Bareilly, Uttar Pradesh, has been identified as one of ten Indian districts exhibiting early indicators of rising mule-account and broader financial fraud activity. This assessment comes from an analysis of Video-KYC (V-KYC) onboarding data conducted by IDfy, a leading identity verification and risk-intelligence firm. The findings underscore a dynamic shift in India's fraud landscape, where new hotspots emerge before traditional law enforcement measures catch up.
Key Emerging Fraud Hotspots Identified
IDfy's analysis, spanning from April 2025 to June 2026 and covering approximately 130 districts, pinpointed a cluster of ten districts with statistically significant concentrations of fraudulent activity. These locations are characterized by elevated V-KYC rejection rates, signaling suspicious user behavior, document tampering, or impersonation attempts during the onboarding process.
- Lakhimpur Kheri: 14.03% V-KYC rejection rate
- Bareilly: 13.37% V-KYC rejection rate
- Varanasi: 12.32% V-KYC rejection rate
- Saharanpur: 10.32% V-KYC rejection rate
- Muzaffarnagar: 9.77% V-KYC rejection rate
- Panipat: 9.52% V-KYC rejection rate
- Firozabad: 8.81% V-KYC rejection rate
- Jodhpur: 7.61% V-KYC rejection rate
- Lucknow: 6.00% V-KYC rejection rate
- Ghaziabad: 5.91% V-KYC rejection rate
The Shifting Landscape of Financial Crime
The study highlights the transient nature of fraud risk. In 81% of cases, a district's elevated risk status lasted only a single month, with only 6.5% persisting for three months or longer. Interestingly, when one hotspot cools down, the next often emerges within the same state, approximately 39% of the time, with an average geographical shift of around 190 km. Furthermore, previously flagged locations can reactivate, as evidenced by 11 active hotspots in the current quarter that had been identified earlier in FY26.
V-KYC as an Early Warning System
The data suggests that V-KYC serves as an effective early-warning mechanism. IDfy's telemetry identified an average of 16 district-level fraud hotspots each quarter in FY26. Of these, 85-90% were subsequently confirmed through law enforcement actions or media reports, with signals often appearing up to two months before public disclosure. This proactive identification capability could prove invaluable for financial institutions and regulatory bodies.
IDfy's Hotspot Identification Methodology
IDfy's methodology involves applying statistical and minimum fraud-volume thresholds. A district must record at least 30 rejected V-KYC calls in a month to qualify for ranking, ensuring that identified concentrations are genuine and not merely statistical anomalies from small sample sizes. By using a Z-score, districts are ranked based on the significance of their rejection patterns.
The findings advocate for integrating onboarding signals with traditional enforcement data to enhance the early detection of fraud clusters and facilitate timely alerts to neighboring regions, thereby bolstering India's defenses against evolving financial crimes.