• As part of the Reserve Bank of India’s continued efforts to prevent and mitigate digital frauds, an innovative artificial intelligence-based model called ‘MuleHunter.ai’ has been developed by the Reserve Bank Innovation Hub (RBIH) in Bengaluru.

• Use of money mule accounts is a common method adopted by fraudsters to channel proceeds of frauds. 

• This model enables detection of mule bank accounts in an efficient manner. 

• This will help the banks to deal with the issue of mule bank accounts expeditiously and reduce digital frauds.

What is a mule account?

• A significant challenge in preventing financial fraud is the use of money mule accounts. 

• It is a bank account used by criminals to launder illicit funds, often set up by unsuspecting individuals lured by promises of easy money or coerced into participation. The transfer of funds through these highly interconnected accounts make it difficult to trace and recover the funds.

• A money mule is a term used to describe someone who receives and moves money that came from victims of fraud. Some money mules know they are assisting with criminal activity, but others are unaware that their actions are helping fraudsters.

• Trans-national criminals create illegal digital payment gateways using mule/rented accounts. These illegal infrastructure facilitating money laundering as a service are used for laundering proceeds of multiple nature of cybercrimes.

• Indian Cybercrime Coordination Center (I4C), under the Ministry of Home Affairs, has advised citizens not to sell/rent their bank accounts/company registration certificate/Udyam Aadhaar Registration certificate to anyone. Illicit funds deposited in such bank accounts can lead to legal consequences, including arrest.

• The RBI has been taking various measures in coordination with banks and other stakeholders to prevent and mitigate digital frauds in the financial sector. 

How MuleHunter.ai works?

• RBIH conducted extensive consultations with banks to understand the existing methods and processes employed to identify and report these money mule accounts. 

• The static rule-based systems used to detect mule accounts result in high false positives and longer turnaround times, causing many such accounts to remain undetected.

• RBIH has developed an in-house AI/Machine Learning-based solution which is better suited than a rule-based system to identify suspected mule accounts. 

• Advanced machine learning algorithms can analyse transaction and account detail related datasets to predict mule accounts with higher accuracy and greater speed than typical rule-based systems. 

• This machine learning-based approach has enabled the detection of more mule accounts within a bank’s system.

• 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.

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