Rented identity
Valid documents from a real person are used in an application controlled by someone else.
Read the analysisPraman Labs scores whether a real, independent, economically active person stands behind every financial application.
Every check can pass before the loss.
A genuine credential does not prove that the named person controls the application or will operate the account.
- Aadhaar
- Valid
- PAN
- Valid
- Face
- Matched
- Economic actor
- Unresolved
The checks pass and the account opens. The first EMI may never arrive.
Explore the productFive ways a valid identity becomes a false application.
Each pattern passes different checks and leaves a different trail.
Hollow real identity
Every credential is authentic, but the identity has no sustained economic footprint.
Read the analysisRecombinant identity
The application joins valid identity attributes that disagree across credentials.
Read the analysisSeasoned ghost
Small credit lines create a plausible bureau history before a larger application arrives.
Read the analysisFarmed cluster
One operator repeats devices, addresses, faces or beneficiary accounts across many applications.
Read the analysisPersonhood checks across five origination channels.
Praman Labs fits before account opening without replacing the controls each institution already runs.
View all solutionsNBFCs and digital lenders
Flag hollow applicants before first-payment default becomes the signal.
Banks and small finance banks
Add personhood evidence to branch-assisted and digital account opening.
Insurers
Screen policyholder and agent identities before issuance.
Payment aggregators
Test whether a real merchant stands behind an onboarding record.
Brokers and AMCs
Resolve mobile-number and identity contradictions at investor onboarding.
Prove it on your own book before integration.
Send twelve months of application data, hashed inside your environment. We score it offline.
The return file identifies applications flagged as synthetic and calculates the loss linked to that cohort.
Its precision curve maps the result to your team's review capacity.
The scale is already visible.
Reports from RBI and I4C show the scale after accounts open.
₹36,014 crore
Amount involved in banking-sector frauds reported during the year
RBI Annual Report 2024-25, FY 2024-25
32.08 lakh
Layer-1 mule accounts shared through the I4C Suspect Registry
Ministry of Home Affairs, Lok Sabha reply, 30 June 2026
Add one decision before the account opens.
We do not replace KYC, the bureau pull or post-onboarding monitoring. We answer the missing question at application time.
KYC verification
Checks whether documents and credentials are valid.
Bureau pull
Reads repayment history from the available credit file.
Personhood score
Tests whether an independent economic actor is behind the application.
MuleHunter.AI
Reads account behaviour after money begins to move.
A thin file is not a hollow identity.
Production reviews keep thin-file applicants separate from cases with absent economic control.
Book a backtestReview record
- Personhood band
- Thin
- Recommended action
- Step-up verification
- Primary reason
- Thin footprint, no contradictions
Final action remains inside the institution's policy and review process.
Praman Labs
Run it against your own book.
We sign a mutual NDA before any data moves. No integration is required.
Book a backtest