Enhancing Insurance Claim Fraud Detection Through Advanced Data Analytics Techniques

Insurance claim fraud is a significant problem that can cost insurers millions of dollars each year. The use of advanced data analytics techniques such as machine learning, data mining, and natural language processing can help insurance companies to detect and prevent fraudulent activities. In this research paper, this proposes a methodology for insurance claim fraud detection that involves data collection, preprocessing, feature engineering, model selection, training, evaluation, tuning, and deployment. Also discuss the experimental setup for insurance claim and fraud detection and future scope for research in this area. Our findings suggest that the use of advanced analytics techniques can significantly improve the accuracy and efficiency of fraud detection systems, enabling insurers to detect and prevent fraudulent activities in a timely and efficient manner.