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Insurance Risk Prediction Using Machine Learning by Rahul Sahai; Ali Al-Ataby; Sulaf Assi; Manoj Jayabalan; Panagiotis Liatsis; Chong Kim Loy; Abdullah Al-Hamid; Sahar Al-Sudani; Maitham Alamran; Hoshang Kolivand is a book available to read on EtoBox.

What is Insurance Risk Prediction Using Machine Learning about?

Underwriting decisions by insurance companies make a significant contribution to their profitability. Machine Learning (ML) techniques in underwriting decision making have saved time and improved operational efficiencies. A user-friendly cause-and-effect explanation of model's predictions is useful to stakeholders, financial institutions and regulators. This research performed comparative analysis between tree-based classifiers such as Decision Tree, Random Forest and XGBoost. The study focused on enhancing risk assessment capabilities for life insurance companies using predictive analytics by classifying the insurance risk based on the historical data and propose the appropriate model to assess risk. Its purpose also included incorporating mechanisms that can aid in user friendly interpretation of ML models. Of all the models created as part of this research, the XGBoost classifier performed the best when compared to other classifiers, with an AUC value of 0.86 and F1-score above 0.56 on the validation set. The Random Forest classifier got AUC value of .84 and f1 score of .53 on the validation dataset. The results indicate the importance and advantages of tree -based models. These

Author

Rahul Sahai; Ali Al-Ataby; Sulaf Assi; Manoj Jayabalan; Panagiotis Liatsis; Chong Kim Loy; Abdullah Al-Hamid; Sahar Al-Sudani; Maitham Alamran; Hoshang Kolivand

Publisher

Springer Nature Singapore Pte Ltd Fka Springer Science + Business Media Singapore Pte Ltd

Published

2023

Language

EN

ISBN

9789819907410

Subjects

Computer Science, Engineering, Mathematics