• Journal of Multimedia Information System
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• v.8 no.1
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• pp.69-74
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• 2021

This paper accumulates the product structure data such as bond obligation ratio and investment ratio for variable insurance using crawling from the insurance company's API, also accumulates variable insurance income and project expenses for variable insurance using crawling from the API of life insurance association. From these accumulated data, the correlation coefficient between fund product and customer preference is calculated with an investment algorithm, and variable insurance funds by customer investment preference and product structure are recommended according to market conditions. From the simulation results, it is shown that the proposed variable insurance management system properly recommends and manages variable insurance according to customer preferences.

• Asia Marketing Journal
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• v.7 no.3
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• pp.105-119
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• 2005

Samsung Fire & Marine Insurance Co., Ltd. launched 'Samsung Super Insurance Package', an insurance policy combining household's property & casualty insurance policies, except insurance with savings and pension insurance, into one for the first time in Korea. The product was developed to tackle challenges and threats from environmental changes, whose success owes to an excellent marketing strategies like adopting MCS system which is coined to support sales force activities, and building SRC(Samsung Risk Consultant), the organization dedicated to the sales of the product. The case shows how Samsung Fire & Marine Insurance Co., Ltd. developed and successfully marketed the combined insurance policy for the first time in Korea identifying the key success factors and discusses its business implications and future challenges.

• The Journal of Society for e-Business Studies
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• v.12 no.1
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• pp.99-122
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• 2007

The rule based system is inference engine which can correspond quickly to new business model change and improvement requirement by dealing with the business know-how and expert knowledge as well as business process of enterprise and has been trying to apply to the various industries. As a part of application cases for rule-based system, we develop and implement the rule-based insurance product recommend and design system for the efficient decision making of insurance product in insurance industry which is sensitively affected by needs of customers, various kinds of product, and environment changes. The process of fuzzy inference of the developed system helps to recommend and design the proper Insurance product using the information of the present customer and the previous members. This approach is expected that it will be the core technology for the recommendation and design of the tailored insurance product by deciding and corresponding needs of various kinds of customer quickly in future insurance industry.

• The Journal of the Korean life insurance medical association
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• v.24
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• pp.21-25
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• 2005

• Journal of Information Technology Services
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• v.2 no.2
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• pp.63-73
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• 2003

Insurance system has a lot of decision factors which are affected by the kinds of insurance products. and has the features of many exceptions. Since in applying the product attributes to the current system the value definition through tables and the exception treatment logic (if then else) are used in parallel, the cost of a product change and a new product development becomes increased and the prompt market reaction is difficult. In this paper, we propose the well formed rule base system which makes data for the business logic of insurance attributes and discuss the benefit of application of this system to the real project.

• Management & Information Systems Review
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• v.14
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• pp.195-214
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• 2004

The purpose of this study is to find proper strategies for the reinforcement of brand image in the online insurance retail stores. First, from theoretical backgrounds, I have investigated acquisition effects, strategies for changing brand image, decisions on a product position and product differentiation under various strategic marketing factors based on the reinforcement strategies of brand image. Second, there is a significant difference between corporate/on-line insurance retail store names recalled first and brand names recalled first. Third, in terms of consumers' preferences, there exists a substantial difference between corporate/on-line insurance retail stores and brands. From this study, the following conclusions are drawn. First, on-line insurance retail stores and brand image should be carefully developed and used as a tool to secure competitive advantages. Second, on-line insurance retail stores and brand image should be considered and continuously enhanced as an important asset for the on-line insurance retail stores. As the importance of brands grows, brand management is an essential step for powering brands.

• The Journal of the Korean life insurance medical association
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• v.27 no.1
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• pp.18-20
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• 2008

I have taught various case audit underwriting methods to underwriters for many years. Underwriting requires specialized medical knowledge and experience of comprehensive individual risk analysis. Decisions in individualized underwriting depend on the insurance company's management philosophy and vision, marketing strategies, culture, product line etc. They also depend on the information acquired through proposal forms, inspection reports, laboratory results and disclosures. Underwriters match this acquired information to each insurance product, search appropriate medical resources, assess the probabilities of future claims, and decide what, if any, further medical information should be gathered. Sources include doctors' reports, medical records, biopsies, blood and urine tests, CT, MRI, Holter monitoring, sonography etc. Insurance medical directors help and teach underwriters to analyze individual medical risks and make effective.

• Journal of the Korea Safety Management & Science
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• v.10 no.4
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• pp.309-314
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• 2008

Many studies on predicting and recommending information and products have been studying to meet customers' preference. Unnecessary information should be removed to satisfy customers' needs in massive information. The some information filtering methods to remove unnecessary information have been suggested but these methods have scarcity and scalability problems. Therefore, this paper explores a personalized recommendation system based on artificial neural network (ANN) to solve these problems. The insurance product recommendation is adapted as an example to demonstrate the proposed method. The proposed recommendation system is expected to recommended a suitable and personalized insurance products for customers' satisfaction.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.3
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• pp.87-95
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• 2006

In this paper, an approach for analyzing brand loyalty and brand switching behavior in car insurance market is presented. A two-choice model by Blumen, et al. that uses Markov chain is adopted as a main technique for estimating brand parameters. Survey data have been collected through personal interviews with questionnaires. Following the application of the model to the data, it is found that there are five leading companies in car insurance market where the number of potential brand switchers is larger than that of brand loyal customers. Therefore, differentiation of this product along with the conversion of this low-involvement product to a high-involvement one could make car insurance stand out against a somewhat undifferentiated field of competitors.

• Proceedings of the Safety Management and Science Conference
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• 2001.05a
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• pp.235-239
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• 2001

The purpose of this study is to develop an insurance system for product quality liability(PQL) by reviewing some legal issues concerning the product liability It is concluded that the purpose and the function of PQL insurance have to be considered with robust experience data for the life of product, quality system of the company, size of company, the number and amount of products produced by a company and the type of company etc. And this article reviews some problems of policy including the possibility of anti-selection and reverse selection.