Improving the Accuracy and Performance of Deep Learning Model by Applying Hybrid Grey Wolf Whale Optimizer to P&C Insurance Data

Abstract

The insurance industry is based on risk calculations, high profits, and detailed information. The predictive models that insurance companies utilize allow insurance companies to make accurate decisions about the insurance sector. This research focuses on improving the accuracy of predicting customers of Property and Casualty (P&C) insurance. In this study, a reliable quantitative analytical big data method has been developed, and the Hybrid Grey Wolf and Whale Optimization (HGWWO) is utilized with Deep Learning Model for evaluating customer behavior of the customers of P&C insurance. The research discussed the Hybrid Gray Wolf-Whale Optimization algorithm and the steps involved in the optimization process. This paper has presented the details of how to create a Grey Wolf Optimizer, Whale Optimizer and then combining both for initialization, evaluation, and optimization of the relevant P&C insurance dataset to improve the prediction accuracy. We have also compared the performance of the Deep Learning model with a few traditional machine learning models.