若要進行適當的核保決策並且防範保險詐欺，保險公司試圖收集各種不同的數據資料作為適當的危險評估，並準確的評估要保人或被保人的風險狀況後加以分類並給予正確的保險費率。在此狀況下，核保人員往往必需撒下廣泛的網絡來發現及取得相關文件進行比對，進而去發現投保資料和所收集到的資料是否有不一致的地方、要保人的敘述或者是否有保險詐欺等跡象。然而，傳統核保屬於勞力密集且成本昂貴的技術，幸運的是，透過社群網路的技術可以幫助保險公司改善核保上的技術並獲得核保上的利潤，就可以選擇潛在良好的要保人。但保險人面臨許多困難，無法馬上運用網路技術進行核保，是因為沒有任何保險監理單位或保險人發展相關準則或指導方針來說明該如何適當使用社群網路技術進行核保。而目前也沒有科學性研究去決定何種類型的社群網站資料是可以被參考與使用，為了填補此研究上的缺口，本研究第一個目的是在探討哪一些核保因子是核保人員最想要從社群網站上所獲取的資料，其次則欲探討哪種型態的社群網站資料可以為保險人在核保決策上提供核保因子最佳的見解。此研究發現可以提供給以社群網站進行核保決策的核保人員，使其能選擇較佳的投保業務並賦予公平的保費，進而從中獲得核保利潤。

To make appropriate underwriting decisions and prevent insurance fraud, insurance companies attempt to collect various sources of data to accurately rate the risk profile of certain classes of policyholders or applicants. In this context, underwriters will often cast a broad net in discovery requests, seeking as much documentation as possible to search for inconsistencies in the applicant or policyholder’s story or indications of potential fraud. However, these traditional techniques are labor intensive and very expensive. Fortunately, the new online social networking technology may help insurance companies to improve their underwriting profits and select prospective policyholders. However, insurers face obstacles that may impede the speed-to-market of applying social networking data to underwriting. This is because neither regulators nor insurers have developed guidelines for the overall use of social data, and scientific studies have not determined what types of social medial data are referable. To fill this research gap, the first purpose of this study is to identify what underwriting factors underwriters prefer to search for in social media networking. The second purpose of this paper is to explore the types of social media data that may offer the best insights on underwriting factors for insurers to make underwriting decisions. The findings may provide information for those who employing social media networking data to make underwriting decision to attain underwriting profits, select prospective policyholders, and provide equity among policyholders.

CONTENTS
摘要	I
ABSTRACT	II
CONTENTS	III
LIST OF FIGURE	IV
LIST OF TABLE	V
1. INTRODUCTION	1
2. LITERATURE REVIEW	4
2.1 Information Provided by Social Popular Networking Sites	4
2.2 The Role of Social Media in Insurance Underwriting	5
2.3 Social Media Data Used as Sources of Evidence in Courts of Law in Claim Cases	6
2.4 Important Underwriting Factors That Determine a Life Insurance Premium	7
2.5 Useful Social Media Data in Underwriting	8
3. METHODOLOGY	8
3.1 Establish a hierarchical structure	9
3.2 Establishment of pairwise comparison matrix	10
3.3 Compute the eigenvalue and eigenvector	12
3.4 Perform the consistency test	12
3.5 Compute the entire hierarchical weight	13
3.6 Calculate the whole level weight to select the best alternatives	13
4. DECISION MODEL APPLICATION AND RFESULTS.	13
4.1 Designate the AHP participants	13
4.2 Establish a hierarchy structure	14
4.3 Establish a pairwise comparison matrix.	14
4.4 Compute the eigenvalue and eigenvector.	14
4.5 Perform the consistency test.	14
4.6 Compute the relative weight of each hierarchy.	15
4.7 Calculate the whole level weight to select the most appropriate type of social medial data.	16
5. CONCLUSIONS AND RECOMMENDATIONS.	17
REFERENCES	19
APPENDICES	22
 
LIST OF FIGURE
Figure 1: Determinants of Underwriting Decision Making	7
Figure 2: Research Procedures	9
Figure 3: The Hierarchy Structure	10

 
LIST OF TABLE
Table 1: Aggregation of the Pairwise Comparison Matrix for Criteria of Main Criteria	14
Table 2: Weights of the Criteria and Sub-criteria	15
Table 3: Life Insurance Company Application of the AHP Model to Select an Appropriate type of Social Media Data to Improve the Effectiveness of Underwriting	16

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