Since the beginning of the insurance industry, there has been the problem of fraudulent insurance claims. These are a broad variety of illegal activities, the most of which are never uncovered while costing the insurance industry billions of dollars annually. It is estimated that India's insurance industry is suffering losses of around 600–Rs. 600 million each year because of India's growing economy, more awareness, and strengthened distribution networks. 800 crores in losses sustained yearly due to bogus claims. India comes up at number 10 for gross premiums collected by life insurance companies and number 15 for the total amount earned by non-life insurance companies. As a result of this, we are presenting a framework for the selection of features to be used in machine learning, which will enable the robust categorization of insurance claims. It will demonstrate how these technologies might be used to the development of a system that can prevent certain kinds of fraud in the field of healthcare. Several different studies have been carried out to demonstrate that the established approach may effectively identify instances of healthcare fraud. As a result, it may be useful in the prevention of false claims and gives greater insight into how to enhance patient management and treatment methods.