Insurance Fraud Detection using Machine Learning

Abstract

Many insurance companies today deal with the issue of fraudulent insurance claims, which results in significant yearly financial loss. Since the losses are covered by raising policyholders’ premium costs, these frauds have a negative impact on society. The traditional claim investigation procedure has also been blamed for producing unreliable conclusions because it is time-consuming and laborious. Therefore, using machine learning and the XGBoost method, we construct an automated fraud detection application framework in this study. Accurately identifying fraud claims in a shorter amount of time is the goal. Data analysis is utilized throughout the process to validate, sanitize, and extract the pertinent data. As a result, the insurance firm can retain its reputation outside by employing this structure and has a reliable relationship with clients that they can share.