An innovative artificial intelligence based model called the ‘Mule Hunter.ai’ has been developed by the Reserve Bank innovation hub in Bengaluru. Governor Shaktikanta Das said that this new AI based system will help banks in reducing instances of digital fraud.

Mule accounts are bank accounts that are used to launder proceeds of crime by fraudsters. In an age when there are strict laws on holding or using large amounts of cash, transactions in the banking system cannot be avoided.

What did the RBI say about MuleHunter.Ai

In a press release dated December 6, 2024, RBI said: “AI solutions to identify mule bank accounts MuleHunter.AI The Reserve Bank has been taking various measures in coordination with banks and other stakeholders to prevent and mitigate digital frauds in the financial sector. These include RBI guidelines to regulated entities for strengthening cybersecurity, cyber fraud prevention and transaction monitoring. Use of money mule accounts is a common method adopted by fraudsters to channel proceeds of frauds. The Reserve Bank is currently running a hackathon on the theme “Zero Financial Frauds” which includes a specific problem statement on mule accounts, to encourage development of innovative solutions to contain the use of mule accounts. Another initiative in this direction is the AI / ML based model called MuleHunter.AI, being piloted by Reserve Bank Innovation Hub (RBIH), a subsidiary of Reserve Bank. This model enables detection of mule bank accounts in an efficient manner. A pilot with two large public sector banks has yielded encouraging results. Banks are encouraged to collaborate with RBIH to further develop the MuleHunter.AI initiative to deal with the issue of mule bank accounts being used for committing financial frauds.According to Sheetal R Bhardwaj, executive member of Association of Certified Financial Crime Specialists (ACFCS), “A money mule fraud involves using someone’s bank account to transfer or move illegally acquired money on behalf of others. Fraudsters recruit individuals, often through enticing job offers or romantic relationships, to open or lend their bank accounts. These accounts are then used to receive and transfer illicit funds, making it harder for law enforcement to trace the money back to the criminals. The recruited individuals, known as money mules, may or may not be aware that they are aiding in illegal activities.”

According to Vikram Gidwani, BioCatch South Asia Business Head and Fraud Risk-Management Expert, BioCatch, “All banks in India have recently been plagued by consumers either opening account Bank accounts for the specific purpose of Money Muling or renting / selling their Bank accounts and chasing easy money. Traditional transaction-monitoring solutions available in the market today, tend to identify money mules only after the critical moment of payments /fund transfers. According to a Forrester report on Enterprise Fraud Management (EFM) and Anti Money Laundering (AML), key stakeholders have reported a drastic increase in global investigation time year over year. This has led to increased operational costs to investigate, report, and shutter money laundering accounts, which typical happens very late in the journey. These traditional solutions are simply not enough to solve the problem at scale.”

How RBI’s MuleHunter.AI can help mitigate digital frauds

According to Ranjan R Reddy, Founder & CEO, Bureau, “The combined efforts of the RBI, RBiH, and collaborating banks to develop Mulehunter.ai have arrived at a critical juncture. The current systems largely rely on legacy rules, generating millions of alerts for potential mule activity. As a result, the investigation process is time-consuming, the trail often runs cold and thus, the catch rate is poor. With its improved ML-powered system, Mulehunter.ai promises greater efficiency and faster mule detection by significantly reducing false alerts. It achieves this by analyzing massive transaction and account-related datasets in near real-time. While it is a powerful tool for detecting mule activity, it remains reactive as fraudsters are already onboarded. To truly disrupt these operations, we need to proactively prevent mules from infiltrating the financial system and that’s where efforts of fraud prevention startups like ours come in. This is a complex problem that requires concerted efforts from various stakeholders. The RBIH’s Mulehunter.AI is a step in the right direction and paves the way for greater collaboration.”

According to Bhardwaj, the MuleHunter AI model developed by the Reserve Bank Innovation Hub (RBIH) uses advanced artificial intelligence and machine learning to detect and curb the use of mule accounts. By analyzing user behavior and identifying anomalies in account activities, this model can proactively detect potential mule accounts before they are used for illegal transactions.

“This helps banks and financial institutions to shut down suspicious accounts promptly, thereby preventing the flow of illicit funds and reducing the incidence of digital fraud. The pilot programs with public sector banks have shown promising results, indicating that widespread adoption of this technology could significantly enhance the financial system’s defenses against fraud,” she said.

According to Bhardwaj, “RBI’s MuleHunter innovation is a significant step toward combating digital fraud in India. Here’s how it can help:

1. Identification of Mule Accounts: MuleHunter focuses on identifying and tracking mule accounts, which are often used to facilitate fraudulent transactions. By analyzing transaction patterns, the system can flag suspicious accounts that are being used to transfer illegally obtained funds.

2. Real-time Monitoring: The innovation enables real-time monitoring of transactions, allowing banks and financial institutions to detect and respond to suspicious activities promptly. This immediate action can prevent further fraudulent transactions.

3. Data Analytics: MuleHunter employs advanced data analytics and machine learning algorithms to assess large volumes of transaction data. This helps in recognizing trends and patterns associated with fraudulent activities, making it easier to preemptively shut down potential scams.

4. Collaboration Among Institutions: The platform encourages collaboration among banks, payment service providers, and law enforcement agencies. Sharing information about identified mule accounts helps create a more comprehensive defense against digital fraud.

5. Customer Awareness: By leveraging insights gained from MuleHunter, banks can educate their customers about potential risks and fraudulent schemes, empowering them to take precautions.

6. Regulatory Compliance: MuleHunter aids financial institutions in complying with regulatory requirements related to anti-money laundering (AML) and combating the financing of terrorism (CFT), thus enhancing the overall integrity of the financial system.

7. User-Friendly Interface: The innovation is designed to be user-friendly for bank staff, allowing them to easily navigate the system and quickly respond to alerts regarding potential fraud.”