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.

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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

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

Three steps to a verified answer

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

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 it 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 check in under a minute.

Free trial credits and full documentation from day one.

Start free trial or book a 20-minute call