The 10 districts identified in the latest analysis are Bareilly, Lakhimpur Kheri, Firozabad, Muzaffarnagar, Saharanpur, Panipat, Jodhpur, Lucknow, Ghaziabad and Varanasi. Lakhimpur Kheri recorded the highest V-KYC rejection rate at 14.03%, followed by Bareilly at 13.37%. Varanasi stood at 12.32%, Saharanpur at 10.32%, Muzaffarnagar at 9.77%, Panipat at 9.52%, Firozabad at 8.81%, Jodhpur at 7.61%, Lucknow at 6% and Ghaziabad at 5.91%.
IDfy said the findings are based on V-KYC onboarding telemetry captured between April 2025 and June 2026, covering approximately 130 districts. The company analysed monthly V-KYC attempts and rejection patterns to identify districts where fraudulent activity showed statistically significant concentrations. Rejections can result from flags involving impersonation, document tampering, third-party prompting or suspicious user behaviour.
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V-KYC rejection rates in 10 emerging fraud hotspots
| District |
V-KYC rejection rate |
|---|
| Lakhimpur Kheri |
14.03% |
| Bareilly |
13.37% |
| Varanasi |
12.32% |
| Saharanpur |
10.32% |
| Muzaffarnagar |
9.77% |
| Panipat |
9.52% |
| Firozabad |
8.81% |
| Jodhpur |
7.61% |
| Lucknow |
6.00% |
| Ghaziabad |
5.91% |
Source: IDfy whitepaper
Fraud hotspots are moving
The analysis found that fraud risk can shift quickly. In 81% of cases, a district’s elevated risk lasted only one month, while just 6.5% of risk spikes persisted for three months or longer. When one district cooled down, the next hotspot emerged in the same state 39% of the time, with the average movement estimated at around 190 km.
The study also found that previously flagged locations can become active again. In the current quarter, 11 active hotspots had already been flagged earlier in FY26, indicating that a decline in risk does not necessarily mean a district’s vulnerability has disappeared.
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V-KYC offers an early warning
V-KYC data also showed potential as an early-warning tool. IDfy identified around 16 district-level fraud hotspots every quarter in FY26, of which 85-90% were later confirmed through law-enforcement action or news reports. In verified cases, signals emerged up to two months before public reporting.
How IDfy identifies hotspots
The methodology uses statistical and minimum fraud-volume thresholds before ranking districts through a Z-score. A district must record at least 30 rejected calls in a month to qualify for ranking, helping distinguish genuine concentrations from fluctuations caused by small samples.
IDfy said its onboarding telemetry identified 33% more active fraud locations than official daily reporting, although it noted that the early lead is not universal. The findings suggest that monitoring onboarding signals alongside traditional enforcement data could help financial institutions identify emerging fraud clusters and alert neighbouring districts earlier.
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