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Comparative Study of Regression Models and Deep Learning Models for Insurance Cost Prediction

  • Aditya ShindeEmail author
  • Purva Raut
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

In the finance world, Insurance is a product that reduces or eliminate the cost of loss caused due to different risks. There are various factors associated that affect the insurance charges. These factors contribute in formulating the insurance policies. Using machine learning, we can predict insurance charges by developing a model that generalize the entire process. Machine Learning in the Insurance industry would enable seamless formulation of insurance policies with better performance and will time. This study presents how insurance charges can be predicted using various regression models. Also, comparison between the performances of models like Multiple Linear Regression, Support Vector Machine, Random Forest Regressor, XGBoost and Deep Neural Networks is done. This paper provides the most optimal solution using Deep Neural Networks with a root mean square error RMSE value of 0.0695 and model accuracy of 87.95.

Keywords

Regression Insurance Random Forest Regressor XGBoost Deep Neural Networks 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Dwarkadas J. Sanghvi College of EngineeringMumbaiIndia

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