Μέθοδοι αναλυτικής των δεδομένων και στατιστικής μηχανικής μάθησης στην ανίχνευση της απάτης στην ασφάλιση υγείας
Data analytics and machine learning methods for health insurance fraud detection
KeywordsFraud ; Health insurance fraud ; Machine learning ; Data analytics ; Fraud detection ; Supervised methods ; Unsupervised methods ; Hybrid methods ; Areas of fraud detection
The extent, probability, and complexity of the healthcare industry have attracted widespread fraud that has a significant impact on the economy; fraudulent activities not only contribute to the problem of increasing health care costs but also affect patient health; the challenge in current fraud detection systems lies mainly in understanding the burden of financial loss and unusual areas of behavior. Despite the implementation of various anti-fraud technologies and strategies, such as planned, targeted and random checks, complaints, and biometric systems, insurance fraud is still a major problem for most health insurance providers. The purpose of this thesis is to raise awareness of this important area among the general public and to demonstrate the need for relevant public bodies and health companies to invest a significant part of their capital and their time in the wise exploitation of the data now available in the world, statistical machine learning, as well as predictive analysis techniques to reduce health insurance fraud. Initially, the relevant definitions for health insurance fraud are given, and it is provided an overview of the problem and the ways and forms in which health insurance fraud occurs. Next, it is highlighted how important the problem is with various examples of healthcare fraud worldwide, and a presentation of punishments for health insurance fraud. In addition, other fraud detection areas are listed, supported by case studies. Then there will be a thorough search in the literature for the applications of data analytics and statistical machine learning that have been used along with case studies. At a later stage, the methods of machine learning in fraud detection that are commonly used are mentioned. Finally, selected methodologies were applied to real-world data in order to present a complete case study that aims to build a model that can detect fraudulent providers with lower costs and expenditures.