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Uncovering the factors that affect earthquake insurance uptake using supervised machine learning.
John N Ng'ombe1, Kwabena Nyarko Addai2, Agness Mzyece3
1Department of Agribusiness, Applied Economics and Agriscience Education, North Carolina A&T State University, Greensboro, NC, 27411, USA. jngombe@ncat.edu.
Scientific Reports
|December 3, 2023
Summary
Understanding earthquake insurance uptake is vital for environmental risk management. Factors like age, gender, and past earthquake experience influence purchasing decisions, with machine learning models showing predictive power.
Area of Science:
- Environmental Science
- Risk Management
- Geosciences
Background:
- Natural disasters pose increasing threats to public safety globally.
- Earthquake insurance is a key tool for environmental risk management, especially concerning Oklahoma's seismicity linked to wastewater injection (2011-2020).
Purpose of the Study:
- To identify factors influencing earthquake insurance uptake in Oklahoma.
- To predict individuals likely to purchase earthquake insurance using supervised machine learning.
Main Methods:
- Survey of 812 Oklahoma residents.
- Application of supervised machine learning classifiers: logit, ridge, LASSO, decision tree, and random forest.
Main Results:
- Demographic factors (older age, male gender, race, ethnicity), rental property residency, longer Oklahoma residency, and prior earthquake experience significantly influence insurance uptake.
- Decision trees and random forests showed strong predictive capabilities, with random forests demonstrating superior precision and robustness.
Conclusions:
- Insurance is a critical environmental risk management tool.
- Awareness and education on earthquake insurance are needed.
- Supervised machine learning, particularly random forests, is effective for earthquake insurance modeling and similar classification tasks.
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