The Reserve Bank of India (RBI) has introduced an artificial intelligence/machine learning (AI/ML)-based model, MuleHunter.AI, developed by the Reserve Bank Innovation Hub (RBIH), in an attempt to tackle digital frauds. The AI model is also expected to help banks deal with the issue of mule bank accounts, a typical tactic fraudsters use to funnel the proceeds of their fraudulent activities.
“As part of the Reserve Bank’s continued efforts to prevent and mitigate digital frauds, an innovative AI/ML based model, namely, MuleHunter.AI has been developed… This will help the banks to deal with the issue of mule bank accounts expeditiously and reduce digital frauds,” Shaktikanta Das, RBI’s Governor, said in his Monetary Policy statement. Monetary Policy refers to actions taken by a country’s central bank to manage the money supply and influence economic activity.
The use of money mule accounts is a common method fraudsters adopt to channel fraud proceeds, according to the RBI’s statement on Developmental and Regulatory Policies. MuleHunter.AI, being piloted by the RBIH, a subsidiary of RBI, is a model that enables the detection of mule bank accounts.
“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,” the statement said.
The RBI is also running a hackathon on the theme “Zero Financial Frauds,” which includes a specific problem statement on mule accounts to encourage the development of innovative solutions.
MuleHunter.AI is an infrastructure that uses databases from all banks and payment system operators. Based on this broad set of data, its AI engine will be trained and is expected to identify fraud more effectively in the financial system.
“If you look at the number of frauds; (they) are increasing, the amount involved in frauds are increasing, although the number of frauds per transaction is coming down over the years. But the number is increasing fast enough for us to be concerned about it,” RBI’s deputy governor, T. Rabi Sankar, said during a press conference after the Monetary Policy.
“(The) Reserve Bank’s idea of introducing this MuleHunter is to create an infrastructure-level facility which others can use. Many participants, many small banks, cannot devote enough resources to develop systems, (and) can use this. Even for the well-developed ones, this is a base which everyone can use,” Sankar added.
At the same time, payment system operators, banks, credit card networks, payment aggregators and payment apps are also “free to use” their own fraud detection systems, over and above MuleHunter.AI.
“Every individual is free to innovate based on their own expertise, their own AI engines, and so on,” Sankar clarified.
Data from RBI’s annual report show that digital payment fraud in India jumped to a record Rs 14.57 billion ($175 million) in the fiscal year that ended in March 2024 (FY2023-24). Card or internet-based frauds accounted for about 80% of total bank and financial institution frauds in FY2023-24, compared to about 49% in the previous year (FY2022-23). However, frauds reported in a year could have occurred several years prior to the year of reporting.
“The pandemic and increased penetration of internet and mobile phones have only accelerated the use of digital payments, and the dependency on them will continue to increase in future,” according to a PwC report.
“Payment fraud threats have provided a useful opportunity to FIs [financial institutions] to strategically collaborate with technology firms offering new-age solutions in areas related to security, data privacy and fraud risk management. These firms leverage the power of big data, AI and advanced predictive modelling to detect security risks and frauds at various touchpoints in the payments life cycle and help to mitigate them,” PwC added.
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