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One more way to catch applications that don't add up.

Predict gender from names using advanced algorithms, and cross-check it against the gender stated on the application - a quiet but effective consistency signal in fraud screening.

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Free trial credits included · Full API documentation

Sample responseLIVE API · <1Minute
Input nameR. Sharma
PredictedMale - 97.2%
Application statesMale ✓
✓ VerifiedAPI docs →
What you get

GenderDetect: built for decisions, not just data

Advanced name algorithmsTrained on Indian naming patterns for accurate prediction across regions and languages.
Consistency checkingMismatches between predicted and stated gender surface applications worth a second look.
Zero-friction signalRuns invisibly on data you already collect - no extra step for the applicant.
How it works

How GenderDetect works in three steps

01 Send the name

One API call with the applicant's name.

02 Predict

The model returns predicted gender with a confidence score.

03 Compare

Match against the stated application data and flag inconsistencies.

FAQ

Before you integrate GenderDetect

How should the result be used?

As one consistency signal among many - a mismatch is a prompt for review, never an automatic decision on its own.

Does it work with initials or short names?

It works best with full first names; confidence scores reflect how much signal the name carries.

Is it included in other products?

Yes - the Advanced Bank Statement Analyzer runs it as part of the full check suite.

Works well with

Chain GenderDetect into a full verification flow

Advanced Bank Statement AnalyzerGender validation inside the full credit file.Explore →
PAN Validator AdvanceName-match scoring against PAN records.Explore →
WhatsApp AuthenticationAnother low-friction consistency check.Explore →

Run your first GenderDetect check in under a minute.

Free trial credits included · Full API documentation

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