Life Insurance Claim Model to Efficiency

Abstract

With the innovative and health-driven culture, people nowadays tend to purchase Life Insurance to protect their families as well as to invest and save some money. When someone admits in the hospital because of doctor handwriting, there should be a human involvement for the claim settlement process. But if we can get to know the ailment and if it automatically validates, then every insurance company can send their valuable customers discharge as soon as they post a photo of the prescription. A novel approach from medical records for improving self-insurance claims is presented and validated. The proposed method is based on a collection of machine learning algorithms. In this study, we take all previous customer drugs and predicting the ailment using the existing medical data. The approach is evaluated by comparing the accuracy of the machine learning model. The results showed that the Ailment Prediction (AP) System outperforms the status of ailment in terms of predictive accuracy, proving that the approach might be a good alternative in the medical claim process. As a result of the proposed AP system might become a beneficial tool in the medical claims process for Life Insurance Domain.