Gujarat Police Uncover Rs 2,289 Crore Cyber Fraud Network, Target 913 Mule Accounts Under 'Op Mule Hunt 1.0'

(MENAFN- IANS) Gandhinagar, June 1 (IANS) A coordinated statewide crackdown on cyber-enabled financial crime in Gujarat has led to the exposure of Rs 2,289 crore in fraud linked to mule bank accounts, alongside hundreds of arrests and large-scale enforcement action across multiple agencies.

The operation, from December 1 to December 31, 2025, named 'Operation Mule Hunt 1.0', was conducted by the Cyber Centre of Excellence of the Gujarat Police with the involvement of police units across districts, including local crime branches, cyber police stations, and supervisory officers at commissionerate and range levels

Officials said the drive was aimed at identifying and disabling“mule accounts”, which are bank accounts used to receive, transfer or launder money obtained through cyber fraud.

According to the police definition provided during the operation, a mule account is typically operated by an individual who may knowingly or unknowingly allow their account to be used by cybercriminals.

These accounts are used as transit points to move illicit funds across multiple layers, making detection and recovery more difficult.

As part of the enforcement action, authorities registered 565 FIRs, carried out 638 arrests, and initiated proceedings against 913 mule accounts.

Investigators also identified 4,052 cybercrime-linked cases, including 491 cases within Gujarat, based on consolidated intelligence inputs.

The operation relied heavily on data integration and inter-agency coordination. Inputs were collected from the Indian Cyber Crime Coordination Centre (I4C), the National Cyber Crime Reporting Portal (NCRP), the Samanvay portal, and the national cybercrime helpline 1930.

District-level nodal officers were assigned to coordinate field response teams, while banks were brought into a structured framework for real-time sharing of transaction-related alerts and account information.

Officials stated that this combined approach of data intelligence and institutional coordination enabled targeted action against mule account networks operating across different states.

The financial impact of the operation, as reported by authorities, includes a significant reduction in suspicious transaction activity in the banking system.

Cheque-based withdrawals reportedly declined by 75 per cent, while monthly cheque withdrawals fell from Rs 126 crore to Rs 25 crore, reflecting an 80 per cent reduction.

First-layer mule accounts, where fraudulent funds are initially deposited before being routed further, decreased by around 30 per cent between August and December 2025.

ATM withdrawals also recorded a 66 per cent decline between September and December 2025.

Alongside enforcement, regulatory institutions are preparing to strengthen technological monitoring of suspicious financial activity.

The Reserve Bank of India (RBI) has proposed the use of artificial intelligence-based systems to identify and classify transactions by risk level.

Under the framework being developed, transactions will be categorised as low, medium or high risk to assist banks in detecting potentially fraudulent activity at an early stage.

The system is being implemented with the involvement of the Indian Digital Payment Intelligence Corporation (IDPIC), which has been designated as the nodal agency.

A dedicated registry, mulehunter, has also been created to enable information-sharing between banks regarding suspected mule accounts and related entities.

Officials said the initiative is part of a broader national effort to address cybercrime risks that have expanded alongside rapid digital adoption and the growth of online financial systems.

They noted that the increase in digital transactions has required stronger coordination between law enforcement agencies, financial institutions and regulatory bodies to prevent misuse of banking channels.

The operation is expected to feed into continuing efforts to strengthen preventive monitoring systems and improve real-time detection of cyber fraud networks operating through financial intermediaries.

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