The Reserve Bank Innovation Hub (RBIH), which is the innovation arm of the Reserve Bank of India (RBI), is making giant strides in the fight against financial fraud through the promotion of the use of an advanced AI tool called MuleHunter.AI. This technology identifies and flags mule accounts, commonly used in money laundering schemes.
The application of MuleHunter.AI has already been successfully demonstrated in two public sector banks. Data from the National Crime Records Bureau (NCRB) indicate that online financial frauds are responsible for 67.8% of all complaints related to cybercrime. This makes the effective provision of fraud prevention AI tools highly urgent.
One of the biggest problems in fighting financial fraud is the exploitation of money mule accounts. These accounts are a key enabler of illicit financial activities; hence, tools like MuleHunter.AI is of paramount importance to protect the financial ecosystem and curb cybercrime.
What is a money mule account?
According to RBIH, 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.
The Development of MuleHunter.AI
According to the Reserve Bank Innovation Hub, the department has 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.
After working with several banks to analyse nineteen different patterns of mule account activity, the platform was created. Mulehunter.Ai’s initial results demonstrate notable gains in efficiency and accuracy.
How Mulehunter.Ai works
This in-house AI/ML-based solution is better suited than a rule-based system to identify suspected mule accounts. 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.
The purpose of RBIH’s AI platform is to speed up the identification of fraudulent accounts. Frauds can happen through a variety of channels, and they are no longer little incidents; they are becoming big day by day. The best approach would be to look at where the money eventually goes-to mule accounts. This machine learning-based approach has enabled the detection of more mule accounts within a bank’s system.
