A Comparative Study of Using Various Machine Learning and Deep Learning-Based Fraud Detection Models For Universal Health Coverage Schemes

摘要

Fraud detection is an important area of research in the healthcare systems due to its financial consequences arising mainly from investigation costs, revenue losses, and reputational risk. To mitigate this, most of the companies adopt Machine Learning and/or Deep Learningbased fraud detection models. Efficient fraud detection models improve the performance of healthcare systems. Key challenges in building an efficient fraud detection model include.