Fraudster Detection Based on Modularity Optimization Algorithm

With the advancement and popularization of healthcare informatization, healthcare fraud has happened frequently. Doctors, patients and pharmacies are involved, and these various subjects cooperate with each other to form a complicated fraud relationship. Healthcare fraud has caused great damage to the safety of the health insurance fund. However, fraudster detection problem has not been solved properly at present. Therefore, Fraudster Detection Based on Modularity Optimization Algorithm (FDMOA) approach is developed to find doctor fraudsters in a good accuracy, which models the medical insurance records as a heterogeneous weighted network of doctors and medicines, and uses a modularity optimization algorithm to divide doctors and drugs into corresponding communities on a heterogeneous network. Finally, anomaly detection strategy was used in the community to find an abnormal doctor. Experiments show that the proposed method outperforms distance-based, clustering-based, and LOF algorithms.