No bureau file to read. Alternate data says this borrower is creditworthy today.
Bureau scores are backward-looking by design and silent on 300 million Indians with no credit history. The signals that predict default, first-payment failure and over-leveraging exist at the moment of application — in the device, the digital footprint, the SMS inbox and the behavioural session. Sign3 captures them, calibrates them to your own portfolio, and delivers a risk view that sits orthogonal to bureau.
Indian digital lending is growing at 30%+ CAGR, and the borrowers driving that growth are disproportionately new-to-credit, thin-file, or gig-economy workers with no bureau history. For these segments the traditional infrastructure returns no score — and the absence of a score is treated as grounds for decline.
The cost extends beyond the individual rejection. It is market share conceded to institutions that have found alternative means to assess them. Meanwhile the risks bureau does track are months stale: a borrower who has taken on three new obligations this week still reflects last month's score. The gap between reported data and actual behaviour widens with every reporting cycle.
For each: the challenge, why current systems fail, how Sign3 addresses it — and what it measured on a production portfolio.
300 million Indians carry no bureau score. An application from this segment returns no data, and the underwriting model defaults to decline. The borrower is not necessarily high-risk; the scoring infrastructure is simply unable to evaluate them.
Bureau-dependent scorecards need a minimum credit history to produce a score. Without one, no score is generated and no decision is possible. The model treats absence of data as presence of risk.
Sign3 builds a credit-relevant profile from alternate data: footprint depth across 100+ platforms, SMS-derived salary regularity and bounce patterns, device price band, location affluence, and application-portfolio analysis — all independent of bureau.
128%
uplift in monthly disbursals with no corresponding increase in default rates.
Lending client
A bureau score reports what a borrower did with another institution's capital, months ago. It does not know the phone number was registered last month, that the SMS inbox holds 12 bounce notifications, or that the same device filed four other applications this week.
Sign3 adds five.
Sign3 captures all of this from the same application data and returns a risk assessment calibrated to your own portfolio outcomes. Two institutions using Sign3 get different models, because their borrowers, risk appetite and products differ. The enrichment signals are universal; the intelligence is institution-specific.
Outcomes from live credit deployments across lending, credit cards, and personal loans.
128%uplift in monthly disbursals with no increase in defaults
Lending client.
65%of NPAs concentrated in the riskiest 5% of scored applicants
Credit card portfolio, 1.46 lakh users.
6.5xfraud capture ratio using phone and email data alone
Payday lender, 150K records.
₹30 Crmonthly disbursal unlocked in the 600–650 CIBIL band
Previously declined. Leading NBFC.
16.85%of an approved portfolio reclassified as elevated-risk
Scored retroactively.
40%improvement in risk identification
With a 60% reduction in decision time, per lending partners.
Indian digital lending is growing at 30%+ CAGR, and the borrowers driving that growth are disproportionately new-to-credit, thin-file, or gig-economy workers with no bureau history. For these segments the traditional infrastructure returns no score — and the absence of a score is treated as grounds for decline.
The cost extends beyond the individual rejection. It is market share conceded to institutions that have found alternative means to assess them. Meanwhile the risks bureau does track are months stale: a borrower who has taken on three new obligations this week still reflects last month's score. The gap between reported data and actual behaviour widens with every reporting cycle.
For each: the challenge, why current systems fail, how Sign3 addresses it — and what it measured on a production portfolio.
300 million Indians carry no bureau score. An application from this segment returns no data, and the underwriting model defaults to decline. The borrower is not necessarily high-risk; the scoring infrastructure is simply unable to evaluate them.
Bureau-dependent scorecards need a minimum credit history to produce a score. Without one, no score is generated and no decision is possible. The model treats absence of data as presence of risk.
Sign3 builds a credit-relevant profile from alternate data: footprint depth across 100+ platforms, SMS-derived salary regularity and bounce patterns, device price band, location affluence, and application-portfolio analysis — all independent of bureau.
128%
uplift in monthly disbursals with no corresponding increase in default rates.
Lending client
A first-payment default is the earliest confirmation that the origination decision was wrong — and by then the money is out the door. The signals that would have predicted it were available at application, but never entered the credit decision.
Bureau scores reflect historical repayment, not current financial condition. A borrower whose salary credits have stopped, who has 12 EMI bounces in 60 days and has registered on 8 lending apps this month stays invisible to a backward-looking score.
SMS Parser captures real-time financial behaviour at application: salary regularity, bounce patterns, UPI velocity, recent disbursals. Combined with device and footprint signals, Sign3 scores FPD risk before disbursal — months before bureau would surface it.
65%
of NPAs concentrated in the riskiest 5% of scored applicants.
Credit card portfolio, 1.46 lakh users
A borrower takes on multiple obligations across lenders within days. Each lender sees only its own application. Bureau reporting lags two to three months, so by the time the pattern is visible several lenders have already disbursed to the same over-leveraged individual.
Reporting cycles create a structural blind spot. A loan disbursed on Monday does not appear in a bureau pull on Wednesday. Three lenders can disburse within a week, each unaware of the others.
SMS Parser identifies disbursal and EMI obligation messages in real time; Digital Footprint detects presence across multiple lending platforms. Together they surface the stacking pattern at the next application, not three months later.
0.230
Information Value for presence on 5+ lending platforms — a strong standalone predictor of credit stress.
Fintech over-indexing feature
Borrowers already on the book were assessed at origination. Their risk has since moved: some are over-leveraged, some have lost income, some were marginal approvals the model got wrong. The risk sits inside the portfolio, undetected until delinquency.
Post-disbursal monitoring depends on bureau refreshes and payment history — both lagging indicators. Early warnings such as behavioural drift, device changes and new stacking live outside the systems watching the book.
Sign3 scores an existing portfolio retroactively. Historical applicant data (phone, email, device, outcome labels) runs through the signal modules and returns a risk classification for every borrower, surfacing concentrations invisible at origination.
16.85%
of an already-approved portfolio reclassified as elevated-risk when scored retroactively.
Lending client
Internal credit models hit diminishing returns once the feature set is exhausted. Bureau-derived variables are optimised to their practical limit; the scorecard is stable but no longer improving. Further lift needs orthogonal data, not more tuning.
Bureau supplies the same feature set to every institution in the market, so differentiation through it is structurally constrained. Building proprietary alternate-data pipelines takes years of investment and ongoing maintenance.
Sign3 delivers 500+ raw features per applicant, consumable directly into an existing scorecard or BRE: WhatsApp absence (IV 0.319), lending app count (IV 0.230), phone vintage, email depth, device price band, location affluence — orthogonal lift with no rebuild.
6.5x
fraud capture ratio using phone and email data alone, with alternate features outperforming most bureau features on NTC.
Payday lender, 150K records
A bureau score reports what a borrower did with another institution's capital, months ago. It does not know the phone number was registered last month, that the SMS inbox holds 12 bounce notifications, or that the same device filed four other applications this week.
Sign3 adds five.
Sign3 captures all of this from the same application data and returns a risk assessment calibrated to your own portfolio outcomes. Two institutions using Sign3 get different models, because their borrowers, risk appetite and products differ. The enrichment signals are universal; the intelligence is institution-specific.
Outcomes from live credit deployments across lending, credit cards, and personal loans.
128%uplift in monthly disbursals with no increase in defaults
Lending client.
65%of NPAs concentrated in the riskiest 5% of scored applicants
Credit card portfolio, 1.46 lakh users.
6.5xfraud capture ratio using phone and email data alone
Payday lender, 150K records.
₹30 Crmonthly disbursal unlocked in the 600–650 CIBIL band
Previously declined. Leading NBFC.
16.85%of an approved portfolio reclassified as elevated-risk
Scored retroactively.
40%improvement in risk identification
With a 60% reduction in decision time, per lending partners.
We'll score it, surface the fraud patterns your current stack missed, and walk you through what we found. Your data, our scoring. No commitment, no integration, no decision required until you've seen the result.