Table Of ContentThe Demand for Life
Insurance
Dynamic Ecological
Systemic Theory Using
Machine Learning
Techniques
Wookjae Heo
The Demand for Life Insurance
Wookjae Heo
The Demand for Life
Insurance
Dynamic Ecological Systemic Theory
Using Machine Learning Techniques
Wookjae Heo
South Dakota State University
Brookings, SD, USA
ISBN 978-3-030-36902-6 ISBN 978-3-030-36903-3 (eBook)
https://doi.org/10.1007/978-3-030-36903-3
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reface
It was difficult to combine multiple disciplines—consumer economics,
financial planning, and computer sciences—into a book. The conceptual
linkages among these fields were not easily made. However, these fields
have different theories with a high number of similarities: ecological sys-
temic theory, dynamic concepts from chaos theory, and neural networks
algorithms. All of these theories and concepts indicate, or imply, that
various factors connected to the consumers are inter- and intra-related.
Based on the similarities among these theories, it was possible to com-
bine the different disciplines into one conceptual approach in this book.
This book is important for two reasons. First, it presents a compre-
hensive understanding about the demand for life insurance. The book
starts by providing a general explanation about risk and risk manage-
ment, which are the foundational concepts for life insurance. By under-
standing the comprehensive concepts of risk and risk management,
readers can understand why life insurance should be understood while
considering the various influential factors related to consumers.
Second, there is a theoretical linkage among consumer economics,
financial planning, and computer science. Previously, few efforts were
made to link social/behavioral science and computer science to establish
a new theoretical linkage.
In this book, this theoretical linkage is suggested as a new conceptual
framework for understanding the demand for life insurance. With this
research trial, the researchers might find a pathway to combine social/
behavioral sciences with the computer sciences.
v
vi PREFACE
The analytical parts should be read as practical examples for the use
of this new conceptual framework, which is the empirical extension of
a theoretical linkage between social/behavioral sciences and computer
sciences. However, the analytics of consumer sciences technology can be
improved because many computer scientists are rapidly developing new
analytical technologies. Therefore, gaining an understanding of this part
of the analytical procedure is the initial example to follow for learning
how best to use the new conceptual framework (i.e., theoretical linkage
between social/behavioral sciences and computer sciences) empirically.
I would like to thank Dr. John E. Grable because he has been a great
supporter for me to develop the theoretical linkage between social/
behavioral sciences and computer sciences. Also, I thank Dr. Lance
Palmer and Dr. Swarn Chatterjee for giving advices on these contents.
My thanks go out to my friends, family, and colleagues: Mr. Sooho Heo,
Ms. Jassuk Han, Mr. Jungduk Choi, Ms. Junglan Lee, Dr. JunHyuck
Choi, Dr. Kristi Warren Scott, Dr. Jae Min Lee, Dr. Narang Park, Dr.
Anthony L. Mescher, and Dr. Joseph C. Miller. Finally, a special thank
you goes out to Ms. Jeong Hyun Choi.
Brookings, SD, USA Wookjae Heo
c
ontents
1 Introduction: A Need of New Framework in Financial
Planning with the Case of Life Insurance Demand 1
2 Theoretical Background: A New Theoretical Framework
for Financial Planning with the Case of Life Insurance
Demand—Dynamic Ecological Systemic Framework 19
3 Literature Review: Previous Literature for Understanding
Life Insurance and Behavioral Demand for Life Insurance 47
4 Practical Approach: Practical Approach to Personal Needs
of Life Insurance with Dynamic Systemic Framework 65
5 Empirical Analysis Part 1 Methodology and Data:
Empirical Example of Predicting the Demand for Life
Insurance by Using the Dynamic Systemic Framework 77
6 Empirical Analysis Part 2 Result and Findings:
Empirical Example of Predicting the Demand for Life
Insurance by Using the Dynamic Systemic Framework 101
vii
viii CONTENTS
7 Implications and Conclusion: Implications and
Conclusion from the Empirical Example of Predicting
the Demand for Life Insurance by Using the Dynamic
Systemic Framework 145
Index 161
L f
ist of igures
Fig. 2.1 Example of intra- and inter-system interdependent
interactions 33
Fig. 2.2 Basic neuron algorithms 36
Fig. 2.3 Artificial neural network algorithms 37
Fig. 6.1 Prediction error rate by different numbers of hidden
layers in ANN model (Clusters A, B, and C) with decay
weight of 0.001 126
Fig. 6.2 Prediction error rate by different numbers of hidden
layers in ANN model (Clusters A, B, and C) with decay
weight of 0.1 126
Fig. 7.1 Top weighted value variables associated with dropping
life insurance among Clusters A, B, and C 152
Fig. 7.2 Top weighted value variables associated with purchasing
life insurance among Clusters A, B, and C 153
ix
L t
ist of abLes
Table 1.1 Controversial research findings related to life insurance
demand 7
Table 4.1 Taxonomy of systems and variables in each system 73
Table 5.1 Sample size by year in the NLSY79 dataset 80
Table 5.2 Individual characteristic variables from NLSY79 matched
with selected factors 82
Table 5.3 Household-level system variables from NLSY79 matched
with selected factors 87
Table 5.4 Microenvironmental system variables from NLSY79
matched with selected factors 91
Table 5.5 Macroenvironmental system variables from NLSY79
matched with selected factors 93
Table 6.1 Descriptive table for observations, individual characteristics 103
Table 6.2 Descriptive table for observations, family characteristics 104
Table 6.3 Descriptive table for observations, macro- and
microenvironment 105
Table 6.4 Sub-sampling by cluster analysis, categorical demographics 108
Table 6.5 Sub-sampling by cluster analysis, continuous demographics 108
Table 6.6 Split of observations into training set and testing set
(Clusters A, B, and C only) 110
Table 6.7 Multinomial logistic estimations in training model
by using half of Cluster A 111
Table 6.8 Multinomial logistic estimations in training model
by using half of Cluster B 115
Table 6.9 Multinomial logistic estimations in training model
by using half of Cluster C 119
xi
