TigerGraph launches fraud and money laundering detection technology

The new technology combines graph pattern matching with real-time deep link analytics. Standard pattern matching solutions have a defined starting point such as a specific customer account or payment and a well-defined pattern with a fixed number of hops such as traversal from a customer account to all the payments originating from the account to recipients of those payments etc.

Discovering fraud or money laundering loops is complex, as it does not have a defined starting point as the payment may originate from any customer account and it also does not have defined number of hops as fraudsters or money launderers often use 10+ layers of synthetic accounts to hide their activities. With its massively parallel processing (MPP) engine, TigerGraph 2.4 addresses both the standard as well as complex pattern matching for datasets of all sizes.

Fraud detection looks for transactional patterns similar to those of known cases of fraud or money laundering. Accumulators combined with the pattern matching in TigerGraph allows data scientists to define multi-dimensional criteria for fraud or money laundering detection. As new payments come in every second, accumulators recalculate a new fraud or money laundering risk score for each payment as well as for each account sending or receiving the payment based on the multi-dimensional scoring criteria such as size, frequency and percentage of payments with other accounts suspected of being involved in fraud or money laundering. This is combined with pattern matching as many as 10 levels deep in the payment and customer account graph to flag potentially fraudulent transactions and accounts that have crossed the threshold of acceptable risk and need to be investigated by the fraud or anti-money laundering analysts.