The Demand for Life Insurance: Dynamic Ecological Systemic Theory Using Machine Learning Techniques
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Description
This book, adopting machine learning techniques for the financial planning field, explores the demand for life insurance as seen in previous literature and both estimates and predicts the demand for the adoption of life insurance using these techniques. Previous studies used diverse perspectives, like actuarial and life span, in order to understand the demand for life insurance, though these approaches have shown inconsistent findings. Employing two theoretical backgrounds—ecological systemic theory and artificial intellectual methodology—this book explores a better estimation and a prediction of the demand for life insurance and will be of interest to academics and students of insurance, financial planning, and risk management. Front Matter ....Pages i-xii Introduction: A Need of New Framework in Financial Planning with the Case of Life Insurance Demand (Wookjae Heo)....Pages 1-17 Theoretical Background: A New Theoretical Framework for Financial Planning with the Case of Life Insurance Demand—Dynamic Ecological Systemic Framework (Wookjae Heo)....Pages 19-46 Literature Review: Previous Literature for Understanding Life Insurance and Behavioral Demand for Life Insurance (Wookjae Heo)....Pages 47-64 Practical Approach: Practical Approach to Personal Needs of Life Insurance with Dynamic Systemic Framework (Wookjae Heo)....Pages 65-75 Empirical Analysis Part 1 Methodology and Data: Empirical Example of Predicting the Demand for Life Insurance by Using the Dynamic Systemic Framework (Wookjae Heo)....Pages 77-99 Empirical Analysis Part 2 Result and Findings: Empirical Example of Predicting the Demand for Life Insurance by Using the Dynamic Systemic Framework (Wookjae Heo)....Pages 101-144 Implications and Conclusion: Implications and Conclusion from the Empirical Example of Predicting the Demand for Life Insurance by Using the Dynamic Systemic Framework (Wookjae Heo)....Pages 145-160 Back Matter ....Pages 161-163
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