The squeeze
The board wants disbursal volume up. The risk committee wants NPAs down. Every quarter, both targets tighten. The room to get a decision wrong shrinks while the pressure to decide faster grows.
Loan applicant
DeviceTrusted
BehaviourGenuine
LocationConsistent
Digital footprintAged
Approve more. Default lessExplain every decision. One intelligence layer makes all three possible.
Bureau tells you the past. KYC confirms the paperwork. Neither tells you who the borrower actually is right now. Sign3 builds a 360-degree customer persona from signals bureau can't see, calibrated to your portfolio, so you grow the book, control the risk, and defend every call to the board.
A lender's margin lives in the gap between approving more and losing less. Most stacks force a trade-off
Four pressures every lending team is carrying at once.
The bureau gap
300 million Indians have no score at all. For the rest, the score is months stale. A borrower who stacked three new loans this week still shows last month's number. The risk that matters is in the present.
The onboarding gap
Lending moved online. The decisioning stack didn't fully follow. Agents fill forms in Tier 2/3 markets. Selfies are assisted. Devices are shared. The onboarding flow was built for a genuine applicant sitting alone with their phone. That's not always who shows up.
The segment you can see but can't serve
The opportunity is clear. Millions of creditworthy Indians your product was designed for. But your underwriting model can't score them, so you either reject them or price the risk so high you lose them anyway.
A lender needs decisions that grow the book, hold the loss rate, and stand up in a board review — at the same time.
Device, footprint, location, image and SMS, on every applicant
Approve thin-file borrowers with confidence
Alternate data signals from device, footprint, location, and SMS that give you a credit read on the borrower bureau can't score. 128% disbursal uplift with no rise in defaults.
The decisions it sharpens
The decisions a lending team makes every day, mapped to Sign3's intelligence layer.
A lender needs decisions that grow the book, hold the loss rate, and stand up in a board review — at the same time.
Catch fraud before disbursal
Synthetic identities, agent-assisted applications, and multi-accounting surfaced at the point of application, before a rupee moves. 78% of fraudsters concentrated in the riskiest 5%.
Predict defaults before they hit the book
FPD and early delinquency risk scored from behavioural and SMS signals at onboarding, months before bureau would reflect it. 65% of NPAs identified in the riskiest 5% of scored users.
Continuous borrower monitoring
The borrower you approved six months ago isn't the same borrower today. Device, behavioural, and SMS signals that track how risk evolves after disbursal, not just at origination.
One platform across onboarding, underwriting, monitoring, and collections risk
The three suites map directly onto a lender's lifecycle, all reading from one customer graph.
Scores every applicant across device, footprint, location, image, and SMS before disbursal, catching fraud and predicting default with signals bureau can't see, calibrated to your own portfolio.
The modules that matter most for a lender: digital footprint and SMS for credit signals, device for fraud rings, image for agent-assisted fraud, location for address and affluence.
The proof, from lending deployments
Measured across live NBFC, lending and credit-card deployments in India.
128%uplift in monthly loan disbursals
With no rise in defaults (lending client).
65%of NPAs in the riskiest 5% of applicants
Credit card portfolio, 1.46 lakh users.
78%of fraudsters in the riskiest 5% of the pool
Personal loan provider.
6.5xfraud capture ratio using phone and email alone
Payday lender, 150K records.
A lender's margin lives in the gap between approving more and losing less. Most stacks force a trade-off
Four pressures every lending team is carrying at once.
The squeeze
The board wants disbursal volume up. The risk committee wants NPAs down. Every quarter, both targets tighten. The room to get a decision wrong shrinks while the pressure to decide faster grows.
The bureau gap
300 million Indians have no score at all. For the rest, the score is months stale. A borrower who stacked three new loans this week still shows last month's number. The risk that matters is in the present.
The onboarding gap
Lending moved online. The decisioning stack didn't fully follow. Agents fill forms in Tier 2/3 markets. Selfies are assisted. Devices are shared. The onboarding flow was built for a genuine applicant sitting alone with their phone. That's not always who shows up.
The segment you can see but can't serve
The opportunity is clear. Millions of creditworthy Indians your product was designed for. But your underwriting model can't score them, so you either reject them or price the risk so high you lose them anyway.
A lender needs decisions that grow the book, hold the loss rate, and stand up in a board review — at the same time.
Device, footprint, location, image and SMS, on every applicantApprove thin-file borrowers with confidence
Alternate data signals from device, footprint, location, and SMS that give you a credit read on the borrower bureau can't score. 128% disbursal uplift with no rise in defaults.
The decisions it sharpens
The decisions a lending team makes every day, mapped to Sign3's intelligence layer.
A lender needs decisions that grow the book, hold the loss rate, and stand up in a board review — at the same time.
Catch fraud before disbursal
Synthetic identities, agent-assisted applications, and multi-accounting surfaced at the point of application, before a rupee moves. 78% of fraudsters concentrated in the riskiest 5%.
Predict defaults before they hit the book
FPD and early delinquency risk scored from behavioural and SMS signals at onboarding, months before bureau would reflect it. 65% of NPAs identified in the riskiest 5% of scored users.
Continuous borrower monitoring
The borrower you approved six months ago isn't the same borrower today. Device, behavioural, and SMS signals that track how risk evolves after disbursal, not just at origination.
One platform across onboarding, underwriting, monitoring, and collections risk
The three suites map directly onto a lender's lifecycle, all reading from one customer graph.
- At origination
ScreenX
Scores every applicant across device, footprint, location, image, and SMS before disbursal, catching fraud and predicting default with signals bureau can't see, calibrated to your own portfolio.
- On the book
Cortex
Monitors every active borrower for behavioural drift, loan stacking, device changes, and early warning signals that predict delinquency before it shows up in DPD buckets.
- In operations
Escalation
Assembles investigation cases when fraud is confirmed or a regulatory trigger fires, with full signal trail, linked accounts, and audit-ready documentation.
The modules that matter most for a lender: digital footprint and SMS for credit signals, device for fraud rings, image for agent-assisted fraud, location for address and affluence.
The proof, from lending deployments
Measured across live NBFC, lending and credit-card deployments in India.
128%uplift in monthly loan disbursals
With no rise in defaults (lending client).
65%of NPAs in the riskiest 5% of applicants
Credit card portfolio, 1.46 lakh users.
78%of fraudsters in the riskiest 5% of the pool
Personal loan provider.
6.5xfraud capture ratio using phone and email alone
Payday lender, 150K records.