A Case Study on Analytical Tools for Insurance Fraud

Corruption in the auto insurance industry is a worldwide concern. For insurance firms, manually addressing fraud is always expensive. Data science may be quite beneficial in the fraud detection process and can aid insurance companies to detect fraud. For the fraud analysis, typically, there are probably over forty variables. The purpose of this study is to identify the factors that are crucial for detecting fraud and to offer a framework for doing so. This paper also uses empirical research to demonstrate the commercial use of data analytics for detecting insurance fraud. It shows how the insurance firm can accurately identify fraudulent claims by adopting a few business guidelines, which will probably lead to cost reduction and higher profitability for the business.