The Demand for Life Insurance : Dynamic Ecological Systemic Theory Using Machine Learning Techniques / by Wookjae Heo.

Von:

Heo, Wookjae [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Springer eBook CollectionVerlag: Cham : Springer International Publishing, 2020Verlag: Cham : Imprint: Palgrave Pivot, 2020Auflage: 1st ed. 2020Beschreibung: 1 Online-Ressource (XII, 163 p. 10 illus., 2 illus. in color.)ISBN:

9783030369033

Schlagwörter:

Andere physische Formen: 9783030369026 | 9783030369040 | 9783030369057 | Erscheint auch als: 9783030369026 Druck-Ausgabe | Erscheint auch als: 9783030369040 Druck-Ausgabe | Erscheint auch als: 9783030369057 Druck-Ausgabe | Erscheint auch als: The demand for life insurance. Druck-Ausgabe Cham, Switzerland : Palgrave Macmillan, 2020. xii, 163 SeitenDDC-Klassifikation:

657.836 23

DOI: DOI: 10.1007/978-3-030-36903-3Online-Ressourcen:

Zusammenfassung: Introduction: A Need of New Framework in Financial Planning with the Case of Life Insurance Demand -- Theoretical Background: A New Theoretical Framework for Financial Planning with the Case of Life Insurance Demand -- Literature Review: Previous Literature for Understanding Life Insurance and Behavioral Demand for Life Insurance -- Practical Approach: Practical Approach to Personal Needs of Life Insurance with Dynamic Systemic Framework -- Empirical Analysis Part 1 Methodology and Data: Empirical Example of Predicting the Demand for Life Insurance by Using the Dynamic Systemic Framework -- Empirical Analysis Part 2 Result and Findings: Empirical Example of Predicting the Demand for Life Insurance by Using the Dynamic Systemic Framework -- Implications and Conclusion: Implications and Conclusion from the Empirical Example of Predicting the Demand for Life Insurance by Using the Dynamic Systemic Framework.Zusammenfassung: “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.PPN: PPN: 1686642032Package identifier: Produktsigel: ZDB-2-ECF | ZDB-2-SEB | ZDB-2-SXEF

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