運用機器學習技術建構核保風險預測模型：以A公司為例

以作者查詢圖書館館藏

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姓名

游源榮(Yuan-Jung Yu) 查詢紙本館藏

畢業系所

資訊管理學系在職專班

論文名稱

運用機器學習技術建構核保風險預測模型：以A公司為例
(Using machine learning technology to build an underwriting risk prediction model. Take company A as an example)

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至系統瀏覽論文 (2027-7-5以後開放)

摘要(中)

台灣「保險業資產占金融機構資產比率」自2004年的17.36%一路上升到2020年達36.32%是近年來的最高點，可見台灣保險產業的重要性，保險除了是一種金融工具促進社會經濟活動與發展外，且尚有對社會制度發揮穩定的重要功能。然，依據「財團法人金融法制暨犯罪防制中心」2020年舉辦的防制保險犯罪研討會指出2019年的保險犯罪黑數估計損失金額達105億元，同時也指出國外經驗約佔理賠金額10%，這個潛在的保險詐欺、逆選擇風險是各保險公司風險控管的重要課題。
本研究以A公司2018年承保案件為資料來源，並以保單生效後二年內或三年內發生非急性之疾病醫療理賠案件做為目標變項，運用客戶基本資料、投保資料、財務資料、體況資料、招攬人員資料、招攬人員理賠支出率等六大面向納入分析探討，藉由機器學習技術嘗試多種分類器建構核保風險預測模型與實驗，以八種分類器的實驗結果而言，從自變項中可發現以附加醫療險個數、主約險種類別、主附險種代碼及附加附約數是最好的預測變項，在分類器的預測模型效能以梯度提升機具有較穩定及正確率較高的預測能力，其AUC有0.71以上的標準，如以CA分數而言，則以邏輯斯迴歸表現最佳，達0.612以上。
期能藉由本研究結果提供個案公司未來建構以數據為基礎進行風險分類的核保風險評估機制的參考，透過核保風險預測模型提供更有效率對核保風險分類進行核保評估作業，提升核保效能，加速自動化作業並能提升客戶的投保體驗與滿意度，進而提升與公司的黏著度與忠誠度。

摘要(英)

Taiwan′s "Ratio of Assets of Insurance Industry to Total Assets of Financial Institutions" has risen from 17.36% in 2004 to 36.32% in 2020, which is the highest point in recent years. This shows the importance of Taiwan′s insurance industry. Insurance is not only a financial tool to facilitate socio-economic activities, but also plays a vital role in stabilizing social system. According to the seminar on the prevention of insurance crime held by the "Institute of Financial Law and Crime Prevention" in 2020, it is pointed out that the estimated loss of insurance crime in 2019 will reach NT$10.5 billion, and it is also pointed out that foreign experience accounts for about 10% of the claim amount. , this potential insurance fraud and adverse selection risk is an important subject of risk control for insurance companies.
This study uses the underwriting cases of Company A in 2018 as the data source, and takes the non-acute medical claims cases within two or three years after the policy takes effect as the target variable. Analyze and discuss six aspects of customer basic information, insurance information, financial information, physical condition information, agent information, and agent claim settlement rate, and use machine learning technology to try eight different classifiers to build underwriting risk prediction models and experiments. From the experimental results of the seven classifiers, it can be seen from the independent variables that the number of medical insurance, the type of primary insurance, the code of the primary insurance and the number of riders under the policy are the best predictors. The performance of the prediction model is based on the gradient boosting machine, which has relatively stable and high prediction ability, and its AUC reaches more than 0.71. For CA scores, logistic regression is the best, reaching above 0.612.
It is hoped that the results of this study can provide a reference for the case company to construct an underwriting risk assessment mechanism for risk classification based on data in the future, and provide a more efficient underwriting assessment and underwriting risk classification through the underwriting risk prediction model. So as to improve the underwriting risk classification and efficiency, speed up automated operations, and improve customer experience and satisfaction, thereby enhancing customer’s stickiness and loyalty with the company.

關鍵字(中)

★ 永續
★ 核保風險預測模型
★ 核保預測

關鍵字(英)

★ Sustainability
★ Underwriting risk prediction model
★ Predictive underwriting

論文目次

摘要 I
ABSTRACT II
圖目錄 III
表目錄 III
第1章緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 5
第2章文獻探討 7
2.1 台灣保險巿場概況 7
2.1.1 台灣保險巿場滲透率及投保率 7
2.1.2 影響保險公司營運重要指標 8
2.1.3 影響損失率的因素 11
2.2 保險契約告知義務與詐欺相關介紹 11
2.3 逆選擇、保險理賠詐欺預測模型相關研究 14
2.4 核保風險預測模型相關研究 23
2.5 總結 32
第3章研究方法 33
3.1 資料來源及蒐集 34
3.2 資料前處理 35
3.3 研究變項說明 35
3.4 機器學習技術 37
3.4.1 隨機森林 38
3.4.2 梯度提升機與極限梯度提升 38
3.4.3 支援向量機 38
3.4.4 邏輯斯迴歸 39
3.4.5 樸素貝葉斯 39
3.4.6 自適應增強 40
3.4.7 類神經網路 40
3.5 分析工具 41
3.6 實驗設計與評估 41
3.6.1 實驗設計 41
3.6.2 評估指標 45
第4章實驗結果與分析 47
4.1 描述性統計分析 47
4.2 實驗結果 53
4.3 變項重要性排序 55
4.4 綜合討論 56
第5章研究結論與建議 59
5.1 研究結論 59
5.2 研究限制 59
5.3 未來研究方向與建議 60
參考文獻 62
英文文獻 62
中文文獻 65
附錄 66
附錄一：第一實驗組主約商品代碼之統計分析 66
附錄二：第二實驗組主約商品代碼之統計分析 67
附錄三：第一實驗組第1、2、3組之混亂矩陣預測結果 68
附錄四：第二實驗組第1、2、3組之混亂矩陣預測結果 69
附錄五：第一實驗組第1、2、3組之核保風險預測模型結果 70
附錄六：第二實驗組第1、2、3組之核保風險預測模型結果 71
附錄七：第一實驗組職業代碼之統計分析 72
附錄八：第二實驗組職業代碼之統計分析 88

參考文獻

英文文獻
Akerlof, G.A., 1970. The Market for “Lemons”: Quality Uncertainty and the Market Mechanism. Q. J. Econ. 84, 488–500. https://doi.org/10.2307/1879431
Akkor, D., Ozyuksel, S., 2020. THE EFFECTS OF NEW TECHNOLOGIES ON THE INSURANCE SECTOR: A PROPOSITION FOR UNDERWRITING QUALIFICATIONS FOR THE FUTURE. Eurasian J. Bus. Manag. 8, 36–50. https://doi.org/10.15604/ejbm.2020.08.01.004
B, J.H., 2020. Risk Level Prediction of Life Insurance Applicant using Machine Learning. Int. J. Adv. Trends Comput. Sci. Eng. 9, 2213.
Biddle, R., Liu, S., Tilocca, P., Xu, G., 2018. Automated Underwriting in Life Insurance: Predictions and Optimisation, in: Databases Theory and Applications. Presented at the Australasian Database Conference, Springer, Cham, pp. 135–146. https://doi.org/10.1007/978-3-319-92013-9_11
Boodhun, N., Jayabalan, M., 2018. Risk prediction in life insurance industry using supervised learning algorithms. Complex Intell. Syst. 4, 145–154. https://doi.org/10.1007/s40747-018-0072-1
Bozyi̇Ği̇T, F., Şahi̇N, M., Gündüz, T., Işik, C., Kilinç, D., 2020. REGRESSION BASED RISK ANALYSIS IN LIFE INSURANCE INDUSTRY. Int. J. Eng. Innov. Res. 2, 178–184. https://doi.org/10.47933/ijeir.745343
Breiman, L., 2001. Random Forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324
Cassimiro, J.C., Santana, A.M., Neto, P.S., Rabelo, R.L., 2017. Investigating the effects of class imbalance in learning the claim authorization process in the Brazilian health care market, in: 2017 International Joint Conference on Neural Networks (IJCNN). Presented at the 2017 International Joint Conference on Neural Networks (IJCNN), pp. 3265–3272. https://doi.org/10.1109/IJCNN.2017.7966265
Cortes, C., Vapnik, V., 1995. Support-vector networks. Mach. Learn. 20, 273–297. https://doi.org/10.1007/BF00994018
Dionne, G. eds, 2013. Handbook of Insurance.
Dutta, K., Chandra, S., Gourisaria, M.K., GM, H., 2021. A Data Mining based Target Regression-Oriented Approach to Modelling of Health Insurance Claims, in: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). Presented at the 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1168–1175. https://doi.org/10.1109/ICCMC51019.2021.9418038
Dwivedi, R.M.S.K., Mishra, A., Gupta, R., 2020. Risk Prediction Assessment In Life Insurance Company Through Dimensionality. Int. J. Sci. Technol. Res. 9, 1528–1532.
Friedman, J., 2001. Greedy function approximation: A gradient boosting machine. undefined.
Friedman, J., Hastie, T., Tibshirani, R., 2000. Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors). Ann. Stat. 28, 337–407. https://doi.org/10.1214/aos/1016218223
Govindarajula, S.G.K., 2019. Classifying risk in life insurance using predictive analytics.
Hanafy, M., Ming, R., 2021. USING MACHINE LEARNING MODELS TO COMPARE VARIOUS RESAMPLING METHODS IN PREDICTING INSURANCE FRAUD. . Vol. 16.
Hassan, A.K.I., Abraham, A., 2016. Modeling Insurance Fraud Detection Using Imbalanced Data Classification, in: Pillay, N., Engelbrecht, A.P., Abraham, A., du Plessis, M.C., Snášel, V., Muda, A.K. (Eds.), Advances in Nature and Biologically Inspired Computing, 進展（AISC，第 419 卷）的一部分. Springer International Publishing, Cham, pp. 117–127. https://doi.org/10.1007/978-3-319-27400-3_11
Haykin, S., 2009. Neural Networks and Learning Machines, 3rd Edition | Pearson [WWW Document]. URL https://www.pearson.com/us/higher-education/program/Haykin-Neural-Networks-and-Learning-Machines-3rd-Edition/PGM320370.html (accessed 5.24.22).
He, C.L., Keirstead, D., Suen, C.Y., 2020. A Hybrid Multiple Classifier System Applied in Life Insurance Underwriting, in: Pattern Recognition and Artificial Intelligence. Presented at the International Conference on Pattern Recognition and Artificial Intelligence, Springer, Cham, pp. 171–176. https://doi.org/10.1007/978-3-030-59830-3_15
Hedengren, D., Stratmann, T., 2016. Is There Adverse Selection in Life Insurance Markets? Econ. Inq. 54, 450–463. https://doi.org/10.1111/ecin.12212
Islam, M.R., Liu, S., Biddle, R., Razzak, I., Wang, X., Tilocca, P., Xu, G., 2021. Discovering dynamic adverse behavior of policyholders in the life insurance industry. Technol. Forecast. Soc. Change 163. https://doi.org/10.1016/j.techfore.2020.120486
Jain, R., Alzubi, J.A., Jain, N., Joshi, P., 2019. Assessing risk in life insurance using ensemble learning. J. Intell. Fuzzy Syst. 37, 2969–2980.
Joudaki, H., Rashidian, A., Minaei-Bidgoli, B., Mahmoodi, M., Geraili, B., Nasiri, M., Arab, M., 2016. Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study. Int. J. Health Policy Manag. 5, 165. https://doi.org/10.15171/ijhpm.2015.196
Katiechi, S.O., 2021. A Federated Learning Model for the Detection of Insurance Claims Fraud (Thesis). University of Nairobi.
Liu, Q., 2019. Research on Risk Management of Big Data and Machine Learning Insurance Based on Internet Finance. J. Phys. Conf. Ser. 1345, 052076. https://doi.org/10.1088/1742-6596/1345/5/052076
Makau, L., Okeyo, W., 2021. Risk Underwriting, Crisis Management, Regulatory Framework and Performance of Insurance Companies in Kenya: A Case of Sanlam General Insurance Company. J. Hum. Resour. Leadersh. 5, 96–113. https://doi.org/10.53819/10.53819/81018102t3026
Mustika, W.F., Murfi, H., Widyaningsih, Y., 2019. Analysis Accuracy of XGBoost Model for Multiclass Classification - A Case Study of Applicant Level Risk Prediction for Life Insurance, in: 2019 5th International Conference on Science in Information Technology (ICSITech). Presented at the 2019 5th International Conference on Science in Information Technology (ICSITech), pp. 71–77. https://doi.org/10.1109/ICSITech46713.2019.8987474
Pai, D.D., Agnihotri, P., Jha, B.K., 2016. Applications of Data Mining in Detecting Fraudulent Health Insurance Claim. Int. J. Eng. Res. 4, 4.
Paruchuri, H., 2020. The Impact of Machine Learning on the Future of Insurance Industry. Am. J. Trade Policy 7, 85–90. https://doi.org/10.18034/ajtp.v7i3.537
Patil, T.R., Sherekar, S., 2013. Performance Analysis of Naive Bayes and J 48 Classification Algorithm for Data Classification. undefined.
Powell, D., Goldman, D., 2021. Disentangling moral hazard and adverse selection in private health insurance. Ann. Issue Struct. Econom. Honor. Daniel McFadden 222, 141–160. https://doi.org/10.1016/j.jeconom.2020.07.030
Reliable Accuracy Estimates from k-Fold Cross Validation [WWW Document], n.d. URL https://ieeexplore.ieee.org/abstract/document/8698831/?casa_token=4cKdTZlFReAAAAAA:7_BvuMpPPbJyGf6-H3PR_ijSFkNblgSXy9pTrWxG-l2saB804xg3ZfDgbe8VrnAHW_De2j6_ (accessed 5.22.22).
Rothschild, M., Stiglitz, J., 1976. Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information*. Q. J. Econ. 90, 629–649. https://doi.org/10.2307/1885326
Saripalli, P., Tirumala, V., Chimmad, A., 2017. Assessment of healthcare claims rejection risk using machine learning, in: 2017 IEEE 19th International Conference on E-Health Networking, Applications and Services (Healthcom). Presented at the 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 1–6. https://doi.org/10.1109/HealthCom.2017.8210758
Seema Rawat, Aakankshu Rawat, Deepak Kumar, A. Sai Sabitha, 2021. Application of machine learning and data visualization techniques for decision support in the insurance sector. Int. J. Inf. Manag. Data Insights 1, 100012. https://doi.org/10.1016/j.jjimei.2021.100012
Sengupta, R., Rooj, D., 2019. The effect of health insurance on hospitalization: Identification of adverse selection, moral hazard and the vulnerable population in the Indian healthcare market. World Dev. 122, 110–129. https://doi.org/10.1016/j.worlddev.2019.05.012
Singh A., Ramkumar K.R., 2021. Risk assessment for health insurance using equation modeling and machine learning. Int. J. Knowl.-Based Intell. Eng. Syst. 25, 201–225. https://doi.org/10.3233/KES-210065
T. Wong, P. Yeh, 2020. Reliable Accuracy Estimates from k-Fold Cross Validation. IEEE Trans. Knowl. Data Eng. 32, 1586–1594. https://doi.org/10.1109/TKDE.2019.2912815
Thota, S., Sai, K.V., Swarnalatha, P., 2021. Machine Learning Implementation for Health insurance. Int. J. Adv. Trends Comput. Sci. Eng. 10, 2006–2011. https://doi.org/10.30534/ijatcse/2021/721032021
Vaughan, E.J., Vaughan, T., 2008. Fundamentals of Risk and Insurance, Tenth Edition Book [WWW Document]. Skillsoft. URL https://www.skillsoft.com/book/fundamentals-of-risk-and-insurance-tenth-edition-b0dfbf80-efed-11e6-8ce3-0242c0a80c07 (accessed 6.4.22).
Wang, P., 2021. Predictive Machine Learning for Underwriting Life and Health Insurance | Institute and Faculty of Actuaries [WWW Document]. URL https://www.actuaries.org.uk/learn-develop/attend-event/predictive-machine-learning-underwriting-life-and-health-insurance (accessed 6.4.22).
Yang, B., 2021. Machine Learning in Insurance Underwriting Context, in: 2021 International Conference on Electronic Information Technology and Smart Agriculture (ICEITSA). Presented at the 2021 International Conference on Electronic Information Technology and Smart Agriculture (ICEITSA), pp. 478–484. https://doi.org/10.1109/ICEITSA54226.2021.00097

中文文獻
余建岳, 2017. 壽險業利用大數據建構醫療保險詐欺風控決策之研究. 風險管理與保險學系碩士在職專班. 銘傳大學, 台北市.
張簡淑美, 2019. 基於機器學習建立健康保險理賠風險評估模型. 資訊管理學研究所. 國立臺灣大學, 台北市.
洪緯倫, 2016. 終身醫療保險短期出險因素之探討. 金融系金融資訊碩士在職專班. 國立高雄應用科技大學, 高雄市.
范振庭, 2016. 個人醫療險道德危險及理賠因素之研究—以S壽險公司為例. 風險管理與保險學系碩士在職專班. 銘傳大學, 台北市.
蔡政翰, 2016. 商業醫療保險短期出險因素之探討分析-以C壽險公司為例. 財富與稅務管理系碩士在職專班. 國立高雄應用科技大學, 高雄市.
鄭鎮樑, 2021. 保險學原理─精華版. 五南圖書出版股份有限公司.

指導教授

胡雅涵(Ya-Han Hu)

審核日期

2022-9-26

推文