Si01 / Behaviour
The intent to repay.
Read the sequence behind the score. Month-by-month repayment behaviour brings a borrower’s credit history into focus.
Bureau & repayment sequences
Credit intelligence. In every dimension.
See the risk.
In every
dimension.
Intent to repay. Ability to repay. The product in between. See the full risk space—and the complete curve behind every lending decision.
Product lens
Prime · EMISynthetic demo
₹10k₹2L
Within PD ceiling
2.95%PD
Illustrative PD · 8% ceiling · Not a credit offer
Built on the depth of Indian credit.Published research figures
33Maccounts
Loan accounts in training
1.5Bevents
Repayment events in training
0.84AUC
Reported model discrimination
01 / Make the decision visible
The whole curve.
Every product, together.
Keep all three products in view. Move the exposure, choose a borrower cluster or change the PD ceiling to see exactly where each curve crosses it.
◇ Curve intersections mark where the selected PD ceiling is reached.Full probability range. Curves remain visible across the full exposure range.
Prime · Product-conditioned Si and Sa positions feed these same curves. The 3D maps use the selected exposure; this chart shows the whole exposure range.
₹10,000₹2,00,000
How does a score cutoff differ?
For the Prime example, an illustrative bureau cutoff of 700 passes the profile at a score of 771. An income multiplier would suggest ₹1,56,000.
The complete risk curves show a different question: how does the estimated default probability change at every amount, for every product? A PD ceiling is one input to a lending decision, not an approval by itself.
Synthetic profiles and illustrative probabilities. The 3D maps and curves use the same function. Comparisons hold the default definition and horizon constant; credit lines assume fully drawn exposure. Not Sentinel model outputs or lending decisions.
02 / The geometry of credit
Credit has dimensions.
Your decisions should too.
Two continuous borrower axes. A third axis for product categories. Colour expresses PD; the boundary shows the selected ceiling.
Sa02 / Capacity
The ability to repay.
Understand the room to borrow. Cash-flow stability, disposable income and existing obligations shape repayment capacity.
Cash flow & commitments
P03 / Context
The product that fits.
A bullet loan, an EMI and a credit line ask different things of a borrower. Evaluate risk within each product’s structure.
Product, amount & repayment structure
03 / Built for your lending stack
Intelligence in.
A decision you can use.
Bring bureau and cash-flow inputs together. Return product-aware risk and offer parameters your team can evaluate against its own policy.
Product-specific assessments
Risk and capacity in one response
Your policy remains the reference
assessment.json
EXAMPLE product-aware assessment
{
"borrower": "SYNTHETIC_1",
"product": "emi",
"assessment": {
"requested_amount": 100000,
"illustrative_pd": 0.0295,
"policy_ceiling": 0.08,
"within_policy": true
},
"sizing": {
"illustrative_limit": 200000,
"currency": "INR"
},
"simulation": true
}04 / Research behind the engine
Depth in the data.
Clarity in the evidence.
Sentinel is built on BASIC V4. Start with the published results, then evaluate performance on the segment and outcomes that matter to your book.
Request the evaluation methodologyReported AUC0.84
Default concentration in riskiest decile8×
Training loan accounts33 million
Training repayment events1.5 billion
Figures reported on TARA AI Labs’ Sentinel page. They are separate from this demonstration and are not independently validated here. Request the evaluation population, period, outcome definition and baseline as part of a portfolio review.
Your portfolio. A clearer picture.
Find the opportunity
inside your book.
Start with one portfolio segment. Explore where risk, loan sizing and your current policy tell different stories.
Share your lending context. The Sentinel team will follow up with the process and data requirements.
Or book a walkthroughOn the horizonFacial KYC. Identity verification before underwriting.Ask about the roadmap