National Crime Records Bureau (NCRB) data revealed that online financial frauds constitute 67.8% of all cybercrime complaints received in Q2 2022. 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 make it difficult to trace and recover the funds.

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