Top 10 Mule Account Fraud Hotspots in UP: 8 Districts Highlighted as High-Risk

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An examination of video-KYC data across North India has identified ten districts as areas with heightened risk for mule account activity tied to cyber fraud and illicit financial transfers, with eight of these locations situated in Uttar Pradesh.

10 Major Mule Account Fraud Hotspots Identified, 8 Districts in UP Among High-Risk Areas

Elevated Rejection Rates Detected in Eight Uttar Pradesh Districts

The research reveals that specific northern districts exhibit higher-than-average rejection rates during video-based identity verification, signaling potential fraud risks. Lakhimpur Kheri recorded the highest rejection rate at 14.03 percent, followed by Bareilly at 13.37 percent and Varanasi at 12.32 percent. Other districts with notable rejection rates include Saharanpur (10.32 percent), Muzaffarnagar (9.77 percent), Panipat in Haryana (9.52 percent), Firozabad in Uttar Pradesh (8.81 percent), Jodhpur in Rajasthan (7.61 percent), Lucknow (6 percent), and Ghaziabad (5.91 percent).

Analysts clarified that these rejection figures do not confirm criminal activity among residents of these areas. Instead, they indicate potential red flags during identity checks, such as suspected impersonation, document manipulation, third-party influence, or irregular applicant behavior. These districts are classified as emerging risk zones rather than definitive hotspots for organized crime.

Mule Accounts Function as Intermediaries in Cybercrime Networks

Mule accounts function as intermediaries in cybercrime networks, often established when individuals are enticed by financial incentives to surrender banking credentials. This enables fraudsters to distribute illicit funds across multiple accounts, obscuring the source of transactions.

Rapid Shifts in Fraud Activity Across Regional Boundaries

The data shows that risk levels in specific districts tend to fluctuate quickly, with criminal operations relocating to avoid detection. Over a 15-month period, 81 percent of district-level risk spikes lasted only one month, while 6.5 percent remained elevated for three months or more. When risk indicators declined in one area, a new cluster emerged within the same state in 39 percent of cases, typically within an average distance of 190 kilometers. This mobility underscores the limitations of relying solely on historical hotspots for fraud prevention.

A decrease in suspicious activity in one location often reflects geographic movement rather than the elimination of criminal networks. Financial institutions must monitor onboarding traffic in adjacent districts to address evolving threats. Additionally, previously identified risk zones can reactivate after periods of dormancy.

Early Onboarding Metrics Provide Advanced Warning System

To distinguish isolated anomalies from coordinated fraud, the analysis utilized a three-tier framework requiring minimum sample sizes, baseline fraud volumes, and Z-score rankings. A district had to record at least 30 rejected sessions in a single month to be included in the study. The findings demonstrate how early onboarding data can serve as an operational early-warning tool alongside government and law enforcement efforts.

However, researchers emphasized that automated verification systems cannot replace formal investigations. The study also highlights the importance of public awareness in preventing account compromise. Individuals are urged to avoid sharing passwords, one-time passwords, or account access in exchange for promised financial gains.