10 Districts Raise Mule-Account Alarm, Here's What Did V-KYC Checks Uncover?

New Delhi: A group of districts has emerged as a warning sign for banks monitoring mule accounts, according to an analysis of video know-your-customer (V-KYC) data by fraud prevention firm IDfy. The findings point to fraud networks before complaints or police action bring them into public view.

Where are the risks concentrated?

The study flagged 10 districts: eight in Uttar Pradesh, one in Haryana and one in Rajasthan. It examined V-KYC onboarding data from around 130 districts between April 2025 and June 2026.

Lakhimpur Kheri recorded the highest V-KYC rejection rate at 14.03 percent, followed by Bareilly at 13.37 percent and Varanasi at 12.32 percent. Saharanpur registered 10.32 percent, Muzaffarnagar 9.77 percent and Panipat in Haryana 9.52 percent.

The remaining districts were Firozabad at 8.81 percent, Jodhpur in Rajasthan at 7.61 percent, Lucknow at 6 percent and Ghaziabad at 5.91 percent.

What do the rejections reveal?

A V-KYC application may be rejected when automated checks or a live agent detect impersonation, altered documents, prompting by another person or suspicious behaviour. A higher rejection rate is a risk indicator; it does not establish that every applicant or account in a district is fraudulent.

Mule accounts are used to receive and transfer illicit funds, making early detection important for banks and customers. IDfy said some flagged locations may be established hotspots, while others could be developing into clusters.

How early is the warning?

According to IDfy, its analysis identified about 16 district-level fraud hotspots each quarter during FY26. The firm said 85-90 percent of these were later corroborated by law-enforcement action or news reports.

In cases subsequently verified, V-KYC signals appeared as much as two months before public reporting. IDfy also said its forecast preceded reporting by the Indian Cyber Crime Coordination Centre, or I4C, in 33 percent of regions.

The findings suggest banks could use onboarding patterns alongside transaction monitoring and other checks to investigate emerging clusters sooner. They also underline the need to verify suspicious activity before drawing conclusions about individuals or entire districts.