The Reserve Bank Innovation Hub (RBIH) is making significant progress in the fight against financial fraud. In particular, it is promoting MuleHunter.AI, an advanced AI tool designed to identify and flag mule accounts used in money laundering.
Successful Trials in Public Sector Banks
MuleHunter.AI has already been successfully tested in two public sector banks. According to the National Crime Records Bureau (NCRB), online financial frauds account for 67.8% of all cybercrime complaints. Given this, the need for effective fraud prevention tools, such as AI, has become more urgent than ever.
The Problem with Mule Accounts
Mule accounts are primarily used for laundering money. These accounts are often opened by individuals who are either tricked or coerced into participating. As a result, tracking the movement of funds through these accounts is incredibly difficult.
Development of MuleHunter.AI
In response, RBIH collaborated with banks to better understand how mule accounts were being detected. It became evident that the current rule-based systems were slow and inaccurate, leading to many mule accounts going undetected. To address this, RBIH analyzed nineteen different mule account activity patterns and developed MuleHunter.AI. Early results have shown significant improvements in both speed and accuracy.
How MuleHunter.AI Works
MuleHunter.AI uses machine learning (ML) algorithms to detect mule accounts more efficiently. Unlike traditional rule-based systems, ML algorithms can analyze transaction and account data with greater speed and precision. As a result, this approach enables quicker detection of fraudulent accounts. Ultimately, the AI tool helps banks identify mule accounts faster, which is critical as financial fraud continues to grow.
In conclusion, RBIH’s use of MuleHunter.AI aims to streamline the detection of fraudulent accounts, offering a more effective way to tackle financial fraud and safeguard the banking system.
