RECOGNITON AND ASSESSMENT OF DISHONESTY IN INSURANCE CLAIMS USING MACHINE LEARNING

Abstract: Reducing fraud and maintaining the integrity of the insurance sector depend heavily on the detection and evaluation of dishonesty in insurance claims through machine learning. This study makes use of cutting-edge machine learning methods to identify and evaluate insurance claim fraud. The system's objective is to effectively discriminate between genuine and fraudulent claims by evaluating past data, seeing trends, and putting prediction models into practice. The suggested system is made to be accurate, scalable, and able to learn continuously, all of which will increase its efficacy over time. This study showcases the system's potential to improve fraud detection in the insurance industry by outlining its design, methodology, and implementation details.