Allianz Trade uses algorithms to detect buyer fraud

Allianz Trade, the global leader in credit insurance, has introduced "Sherlock," a machine learning solution to improve the identification of buyer fraud. This tool analyzes vast amounts of data rapidly and accurately, augmenting the company's credit analysis capabilities.

Detecting buyer fraud is a crucial aspect of Allianz Trade's services, aimed at protecting policyholders from financial losses. Buyer fraud occurs when a buyer attempts to defraud a seller and subsequently fails to pay. Historically, identifying such instances relied solely on manual assessments by expert analysts, a process that has become increasingly time-consuming due to escalating data volumes.

Sherlock utilizes advanced algorithms, drawing from multiple data sources without using personal information to maintain data privacy. It operates during the credit limit request stage, flagging suspicious cases for further investigation by human analysts. The system does not make final decisions regarding credit approvals.

Allianz Trade's approach combines algorithmic insights with human expertise. Sherlock assesses the probability of buyer fraud and presents its findings to a credit analyst, who then makes the final determination based on their experience and intuition. The aim is to support and enhance the work of credit analysts, freeing up their time for more in-depth analysis.