The insurance business plays a significant role in any country’s long-term economic success in terms of health sector. With a growth in the number of insurance purchasers, having a sophisticated claim analysis system has become an important need for an insurance firm. It uses claim analysis to identify and distinguish the legitimate and fraudulent claims. Many insurance companies are implementing appropriate Machine Learning (ML) models for the decision support system (DSS) in the claim analysis. To create an effective model, the company’s interaction and their shared data is indeed. Nevertheless, the data maintained by each company is crucial since it contains sensitive consumer information, and sharing this confidential information can be dangerous. The data stored by each company is very critical as it consists of customer’s private information which is very risky to share with others. On the basis of various attributes, such as age, sex, body mass index, number of children, smoking habits, some ML models are trained and evaluated for providing better DSS.