‘Machine learning’ fights fraud behind cash registers

Fraud examiners are working to innovate anti-fraud processes. Column editor Vincent M. Walden, CFE, CPA, in conjunction with other professionals, reports original concepts that help improve the effectiveness and efficiency of anti-fraud monitoring. — ed.

A global “quick-service” (i.e., minimal table service) restaurant business with thousands of locations accelerated its employee theft-and-error detection rate at the cash register by approximately threefold. Enhancements included the risk analysis of geographic regions, stores, employees and point-of-sale (POS) transactions plus integrating “machine learning” for improved fraud detection.

The company’s team of analysts embarked on this journey by developing multiple fraud scenarios that modeled the known behavior of employees stealing from the company’s cash registers. The models calculated fraud schemes by combining multiple data sources, which included POS, store location information and employee scheduling. Then, the machine-learning models used statistical benchmarking and dozens of algorithms to score and rank risks that potentially existed in transactions, employees, stores and regions.

The team displayed the results in data visualizations that provided a global view of risk specifically tailored to the organization’s operating and reporting needs. As the team discovered and confirmed fraud schemes, it added those specific fraudulent transactions into the model to improve the results of the system.