The Demand for Life Insurance: Dynamic Ecological Systemic Theory Using Machine Learning Techniques - Rilegato

Heo, Wookjae

Rilegato

ISBN 10: 3030369021 ISBN 13: 9783030369026

Casa editrice: Palgrave Pivot, 2019

Riassunto

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.

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Informazioni sull?autore

Wookjae Heo is an assistant professor of Consumer Affairs at the South Dakota State University, USA. Prior to earning a PhD from the University of Georgia, USA, he had worked for a marketing consulting firm and received a MA Degree in Consumer Sciences from Seoul National University, South Korea. His main research interest is broadly about consumer behavioral intervention, financial stress on consumer behavior, demand of life insurance, and data mining/data analysis in consumer research.

Dalla quarta di copertina

Once every 10 or 20 years, a researcher identifies a tool that can be used to transform the way in which a product or service is conceptualized. It is my opinion that Dr. Heo has made such a breakthrough. As the largest life insurance companies move closer to writing $400 billion in annual direct premiums, it is important for financial advisors, insurance executives, consumer advocates, and policy makers to better understand the factors that shape the insurance buying process. Through the use of machine learning technologies, Dr. Heo provides ground-breaking insights into why some consumers purchase insurance, why some drop coverage, and what can be done to make sense of the complicated insurance marketplace. Anyone interested in gaining an understanding of the nuances embedded in the life insurance marketplace should read this book.

John E. Grable, University of Georgia, USA

Heo is on a quest to reflect the complexities of the world in his study. He introduces how holistic patterns in reality can be better identified and addressed in predicting the demand for life insurance. This book will reshape the way we see how financial decisions are studied. This book is a delightful guide full of useful insights for those of us who want to enjoy a journey to a dynamic nonlinear systemic framework in consumer finances and relevant disciplines. Heo offers a great explanation of the basics of the analytic framework and all the key results, making the book a great resource for researchers and for financial practitioners as well. The new framework can be applied in diverse setting and the book will provide the ideal introduction to an artificial neural network application in the field.

Jae Min Lee, Minnesota State University, Mankato

Wookjae Heo is an assistant professor of Consumer Affairs at the South Dakota State University, USA. Prior to earning a PhD from the University of Georgia, USA, he had worked for a marketing consulting firm and received a MA Degree in Consumer Sciences from Seoul National University, South Korea. His main research interest is broadly about consumer behavioral intervention, financial stress on consumer behavior, demand of life insurance, and data mining/data analysis in consumer research.

Dettagli dell'edizione

• Editore: Palgrave Pivot
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 3030369021
• ISBN 13: 9783030369026
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 163
• Contatto del produttore: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Germania