Data consistency
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.
Free trial credits included · Full API documentation
Sample responseLIVE API · <1S
Input nameR. Sharma
PredictedMale - 97.2%
Application statesMale ✓
ConsistencyMatch
✓ 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
Send the name
One API call with the applicant's name.
Predict
The model returns predicted gender with a confidence score.
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.
Run your first GenderDetect check in under a minute.
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