Healthcare industry is one of the many industry suffering from high number of Fraud and abuse cases. Frauds may happen intentionally or unintentionally, but it creates a huge loss to insurance companies that effects the financial growth of the business and their profits. Fraud may occur in terms of provider, member, broker, pharmacy, payer and new forms of fraud consistently take place depending upon the current situations. For example, on March 23rd 2020, the U.S. Department of Health and Human Services, Office of the Inspector General (HHS OIG), issued a public COVID-19 fraud alerts which outlines the fraud schemes which include contribution illegal COVID-19 test kits to Medicare payees, in exchange for personal health information, using PHI for fraudulent billing for unrelated items and services, aiming people through social media and telemarketing. There are many trainings within the companies and emergency centers to report these fraud and abuse cases, but only after the incident happens. This creates a huge challenge to health care industry and data science as well. Early detection (well before fraud happens) is the key component of fighting fraud and abuse for health care industry. Sophisticated data analytics and both prospective and retrospective methods to detect areas of fraud, waste and abuse will help in this regard. This Case study will be focusing on the issues in Health Care Industry provider frauds where the work will analyze current data sets associated with any health insurance company (Source Kaggle) and build a predictive score model that identifies the suspected claims and providing the insights and models to understand the Fraud.