Fraud Detection and Analysis for Insurance Claim Using Machine Learning

Insurance claim fraud is a serious issue that the insurance business faces. It costs insurance companies money and raises policyholders’ rates. Machine learning has become a potential method for insurance claim fraud investigation and detection in recent years. Machine learning algorithms—such as data mining and deep learning techniques—have been effectively applied to identify trends and abnormalities in insurance claim data that point to fraudulent activity. These algorithms could significantly increase the precision and effectiveness of fraud detection, saving insurance firms money and giving consumers excellent safety. Machine learning algorithms can precisely analyze vast volumes of data and spot trends and abnormalities that can point to insurance claim fraud. These algorithms can look at various factors in claim data, including claim type, policyholder details, and past claim history, to identify anomalies or suspicious trends. With the help of machine learning, insurance companies will be able to build predictive models that will give each claim a Fraud Probability Score (FPS). In this project, we’re focusing on identifying auto insurance fraud using machine learning. An insurance agent should be able to investigate every case and determine if it’s real. But this not only takes time, but it’s also expensive. Hiring and financing the skilled labor needed to review every claim filed daily is impossible. This is where machine learning comes in. In this case, we will use one of the most widely used machine learning algorithms.