combines bureau data with alternative and behavioural signals, builds a dedicated model per borrower cohort, and selects the best-performing machine-learning algorithm through a champion–challenger framework — so it ranks risk more sharply, especially for borrowers the bureau can't see.
See it on your own book
Start with a low-risk POC: we prove the lift on your bureau data first, then show the incremental gain as we layer in the alternative signals — results at every stage before you commit to the next.
- Email: salesteam@cloudbankin.com
- Sales Enquiries: +91 9080996606
- HR Enquiries: +91 9080996576
After smartphone penetration, people are not watching their SMS at all. They use SMS only for OTP related transactions. That’s it.
But What can a Lender see in your SMS after you consent to them?
Lender can see income, expenses, and any other Fixed Obligation like (EMIs/Credit Card).
1) Income – Parameters like Average Salary Credited, Stable Monthly inflows like Rent
2) Expenses – Average monthly debit card transactions, UPI Transactions, Monthly ATM Withdrawal Amount etc
3) Fixed Obligations – Loan payments have been made for the past few months, Credit card transactions.
It also tells the Lender the adverse incidents like
1) Missed Loan payments
2) Cheque bounces
3) Missed Bill Payments like EB, LPG gas bills.
4) POS transaction declines due to insufficient funds.
A massive chunk of data is available in our SMS (more than 700 data points), which helps Lender to make a credit decision.
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