The Evaluation of Distributed Topic Modeling Paradigms for Detection Of Fraudulent Insurance Claims In Healthcare Forum

Abstract

Healthcare fraud is the deliberate misrepresentation of the healthcare industry for the purpose of obtaining unjustified financial gain. There are many different types of healthcare fraud, which can influence patients, healthcare professionals, insurers, and government programmes, such as Billing Fraud, Kickbacks and Bribes, Prescription Fraud, False Claims, Provider Licensing Fraud etc...Healthcare insurance fraud is a severe problem that has an impact on everyone's access to affordable healthcare. Topic modelling can play a role in addressing healthcare insurance fraud by assisting in the detection, analysis, and prevention of fraudulent activities. Overall, the public benefits from healthcare insurance fraud detection because it supports equitable, open, and effective healthcare systems.

Keywords

• bigdata
• topic modeling
• insurance
• machine learning
• dlda
• dnmf