Mule Accounts Fuel Cyber Fraud; Banks Turn to AI and KYC to Block Illegal Money Trails

Cybercriminal networks are increasingly relying on mule bank accounts to receive, layer and transfer money obtained through online fraud, prompting banks, regulators and law-enforcement agencies to strengthen financial monitoring and deploy artificial intelligence to identify suspicious accounts.

A mule account is a legitimate bank account that is used by another person or criminal network to receive or transfer illegally obtained funds. Some account holders may knowingly provide access in return for money or commission, while others can be deceived into opening accounts without understanding how the accounts will be used.

The Reserve Bank of India has long warned that money mules can be recruited to receive fraud proceeds and transfer them to other accounts, sometimes after retaining a commission. The RBI has also directed banks to strengthen KYC, periodically update customer information and monitor transactions to prevent misuse of accounts. The issue has gained greater importance as cybercriminals increasingly move fraudulent money through several accounts instead of transferring it directly to the final beneficiary. This creates multiple layers in the transaction trail and can make it harder for investigators to identify the people controlling the fraud network.

AI Becomes a New Weapon Against Mule Accounts

India is now using artificial intelligence more aggressively to detect suspicious financial activity. On May 12, 2026, the Indian Cyber Crime Coordination Centre (I4C) under the Ministry of Home Affairs and the Reserve Bank Innovation Hub (RBIH) signed a Memorandum of Understanding to strengthen AI-driven detection of mule accounts.

The partnership is designed to use data from I4C's Suspect Registry with AI-based fraud detection systems to identify hidden mule accounts and help financial institutions take faster action against suspicious activity.

The RBI has also developed MuleHunter.AI, a supervised machine-learning model designed to identify mule bank accounts by analysing patterns of account activity.

According to the RBI, MuleHunter.AI is designed for near-real-time identification of mule accounts and has been tested with large public-sector banks. The system learns patterns associated with mule-account activity and is intended to improve detection compared with conventional methods.

The technology can examine signals such as unusual transaction behaviour and patterns that may indicate that an otherwise legitimate account is being used as part of a larger fraud network.

Banks and FIU-IND Tighten Monitoring

The banking sector is also working with the Financial Intelligence Unit-India (FIU-IND) to strengthen the framework for identifying money mule accounts. Recent discussions between banks and FIU-IND have focused on improving Suspicious Transaction Report (STR) mechanisms and developing sharper indicators for identifying organised fraudulent activity.

Under existing requirements, banks are required to file STRs when transactions raise concerns related to suspicious or potentially fraudulent activity. The enhanced approach is intended to help institutions recognise suspicious patterns earlier and improve financial-crime prevention.

Financial Fraud Risk Indicator Adds Another Layer

The telecom sector is also contributing to the fight against financial fraud through the Financial Fraud Risk Indicator (FRI). FRI is a risk-based system that categorises mobile numbers associated with reported financial fraud according to levels such as medium, high and very high risk.

The system allows relevant risk information to be shared with financial institutions and other stakeholders so that transactions involving potentially risky mobile numbers can trigger additional scrutiny. Government documents say the Department of Telecommunications' Digital Intelligence Platform connects more than 1,400 stakeholders, enabling the sharing of actionable information across institutions.

Real Cases Show How Mule Accounts Work

Investigations across India have exposed how mule accounts can become part of large cybercrime networks. In Mumbai, police recently registered a case against two men accused of operating 22 mule bank accounts allegedly linked to at least 42 cyber fraud cases across the country. The cases involved reported losses of around ₹7.42 crore, according to the investigation. In another major case in Thane, police arrested six people accused of supplying bank accounts to cybercriminals. Investigators detected fraudulent transactions amounting to approximately ₹57.25 crore connected with the accounts under investigation. These investigations demonstrate how a relatively small number of individuals supplying bank accounts can potentially facilitate large-scale cyber fraud networks.

Victims Can Become Part of the Money Trail

Mule-account networks can also affect people who are not directly involved in the original fraud. A fraudster may convince an individual to open an account by promising employment, commission, gaming income or financial assistance. The account may then be used to receive money stolen from another victim.

In other cases, criminals may allegedly collect identity documents or biometric information by promising assistance with government schemes, subsidies or other services. Once the money enters a mule account, it can be rapidly transferred to several other accounts. Investigators then have to reconstruct the transaction chain, identify the recipients and freeze the funds before they are withdrawn or moved outside the banking system.

₹20.30 Crore Digital Arrest Case

One investigation in Mumbai South involved an 86-year-old woman who was allegedly defrauded of ₹20.30 crore in a digital-arrest scam.

During the investigation, police traced around ₹5 crore of the alleged fraud proceeds to a Bengaluru-based company's account. The investigation resulted in the arrest of 13 accused persons and the filing of chargesheets against them. The case illustrates how fraud proceeds can pass through accounts belonging to people or businesses that may not necessarily be the final beneficiaries of the crime.

₹5 Crore Account Used in Uttarakhand Case

Investigators in Uttarakhand also uncovered a case in which a young man was allegedly persuaded to open a bank account after being promised earnings through a gaming website. The account was subsequently used to receive money linked to cyber fraud, with transactions reportedly reaching around ₹5 crore.

The bank blocked the account after receiving a complaint through the cybercrime reporting system, after which the account holder approached police.

Why KYC Remains Critical

Technology-based monitoring can identify suspicious activity after an account is opened, but authorities are also focusing on preventing fraudulent accounts from being created in the first place. Strong Know Your Customer (KYC) procedures, verification of customer identity, periodic updating of customer information and transaction monitoring remain key safeguards.

The RBI has previously emphasised strict adherence to KYC and anti-money-laundering requirements to reduce the misuse of bank accounts by money mules. The challenge becomes greater when criminals obtain identity documents or banking credentials from vulnerable individuals or persuade account holders to hand over debit cards, cheque books or SIM cards.

Supreme Court Also Pushes Action on Mule Accounts

The issue has also reached the Supreme Court. In August 2026, the court issued directions in the wider fight against digital-arrest and cyber-fraud schemes and asked the RBI to share a Standard Operating Procedure (SOP) concerning mule accounts. The move reflects growing concern over the speed at which fraudulent funds can move through the banking system and the need for banks, regulators and law-enforcement agencies to coordinate more closely.

With cybercriminals adapting their methods rapidly, authorities are increasingly combining transaction intelligence, telecom data, KYC information and artificial intelligence to identify suspicious financial networks.