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題名 超個人化定價與風險分攤：行為經濟學視角下的保險模式轉型研究
Hyper-Personalized Pricing and Risk Pooling: A study on the Transformation of Insurance Models from a Behavioral Economics Perspective作者 邱振淵
Chiu, Cheng-Yuan貢獻者 周冠男<br>梁嘉紋
Chou, Kuan-Nan<br>Liang, Chia-Wen
邱振淵
Chiu, Cheng-Yuan關鍵詞 超個人化定價
行為經濟學
保險科技
數據驅動
精算公平
保險普惠性
Hyper-personalized Pricing
Behavioral Economics
InsurTech
Data-driven
Actuarial Fairness
Insurance Inclusivity日期 2025 上傳時間 4-Aug-2025 13:03:45 (UTC+8) 摘要 隨著數位科技、大數據及人工智慧的迅速發展，傳統保險業正面臨一場深刻的變革。「超個人化定價」即為這波轉型的重要趨勢，其透過精細化的個人資料（如駕駛行為、健康監測、消費習慣等）進行風險評估與保費訂定，徹底改變了傳統以群體風險平均化為核心的保險模式。本研究旨在從行為經濟學的視角深入探討超個人化定價對保險業風險分攤機制、市場結構及消費者行為的影響。 透過文獻分析與問卷調查，本研究發現超個人化定價帶來了一系列正負交織的影響。在正面效益上，精準的風險評估與定價能提升市場效率，降低風險個體的保費，激發消費者改善自身風險行為的動機，且迎合現代消費者對個性化及透明化服務的期待。然而，另一方面，此一精細化定價可能導致市場分化，高風險群體因高昂的保費負擔而逐步被排除於保險市場之外，弱化了傳統風險池化與互助的功能，引發公平性及社會包容性的嚴重疑慮。 此外，本研究進一步指出，消費者在面對超個人化定價時表現出顯著的行為偏誤，包括對懲罰性定價的強烈反感（損失規避）、對隱私及數據安全的顧慮、框架效應下對獎勵式方案的偏好，以及對市場公平性敏感的心理反應等。這些心理因素影響了消費者對超個人化方案的接受程度與實際參與行為，揭示了市場在推動超個人化定價時需謹慎設計及調整策略的重要性。 在實務應用層面，本研究提出具體的策略建議，包括採取獎勵而非懲罰的框架設計、強化透明與信任機制、運用社群與遊戲化等行為經濟學的推力工具，以有效提高消費者的接受度及參與意願。透過政府監理與第三方認證機制的導入，更可進一步強化市場公平性與透明性。 本研究期望透過深入探討超個人化定價的理論基礎與行為經濟學應用，提供保險公司與政策制定者前瞻且務實的參考依據，以在商業利益與社會責任間取得適切的平衡，推動保險市場永續且健康的發展。
With the rapid advancement of digital technology, big data, and artificial intelligence, the traditional insurance industry is undergoing profound transformation. "Hyper-personalized pricing" is a pivotal trend in this transformation, utilizing highly detailed personal data (such as driving behavior, health monitoring, and consumption habits) for risk assessment and premium setting. This approach fundamentally alters the traditional insurance model based on the averaging of collective risks. This research aims to thoroughly examine the impact of hyper-personalized pricing on the risk pooling mechanisms, market structure, and consumer behavior in the insurance industry from the perspective of behavioral economics. Through literature review and questionnaire surveys, this study identifies a series of intertwined positive and negative impacts of hyper-personalized pricing. On the positive side, precise risk assessment and pricing enhance market efficiency, reduce premiums for low-risk individuals, stimulate consumers' motivation to improve their risk behaviors, and align with modern consumer expectations for personalized and transparent services. Conversely, this refined pricing may result in market segmentation, excluding high-risk groups from insurance coverage due to prohibitively high premiums, thus weakening traditional risk-pooling and mutual aid functions, raising significant concerns about fairness and social inclusivity. Additionally, the study highlights notable behavioral biases demonstrated by consumers when faced with hyper-personalized pricing. These biases include a strong aversion to punitive pricing (loss aversion), concerns over privacy and data security, a preference for reward-based schemes under framing effects, and sensitivity towards market fairness. Such psychological factors critically affect consumer acceptance and participation, underscoring the necessity for cautious strategy design and adjustments in promoting hyper-personalized pricing in the marketplace. In practical terms, this study offers specific strategic recommendations, including employing reward-based rather than punitive frameworks, enhancing transparency and trust mechanisms, and utilizing behavioral economics tools such as community engagement and gamification to effectively increase consumer acceptance and willingness to participate. Furthermore, incorporating government supervision and third-party certification mechanisms can further strengthen market fairness and transparency. By deeply exploring the theoretical foundations and behavioral economic applications of hyper-personalized pricing, this research aims to provide forward-looking and pragmatic references for insurers and policymakers to achieve an appropriate balance between commercial interests and social responsibilities, thereby promoting sustainable and healthy development within the insurance market.參考文獻 • Kunreuther, H., & Pauly, M. (2017). Insurance Decision-Making and Market Behavior. Foundations and Trends in Microeconomics, 10(3–4), 153–298. • Root, N. (2023). Is the law of large numbers still relevant to insurance. Actuarial Post. Is the law of large numbers still relevant to insurance • Karnani, M. Calvo, R. Yamashiro, T. A. (2024). The Future of Insurance: Embracing Hyperpersonalization. NTT Data Insurance. The Future of Insurance • Abraham, K. S., Schwarcz, D. B., & Logue, K. D. (2020). Insurance law and policy: Cases and materials. Wolters Kluwer Law & Business. • Progressive Insurance. (2012). Snapshot program: The role of telematics in auto insurance pricing. Progressive Insurance White Paper. https://www.progressive.com • Sadowski, J. (2020). Draining the Risk Pool - Insurance companies are using new surveillance tech to discipline customers. REAL LIFE. Draining the Risk Pool • Deloitte. (2016). Impact of Big Data on the Future of Insurance. Actuaries Institute Green Paper. Impact of Big Data on the Future of Insurance • Handel, B. R., Hendel, I., & Whinston, M. D. (2015). Equilibria in health exchanges: Adverse selection vs. reclassification risk. Econometrica, 83(4), 1261-1313. • Wassenaar, J. (2016). Behavioural Economics and Insurance: An Actuary’s View. RGA. Behavioural Economics and Insurance: An Actuary’s View • Soliño-Fernandez, D., Ding, A., Bayro-Kaiser, E., & Ding, E. (2020). Willingness to adopt wearable devices with behavioral and economic incentives by health insurance wellness programs: Results of a US cross-sectional survey with multiple consumer health vignettes. BMC Public Health, 19, Article 1649. https://doi.org/10.1186/s12889-019-7920-9 • Lin, X., Liu, Y., & Wei, W. (2021). Personalized insurance pricing in the era of big data: Risks and regulatory challenges. Insurance Markets and Companies, 12(1), 33-50. • Baker, T., & Siegelman, P. (2014). Behavioral Economics and Insurance Law: The Importance of Equilibrium Analysis. The Oxford handbook of behavioral economics and the law (pp. 491–517). Oxford University Press. • Kuryłowicz, Ł. (2021). The value of privacy: Empirical research using drivers as an example. European Research Studies Journal. The value of privacy • Johnson, E. J., Shu, S. B., Dellaert, B. G., Fox, C., Goldstein, D. G., Häubl, G., ... & Weber, E. U. (2013). Beyond nudges: Tools of a choice architecture. Marketing Letters, 24(2), 487-504. • Prince, A. E. R. (2017). Insurance risk classification in an era of genomics: Is a rational discrimination policy rational? Nebraska Law Review, 96(3), 624–687. Insurance risk classification in an era of genomics • Scordis, N. A. (2025). Transparency in place of fair insurance pricing. Journal of Business Ethics. Advance online publication. https://doi.org/10.1007/s10551-025-05966-2 • Ma, M. (2022). The operation of China’s insurance industry in the context of big data: Dilemmas, challenges and countermeasures. Beijing Law Review, 13(4), 853–863. https://doi.org/10.4236/blr.2022.134056 • Sadowski, J. (2024). Total life insurance: Logics of anticipatory control and actuarial governance in insurance technology. Social Studies of Science, 54(2), 231–256. Total life insurance: Logics of anticipatory control and actuarial governance in insurance technology - PMC • Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press. • Arrow, K. J. (1963). Uncertainty and the welfare economics of medical care. American Economic Review, 53(5), 941-973. • Dionne, G. (2013). Risk management: History, definition, and critique. Risk Management and Insurance Review, 16(2), 147-166. • Investopedia (2021). The Law of Large Numbers in the Insurance Industry (The Law of Large Numbers in the Insurance Industry) • Harrington, S. E., & Niehaus, G. R. (2004). Risk management and insurance. McGraw-Hill. • Rothschild, M., & Stiglitz, J. (1976). Equilibrium in competitive insurance markets. Quarterly Journal of Economics, 90(4), 629-649. • Thompson, G. (2024). The Law of Large Numbers. IRMI Expert Commentary (The Law of Large Numbers) • Ersoy, E., & Banerjee, S. (2021). Big Data Analytics in Usage-Based Insurance: Opportunities and Challenges. Insurance Technology Journal, 12(2), 45–62. • EasySend (2023). Hyper-personalization in insurance: leveraging Big Data and AI for customized policies. Hyper-personalization in insurance: leveraging Big Data and AI for customized policies | EasySend • Barry, L., & Charpentier, A. (2020). Personalization as a promise: Can Big Data change the practice of insurance? Big Data & Society, 7(1), 1–13. PDF_Personalization as a promise: Can Big Data change the practice of insurance? • Littlejohns P. (2020). Aviva’s Colm Holmes on claims inflation, stupid drivers and commercial insurance. NS Insurance. https://www.nsinsurance.com/analysis/aviva-colm-holmes-claims-inflation/ • EIOPA. (2021). Artificial intelligence governance principles: towards ethical and trustworthy artificial intelligence in the European insurance sector. EIOPA • Harrison GW, Ng JM. Behavioral insurance and economic theory: A literature review. Risk Manag Insur Rev. 2019;22:133–182. https://doi.org/10.1111/rmir.12119 • 趙強, 彭海林. (2022). 基於消費者心理的科技賦能 保險客戶黏性提升路徑研究. 上海保險雜誌. • Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux. • Kunreuther, H. (2013). Improving Insurance Decisions in the Most Misunderstood Industry. Remarks for the U.S. Senate Small Business Committee Roundtable. Improving Insurance Decisions • Thaler, R. H. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–214. • Nelson, E. (2019). Driving Data: The cost and benefits of usage-based auto insurance. Purdue University Mitch Daniels Schools of Business. Driving Data • Loi, M., et al. (2023). Fairness and risk: An ethical argument for a group fairness definition insurers can use. SN Business & Economics, 3(6), 151. • Skraburski, M (2023). The great compromise: Balancing Pricing Transparency and Accuracy. Quantee.ai. The great compromise • Finkelstein, A., & Poterba, J. (2014). Testing for adverse selection with "unused observables". Journal of Risk and Insurance, 81(4), 709-734. https://doi.org/10.1111/jori.12037 • Ayuso, M., Guillen, M., & Pérez-Marín, A. M. (2019). Measuring the impact of telematics on the compulsory automobile insurance cover. Risks, 7(1), 1-16. https://doi.org/10.3390/risks7010024 • Handel, B. R., & Kolstad, J. T. (2015). Health insurance for “humans”: Information frictions, plan choice, and consumer welfare. American Economic Review, 105(8), 2449-2500. https://doi.org/10.1257/aer.20131126 • Einav, L., Finkelstein, A., & Schrimpf, P. (2010). Optimal mandates and the welfare cost of asymmetric information: Evidence from the U.K. annuity market. Econometrica, 78(3), 1031-1092. https://doi.org/10.3982/ECTA7245 • John Hancock. (2018). Vitality program overview: Leveraging wearable devices in life insurance. John Hancock Life Insurance Company Report. https://www.johnhancock.com • U.S. Federal Trade Commission. (2022). Regulatory framework for algorithmic pricing in insurance. Federal Register. • European Commission. (2020). Guidelines on automated decision-making and profiling in insurance. European Insurance and Occupational Pensions Authority. • Niedośpiał, L. (2024). Power of Personalization in Insurance: How Tailored Experiences Are Redefining the Industry. Higson. Power of Personalization in Insurance • Capco. (2021). 72% of Consumers Would Share Personal Data to Get Cheaper Insurance Premiums. Businesswire. 72% of Consumers Would Share Personal Data • Accenture. (2021). Guide insurance customers to safety and well-being. Accenture Global Insurance Customer Study 2021. • Accenture. (2021). More Consumers Willing to Share Lifestyle Data for Behavior-Based Insurance Premiums, But Trust in Data Security Falls, Accenture Report Finds. Accenture Newsroom. Accenture Newsroom • Capco. (2023). New Capco Insurance Survey Confirms US Policyholders’ Desire For Personalized Offerings. Businesswire. New Capco Insurance Survey Confirms • Open AI. (2025). ChatGPT [Large Language Model]. https://chatgpt.com • Barry, L., Charpentier, A. (2020). Personalization as a promise: Can Big Data change the practice of insurance? Big Data & Society. • Eling, M., Gemmo, I., Guxha, D., Schmeiser, H. (2024). Big data, risk classification, and privacy in insurance markets. The Geneva Papers on Risk and Insurance Theory. • Soyer, B. (2022). USE OF BIG DATA ANALYTICS AND SENSOR TECHNOLOGY IN CONSUMER INSURANCE CONTEXT: LEGAL AND PRACTICAL CHALLENGES. The Cambridge Law Journal. 描述 碩士
國立政治大學
經營管理碩士學程(EMBA)
112932107資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112932107 資料類型 thesis dc.contributor.advisor 周冠男<br>梁嘉紋 zh_TW dc.contributor.advisor Chou, Kuan-Nan<br>Liang, Chia-Wen en_US dc.contributor.author (Authors) 邱振淵 zh_TW dc.contributor.author (Authors) Chiu, Cheng-Yuan en_US dc.creator (作者) 邱振淵 zh_TW dc.creator (作者) Chiu, Cheng-Yuan en_US dc.date (日期) 2025 en_US dc.date.accessioned 4-Aug-2025 13:03:45 (UTC+8) - dc.date.available 4-Aug-2025 13:03:45 (UTC+8) - dc.date.issued (上傳時間) 4-Aug-2025 13:03:45 (UTC+8) - dc.identifier (Other Identifiers) G0112932107 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158342 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 經營管理碩士學程(EMBA) zh_TW dc.description (描述) 112932107 zh_TW dc.description.abstract (摘要) 隨著數位科技、大數據及人工智慧的迅速發展，傳統保險業正面臨一場深刻的變革。「超個人化定價」即為這波轉型的重要趨勢，其透過精細化的個人資料（如駕駛行為、健康監測、消費習慣等）進行風險評估與保費訂定，徹底改變了傳統以群體風險平均化為核心的保險模式。本研究旨在從行為經濟學的視角深入探討超個人化定價對保險業風險分攤機制、市場結構及消費者行為的影響。 透過文獻分析與問卷調查，本研究發現超個人化定價帶來了一系列正負交織的影響。在正面效益上，精準的風險評估與定價能提升市場效率，降低風險個體的保費，激發消費者改善自身風險行為的動機，且迎合現代消費者對個性化及透明化服務的期待。然而，另一方面，此一精細化定價可能導致市場分化，高風險群體因高昂的保費負擔而逐步被排除於保險市場之外，弱化了傳統風險池化與互助的功能，引發公平性及社會包容性的嚴重疑慮。 此外，本研究進一步指出，消費者在面對超個人化定價時表現出顯著的行為偏誤，包括對懲罰性定價的強烈反感（損失規避）、對隱私及數據安全的顧慮、框架效應下對獎勵式方案的偏好，以及對市場公平性敏感的心理反應等。這些心理因素影響了消費者對超個人化方案的接受程度與實際參與行為，揭示了市場在推動超個人化定價時需謹慎設計及調整策略的重要性。 在實務應用層面，本研究提出具體的策略建議，包括採取獎勵而非懲罰的框架設計、強化透明與信任機制、運用社群與遊戲化等行為經濟學的推力工具，以有效提高消費者的接受度及參與意願。透過政府監理與第三方認證機制的導入，更可進一步強化市場公平性與透明性。 本研究期望透過深入探討超個人化定價的理論基礎與行為經濟學應用，提供保險公司與政策制定者前瞻且務實的參考依據，以在商業利益與社會責任間取得適切的平衡，推動保險市場永續且健康的發展。 zh_TW dc.description.abstract (摘要) With the rapid advancement of digital technology, big data, and artificial intelligence, the traditional insurance industry is undergoing profound transformation. "Hyper-personalized pricing" is a pivotal trend in this transformation, utilizing highly detailed personal data (such as driving behavior, health monitoring, and consumption habits) for risk assessment and premium setting. This approach fundamentally alters the traditional insurance model based on the averaging of collective risks. This research aims to thoroughly examine the impact of hyper-personalized pricing on the risk pooling mechanisms, market structure, and consumer behavior in the insurance industry from the perspective of behavioral economics. Through literature review and questionnaire surveys, this study identifies a series of intertwined positive and negative impacts of hyper-personalized pricing. On the positive side, precise risk assessment and pricing enhance market efficiency, reduce premiums for low-risk individuals, stimulate consumers' motivation to improve their risk behaviors, and align with modern consumer expectations for personalized and transparent services. Conversely, this refined pricing may result in market segmentation, excluding high-risk groups from insurance coverage due to prohibitively high premiums, thus weakening traditional risk-pooling and mutual aid functions, raising significant concerns about fairness and social inclusivity. Additionally, the study highlights notable behavioral biases demonstrated by consumers when faced with hyper-personalized pricing. These biases include a strong aversion to punitive pricing (loss aversion), concerns over privacy and data security, a preference for reward-based schemes under framing effects, and sensitivity towards market fairness. Such psychological factors critically affect consumer acceptance and participation, underscoring the necessity for cautious strategy design and adjustments in promoting hyper-personalized pricing in the marketplace. In practical terms, this study offers specific strategic recommendations, including employing reward-based rather than punitive frameworks, enhancing transparency and trust mechanisms, and utilizing behavioral economics tools such as community engagement and gamification to effectively increase consumer acceptance and willingness to participate. Furthermore, incorporating government supervision and third-party certification mechanisms can further strengthen market fairness and transparency. By deeply exploring the theoretical foundations and behavioral economic applications of hyper-personalized pricing, this research aims to provide forward-looking and pragmatic references for insurers and policymakers to achieve an appropriate balance between commercial interests and social responsibilities, thereby promoting sustainable and healthy development within the insurance market. en_US dc.description.tableofcontents 第一章 前言 第一節 研究背景與動機…………………………………8 第二節 研究問題與目的…………………………………13 第三節 研究範圍與限制…………………………………18 第二章 文獻回顧 第一節 保險基礎理論與大數法則………………22 第二節 超個人化保險定價………………………………24 第三節 行為經濟學在保險領域的應用……………26 第四節 歸納研究缺口……………32 第三章 研究架構與假設 第一節 研究架構說明……………36 第二節 研究假設………………38 第四章 研究方法 第一節 研究設計概述………………40 第二節 問卷設計……………………43 第三節 資料分析…………………48 第五章 研究結果與討論 第一節 假設驗證分析……………57 第二節 開放題彙整：主題分析……………61 第三節 群體偏好差異分析…………………65 第六章 結論 第一節 研究主要發現………………69 第二節 對實務的建議………………71 第三節 研究限制…………………77 第四節 未來研究方向………………78 參考文獻………………80 附錄：問卷調查原始資料………………86 zh_TW dc.format.extent 2155433 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112932107 en_US dc.subject (關鍵詞) 超個人化定價 zh_TW dc.subject (關鍵詞) 行為經濟學 zh_TW dc.subject (關鍵詞) 保險科技 zh_TW dc.subject (關鍵詞) 數據驅動 zh_TW dc.subject (關鍵詞) 精算公平 zh_TW dc.subject (關鍵詞) 保險普惠性 zh_TW dc.subject (關鍵詞) Hyper-personalized Pricing en_US dc.subject (關鍵詞) Behavioral Economics en_US dc.subject (關鍵詞) InsurTech en_US dc.subject (關鍵詞) Data-driven en_US dc.subject (關鍵詞) Actuarial Fairness en_US dc.subject (關鍵詞) Insurance Inclusivity en_US dc.title (題名) 超個人化定價與風險分攤：行為經濟學視角下的保險模式轉型研究 zh_TW dc.title (題名) Hyper-Personalized Pricing and Risk Pooling: A study on the Transformation of Insurance Models from a Behavioral Economics Perspective en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) • Kunreuther, H., & Pauly, M. 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Impact of Big Data on the Future of Insurance • Handel, B. R., Hendel, I., & Whinston, M. D. (2015). Equilibria in health exchanges: Adverse selection vs. reclassification risk. Econometrica, 83(4), 1261-1313. • Wassenaar, J. (2016). Behavioural Economics and Insurance: An Actuary’s View. RGA. Behavioural Economics and Insurance: An Actuary’s View • Soliño-Fernandez, D., Ding, A., Bayro-Kaiser, E., & Ding, E. (2020). Willingness to adopt wearable devices with behavioral and economic incentives by health insurance wellness programs: Results of a US cross-sectional survey with multiple consumer health vignettes. BMC Public Health, 19, Article 1649. https://doi.org/10.1186/s12889-019-7920-9 • Lin, X., Liu, Y., & Wei, W. (2021). Personalized insurance pricing in the era of big data: Risks and regulatory challenges. Insurance Markets and Companies, 12(1), 33-50. • Baker, T., & Siegelman, P. (2014). Behavioral Economics and Insurance Law: The Importance of Equilibrium Analysis. 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