RBI Governor Shaktikanta Das today said its AI/ML-based model called MuleHunter.ai, which is being piloted by its subsidiary Reserve Bank Innovation Hub (RBIH), can enable banks to detect mule bank accounts in an efficient manner.

The Governor says the Reserve Bank of India (RBI) 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," he says.

Notably, the use of money mule accounts is a common method adopted by fraudsters to channel the proceeds of fraud. 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 the development of innovative solutions to contain the use of mule accounts.

Besides, the RBI's MuleHunter.AI initiative is being piloted with two large public sector banks. Das says the pilot 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."

What's a mule account?

A significant challenge in preventing financial fraud is the use of money mule accounts. A mule account 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 makes it difficult to trace and recover the funds.

What is MuleHunter.ai?

According to the RBiH platform, MuleHunter.ai leverages AI/ML to detect money mule accounts more efficiently, with near real-time monitoring, AI/ML-based pattern recognition and greater precision of prediction.

Online financial frauds constituted 67.8% of all cybercrime complaints received in Q2 2022, according to the National Crime Records Bureau (NCRB) data.

Joydip Gupta, head of APAC at AI-based fintech platform Scienaptic, says mule accounts pose a significant challenge to India’s financial sector. “These accounts funnel stolen funds, eroding trust in digital systems and creating operational risks. Online financial frauds constitute nearly 68% of cybercrime complaints in India.”

He adds that RBI’s MuleHunter.AI can enhance financial inclusion by addressing fraud, which often deters first-time users from adopting digital platforms.

The in-house AI/ML-based solution is better suited than a rule-based system to identify suspected mule accounts, says the RBiH. Advanced ML 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.

Dhiraj Gupta, co-founder and CTO of NCR-based fraud detection and validation company mFilterIt highlights growing concerns over mule accounts in India, with scams exceeding ₹2,500 crore. "These accounts enable phishing scams and money laundering, with increasing sophistication making detection a challenge for banks."

He says RBI’s MuleHunter.ai can improve mule account detection but stresses the need for cross-sector collaboration and reliable data.

The RBIH says it had earlier 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.