VISUAL ANALYTICS-DRIVEN DETECTION OF COLLABORATIVE FRAUD IN HEALTH INSURANCE SYSTEMS

Abstract

The functioning of the healthcare system is threatened by collusive fraud, in which many con artists band together to steal money from health insurance. However, owing to the absence of labelled data and the great resemblance of fraudulent behaviours to routine medical visits, current statistical and machine learning-based algorithms are restricted in their capacity to identify fraud in the context of health insurance. Expert knowledge must be included into the fraud detection process in order to guarantee the accuracy of the detection findings. We present FraudAuditor, a three-stage visual analytics method for collusive fraud detection in health insurance, developed in close collaboration with audit specialists in the field. In particular, in order to represent the visit connections of various patients holistically, we initially let users build a co-visit network interactively. Second, suspected fraudulent groupings are identified using an enhanced community identification algorithm that takes the level of fraud possibility into account. Lastly, with customised visualisations that accommodate various time scales, users may compare, examine, and validate questionable patient behaviour using our visual interface. In order to identify the true fraud group and rule out the false positive group, we performed case studies in a real-world healthcare setting. The outcomes and professional opinions demonstrated the approach's efficacy and usefulness.