Detection and Prevention of Medical Fraud using Machine Learning

Presently, there is an upward trend in the mean life expectancy of individuals due to reductions in maternal and infant mortality, as well as deaths caused by noncommunicable diseases like cardiovascular disease. A decline in life expectancy results in a corresponding increase in health expenditures sustained by both public and private entities, including insurance providers. The healthcare sector has become an extremely comprehensive and critical industry due to the following factors: the increase in healthcare expenditures, particularly during the pandemic; the cost of each component in the healthcare sector; the increasingly chaotic healthcare technology ecosystem; the growing expectations of numerous and diverse stakeholders; and the presence of numerous and new actors in the sector. Nevertheless, this circumstance exposes the health sector to many hazards, thereby increasing its susceptibility to fraudulent activities. The sector’s substantial volume will inevitably lead to expensive fraudulent activities. For this reason, prospective medical frauds should be prevented and detected immediately. Machine learning is considered one of the most powerful and optimal approaches to prevent medical fraud. An example application is used to assess the efficacy of machine learning in the medical fraud detection context as part of the research. The objective of the proposed application is to classify provider-side medical fraud by applying various machine learning techniques and medical claims.