聲明：本系列文章基于原期刊目錄和摘要內(nèi)容整理而得，僅限于讀者交流學(xué)習(xí)。如有侵權(quán)，請聯(lián)系刪除。

期刊介紹：

《Journal of Risk and Insurance》為季刊，每年4期，每期發(fā)表文章8-10篇左右。2024年影響因子為2.1，是風(fēng)險(xiǎn)管理與保險(xiǎn)領(lǐng)域的頂級權(quán)威學(xué)術(shù)期刊。該期刊主要發(fā)表保險(xiǎn)經(jīng)濟(jì)學(xué)和風(fēng)險(xiǎn)管理主題的理論和實(shí)證方面的學(xué)術(shù)論文，可以為保險(xiǎn)市場的實(shí)踐、決策和監(jiān)管以及企業(yè)和家庭風(fēng)險(xiǎn)管理提供重要的信息。

本期看點(diǎn)：

●災(zāi)害風(fēng)險(xiǎn)最優(yōu)公私共擔(dān)機(jī)制：最優(yōu)的政府干預(yù)策略依賴于個(gè)體損失之間的相關(guān)程度，當(dāng)損失呈現(xiàn)中等程度正相關(guān)時(shí)，政府通過事后救助計(jì)劃來補(bǔ)充私人保險(xiǎn)是最優(yōu)方案。而當(dāng)損失相關(guān)性較高時(shí)，政府再保險(xiǎn)成為更優(yōu)選擇，盡管在政府效率不及私營企業(yè)的情況下，并不會完全替代私人保險(xiǎn)。

●首次經(jīng)歷洪水的周邊居民會上調(diào)對洪水風(fēng)險(xiǎn)的認(rèn)知，并開始主動購買洪水保險(xiǎn)。

●洪災(zāi)發(fā)生后，社區(qū)在參與率和行動強(qiáng)度兩個(gè)方面的風(fēng)險(xiǎn)緩解活動均顯著增加。社區(qū)具備一定的適應(yīng)能力，但也暴露出以災(zāi)害驅(qū)動為主的響應(yīng)機(jī)制存在效率低下的問題，并引發(fā)了社區(qū)間響應(yīng)能力差異帶來的公平性擔(dān)憂。

●一種新型神經(jīng)網(wǎng)絡(luò)架構(gòu)可以提升對已報(bào)告但尚未結(jié)案賠案的損失金額預(yù)測能力，機(jī)器學(xué)習(xí)在提升精算預(yù)測能力方面具有很大的潛力，保險(xiǎn)公司應(yīng)逐步轉(zhuǎn)向更具粒度的數(shù)據(jù)應(yīng)用方式。

●基于大型語言模型的通用性文本差異分析框架，拓展了其在保險(xiǎn)科技與風(fēng)險(xiǎn)管理領(lǐng)域中的應(yīng)用場景。

●利用機(jī)器學(xué)習(xí)規(guī)范保險(xiǎn)行業(yè)的風(fēng)險(xiǎn)文化發(fā)現(xiàn)，保險(xiǎn)公司的風(fēng)險(xiǎn)文化受到不確定風(fēng)險(xiǎn)戰(zhàn)略、在風(fēng)險(xiǎn)定義、執(zhí)行與報(bào)告方面的限制、訴訟決策以及風(fēng)險(xiǎn)管理實(shí)踐等因素的顯著影響。

●科羅拉多州“Peak健康聯(lián)盟”的設(shè)立提升了保險(xiǎn)公司的市場議價(jià)能力，并促使平均保費(fèi)下降，保費(fèi)下降主要?dú)w因于醫(yī)療服務(wù)價(jià)格的降低，而非覆蓋范圍或風(fēng)險(xiǎn)結(jié)構(gòu)的變化。

※ 本期目錄

●Catastrophe risk sharing among individuals, private insurance, and government

●Learning from experience: Flooding and insurance take-up in the flood zone and its periphery

●Community responses to flooding in risk mitigation actions: Evidence from the community rating system

●Advancing loss reserving: A hybrid neural network approach for individual claim development prediction

●Cyber risk assessment for capital management

●Banding together to lower the cost of health care? An empirical study of the Peak Health Alliance in Colorado

●Textual analysis of insurance claims with large language models

●Regulating risk culture in the insurance industry using machine learning

Catastrophe risk sharing among individuals, private insurance, and government

個(gè)體、私人保險(xiǎn)與政府之間的災(zāi)害風(fēng)險(xiǎn)共擔(dān)機(jī)制研究

作者

Ruo Jia（北京大學(xué)經(jīng)濟(jì)學(xué)院），Jieyu Lin（嶺南大學(xué)商學(xué)院），Michael R. Powers（清華大學(xué)經(jīng)濟(jì)管理學(xué)院/蘇世民書院），Hanyang Wang（印第安納大學(xué)凱利商學(xué)院）

摘要：Limited research has been conducted on the optimal public–private risk-sharing for catastrophe risks. This paper develops a theoretical framework to study the risk-sharing decisions and interactions of three types of catastrophe-market participants: a large number of individuals, a large number of private insurers in a competitive market, and a government that can choose between alternatives of re/insurance or ex post relief. Our analysis shows that the optimal government intervention varies depending on the correlation levels among individual losses. For moderately positive levels of loss correlation, it is optimal for the government to offer an ex post relief program to supplement private insurance. However, for higher levels of loss correlation, government reinsurance becomes optimal, although not to the extent of replacing private insurance if the government is less efficient than private firms. In sum, as catastrophe-loss correlations increase, that is, as the risk becomes more catastrophic, more risk-sharing tools and funding are needed to maximize social welfare.

目前關(guān)于災(zāi)害風(fēng)險(xiǎn)最優(yōu)公私共擔(dān)機(jī)制的研究仍較為有限。本文構(gòu)建了一個(gè)理論框架，以分析三類災(zāi)害市場參與者——大量個(gè)體、大量處于競爭市場中的私人保險(xiǎn)公司，以及可以在再保險(xiǎn)或事后救助之間進(jìn)行選擇的政府——在風(fēng)險(xiǎn)分擔(dān)中的決策與相互作用。研究發(fā)現(xiàn)，最優(yōu)的政府干預(yù)策略依賴于個(gè)體損失之間的相關(guān)程度。當(dāng)損失呈現(xiàn)中等程度正相關(guān)時(shí)，政府通過事后救助計(jì)劃來補(bǔ)充私人保險(xiǎn)是最優(yōu)方案。而當(dāng)損失相關(guān)性較高時(shí)，政府再保險(xiǎn)成為更優(yōu)選擇，盡管在政府效率不及私營企業(yè)的情況下，并不會完全替代私人保險(xiǎn)?？傮w而言，隨著災(zāi)害損失相關(guān)性的上升，即風(fēng)險(xiǎn)變得更加系統(tǒng)性和災(zāi)難性，社會福利最大化所需的風(fēng)險(xiǎn)共擔(dān)工具和資金支持也隨之增加。

原文鏈接：https://onlinelibrary.wiley.com/doi/10.1111/jori.12506

Learning from experience: Flooding and insurance take-up in the flood zone and its periphery

經(jīng)驗(yàn)驅(qū)動下的學(xué)習(xí)：洪水事件對洪水區(qū)及其周邊地區(qū)保險(xiǎn)購買行為的影響

作者

Ivan Petkov（紐約城市大學(xué)皇后學(xué)院），F(xiàn)rancesc Ortega（紐約城市大學(xué)皇后學(xué)院）

摘要：Flood insurance take-up remains low outside of the 100-year flood zone (SFHA), where purchasing insurance is entirely voluntary, despite the availability of affordable policies. Merging building footprints and inundation data for a large-scale flooding episode, we document substantial flood risk in the periphery of the SFHA and show that the storm led to large increases in take-up. But, while in the SFHA, the increase vanished after 3 years, it was highly persistent in the periphery. The extent of flooding and the type of policies purchased indicate that periphery residents who experienced flooding for the first time revised upwardly their beliefs about flood risk and began purchasing flood insurance. We also argue that increased granularity in flood risk communication could increase take-up before catastrophic flooding occurs.

盡管可獲得價(jià)格合理的保單，在100年一遇洪水區(qū)（SFHA）以外的地區(qū)，洪水保險(xiǎn)的購買率仍然較低，因?yàn)樵谶@些地區(qū)購買保險(xiǎn)完全屬于自愿行為。本文將建筑物輪廓數(shù)據(jù)與一次大規(guī)模洪災(zāi)的淹沒數(shù)據(jù)相結(jié)合，記錄了SFHA周邊地區(qū)存在顯著的洪水風(fēng)險(xiǎn)，并發(fā)現(xiàn)該次風(fēng)暴導(dǎo)致洪水保險(xiǎn)購買率大幅上升。然而，在SFHA內(nèi)部，這一上升趨勢在三年后消失，而在周邊地區(qū)卻表現(xiàn)出高度的持續(xù)性。洪災(zāi)的嚴(yán)重程度以及所購買保單的類型表明，那些首次經(jīng)歷洪水的周邊居民上調(diào)了對洪水風(fēng)險(xiǎn)的認(rèn)知，并開始主動購買洪水保險(xiǎn)。我們進(jìn)一步指出，如果洪水風(fēng)險(xiǎn)溝通更加細(xì)化，有望在災(zāi)難性洪水發(fā)生之前提高保險(xiǎn)覆蓋率。

原文鏈接：https://onlinelibrary.wiley.com/doi/10.1111/jori.12506

Community responses to flooding in risk mitigation actions: Evidence from the community rating system

社區(qū)應(yīng)對洪災(zāi)的風(fēng)險(xiǎn)緩解行為：來自社區(qū)評級系統(tǒng)的證據(jù)

作者

Yanjun（Penny）Liao（未來資源研究所Resources For the Future），Simon S?lvsten（歐洲風(fēng)險(xiǎn)與韌性研究中心），Zachary Whitlock（未來資源研究所Resources For the Future）

摘要：This paper studies the impact of disaster experiences on communities' engagement in risk mitigation actions, focusing on flooding in the United States. We measure risk mitigation actions using communities' scores in the Community Rating System, an incentive program that scores flood preparedness and mitigation activities and rewards communities with flood insurance premium discounts. Leveraging a panel of communities from 1998 to 2019, we find a significant increase in risk mitigation activities following flood events, in both participation rates and intensity of actions. The effects continue to increase up to 10 years. Communities with greater capacity, particularly those in urban areas, exhibit a much stronger response. The findings highlight the adaptive capacity of communities but also raise several concerns regarding the inefficiency of disaster-driven responses and inequitable outcomes across communities.

本文研究災(zāi)害經(jīng)歷對社區(qū)參與風(fēng)險(xiǎn)緩解行為的影響，重點(diǎn)關(guān)注美國的洪水風(fēng)險(xiǎn)應(yīng)對。我們通過“社區(qū)評級系統(tǒng)”（Community Rating System，CRS）中的評分來衡量社區(qū)的風(fēng)險(xiǎn)緩解行為。CRS是一項(xiàng)激勵(lì)計(jì)劃，用于評估社區(qū)在洪水防范和緩解方面的措施，并通過降低洪水保險(xiǎn)保費(fèi)來獎勵(lì)得分較高的社區(qū)?；?998年至2019年的社區(qū)面板數(shù)據(jù)，我們發(fā)現(xiàn)洪災(zāi)發(fā)生后，社區(qū)在參與率和行動強(qiáng)度兩個(gè)方面的風(fēng)險(xiǎn)緩解活動均顯著增加，且這種效應(yīng)可持續(xù)上升長達(dá)十年。具備更強(qiáng)治理能力的社區(qū)，尤其是城市地區(qū)，表現(xiàn)出更強(qiáng)的應(yīng)對反應(yīng)。研究結(jié)果表明，社區(qū)具備一定的適應(yīng)能力，但也暴露出以災(zāi)害驅(qū)動為主的響應(yīng)機(jī)制存在效率低下的問題，并引發(fā)了社區(qū)間響應(yīng)能力差異帶來的公平性擔(dān)憂。

原文鏈接：https://onlinelibrary.wiley.com/doi/10.1111/jori.70007

Advancing loss reserving: A hybrid neural network approach for individual claim development prediction

推進(jìn)損失準(zhǔn)備金估計(jì)：一種用于個(gè)別賠案發(fā)展預(yù)測的混合神經(jīng)網(wǎng)絡(luò)方法

作者

Judith C. Schneider（漢諾威萊布尼茨大學(xué)風(fēng)險(xiǎn)與保險(xiǎn)研究所），Brandon Schwab（漢諾威萊布尼茨大學(xué)風(fēng)險(xiǎn)與保險(xiǎn)研究所）

摘要：Accurately estimating loss reserves is critical for the financial health of insurance companies and informs numerous operational decisions. We propose a novel neural network architecture that enhances the prediction of incurred loss amounts for reported but not settled claims. Moreover, differing from other studies, we test our model on proprietary datasets from a large industrial insurer. In addition, we use bootstrapping to evaluate the stability and reliability of the predictions and Shapley additive explanation values to provide transparency and explainability by quantifying the contribution of each feature to the predictions. Our model shows superiority in estimating reserves more accurately than benchmark models, like the chain ladder approach. Particularly, our model exhibits nuanced performance at the branch level, reflecting its capacity to effectively integrate individual claim characteristics. Our findings emphasize the potential of using machine learning in enhancing actuarial forecasting and suggest a shift towards more granular data applications.

準(zhǔn)確估算損失準(zhǔn)備金對保險(xiǎn)公司的財(cái)務(wù)穩(wěn)健至關(guān)重要，并影響眾多運(yùn)營決策。本文提出了一種新型神經(jīng)網(wǎng)絡(luò)架構(gòu)，用于提升對已報(bào)告但尚未結(jié)案賠案的損失金額預(yù)測能力。與其他研究不同，我們在某大型工業(yè)保險(xiǎn)公司提供的專有數(shù)據(jù)集上對模型進(jìn)行了測試。此外，我們運(yùn)用自助法（bootstrapping）評估模型預(yù)測的穩(wěn)定性與可靠性，并通過Shapley加性解釋值（SHAP）量化各特征對預(yù)測結(jié)果的貢獻(xiàn)，以增強(qiáng)模型的可解釋性和透明度。研究結(jié)果顯示，該模型在損失準(zhǔn)備金估計(jì)方面顯著優(yōu)于傳統(tǒng)基準(zhǔn)模型，如鏈梯法（Chain Ladder）。特別是，我們的模型在分支機(jī)構(gòu)層面表現(xiàn)出細(xì)微的性能差異，反映了其有效整合單個(gè)索賠特征的能力。研究強(qiáng)調(diào)了機(jī)器學(xué)習(xí)在提升精算預(yù)測能力方面的潛力，并指出保險(xiǎn)公司應(yīng)逐步轉(zhuǎn)向更具粒度的數(shù)據(jù)應(yīng)用方式。

原文鏈接：https://onlinelibrary.wiley.com/doi/10.1111/jori.12501

Cyber risk assessment for capital management

用于資本管理的網(wǎng)絡(luò)風(fēng)險(xiǎn)評估框架研究

作者

Wing Fung Chong（赫瑞–瓦特大學(xué)數(shù)學(xué)科學(xué)麥克斯韋研究所；精算數(shù)學(xué)與統(tǒng)計(jì)系），Runhuan Feng（清華大學(xué)金融系），Hins Hu（康奈爾大學(xué)系統(tǒng)工程系），Linfeng Zhang（俄亥俄州立大學(xué)數(shù)學(xué)系）

摘要：This paper introduces a two-pillar cyber risk management framework to address the pervasive challenges in managing cyber risk. The first pillar, cyber risk assessment, combines insurance frequency-severity models with cybersecurity cascade models to capture the unique nature of cyber risk. The second pillar, cyber capital management, facilitates informed allocation of capital for a balanced cyber risk management strategy, including cybersecurity investments, insurance coverage, and reserves. A case study, based on historical cyber incident data and realistic assumptions, demonstrates the necessity of comprehensive cost–benefit analysis for budget-constrained companies with competing objectives in cyber risk management. In addition, sensitivity analysis highlights the dependence of the optimal strategy on factors such as the price of cybersecurity controls and their effectiveness. The framework's implementation across a diverse range of companies yields general insights on cyber risk management.

本文提出了一個(gè)“雙支柱”的網(wǎng)絡(luò)風(fēng)險(xiǎn)管理框架，以應(yīng)對當(dāng)前網(wǎng)絡(luò)風(fēng)險(xiǎn)管理中普遍存在的挑戰(zhàn)。第一支柱是網(wǎng)絡(luò)風(fēng)險(xiǎn)評估，融合了保險(xiǎn)中的頻率–嚴(yán)重性模型與網(wǎng)絡(luò)安全事件級聯(lián)模型，從而更好地刻畫網(wǎng)絡(luò)風(fēng)險(xiǎn)的獨(dú)特特性。第二支柱是網(wǎng)絡(luò)資本管理，旨在輔助企業(yè)在網(wǎng)絡(luò)安全投入、保險(xiǎn)覆蓋與資本儲備之間實(shí)現(xiàn)科學(xué)的資金配置，推動更為均衡的網(wǎng)絡(luò)風(fēng)險(xiǎn)管理策略?；跉v史網(wǎng)絡(luò)事件數(shù)據(jù)與現(xiàn)實(shí)假設(shè)的案例研究表明，對于預(yù)算有限且面臨多重管理目標(biāo)的公司而言，開展全面的成本效益分析至關(guān)重要。此外，敏感性分析指出，最優(yōu)管理策略對網(wǎng)絡(luò)安全控制措施的價(jià)格與效果高度敏感。該框架在不同類型企業(yè)中的應(yīng)用，揭示了網(wǎng)絡(luò)風(fēng)險(xiǎn)管理的一般性規(guī)律與策略啟示。

原文鏈接：https://onlinelibrary.wiley.com/doi/10.1111/jori.12504

Banding together to lower the cost of health care? An empirical study of the Peak Health Alliance in Colorado

通過聯(lián)合采購降低醫(yī)療成本？——對科羅拉多州Peak健康聯(lián)盟的實(shí)證研究

作者

Mark K. Meiselbach（約翰斯·霍普金斯大學(xué)彭博公共衛(wèi)生學(xué)院），Matthew D. Eisenberg（約翰斯·霍普金斯大學(xué)彭博公共衛(wèi)生學(xué)院；凱里商學(xué)院）

摘要：This paper evaluates the effectiveness of Peak Health Alliance, a public–private initiative in Colorado aimed at lowering health care costs for employers and enrollees by increased bargaining power through the formation of a health care purchasing alliance. Using 2017–2021 plan data provided by the Colorado Department of Regulatory Affairs: Division of Insurance, we use difference-in-differences, event study, and synthetic control methods to compare changes in premiums in counties where Peak operated to other counties in Colorado before and after its implementation. The results suggest that Peak was associated with an increase in insurer market power and led to a 13%–17% decrease in average premiums, depending on the empirical specification. We further assess mechanisms underlying these effects and find evidence that lower prices were the most likely mechanism behind the estimated effect of Peak. Study results provide insights about the future of such public–private partnerships and their potential effectiveness.

本文評估了“Peak健康聯(lián)盟”（Peak Health Alliance）在控制醫(yī)療支出方面的政策效果。該聯(lián)盟是科羅拉多州的一項(xiàng)公私合作項(xiàng)目，旨在通過組建醫(yī)療服務(wù)采購聯(lián)盟提升議價(jià)能力，從而為雇主及參保人降低醫(yī)療成本?；诳屏_拉多州監(jiān)管事務(wù)部保險(xiǎn)司提供的2017年至2021年保險(xiǎn)計(jì)劃數(shù)據(jù)，本文采用雙重差分法（Difference-in-Differences）、事件研究（Event Study）以及合成控制法（Synthetic Control）等計(jì)量方法，對Peak聯(lián)盟實(shí)施前后、其所在縣與其他縣的保費(fèi)變動進(jìn)行對比分析。研究結(jié)果顯示，Peak聯(lián)盟的設(shè)立提升了保險(xiǎn)公司的市場議價(jià)能力，并在不同實(shí)證設(shè)定下促使平均保費(fèi)下降了13%至17%。進(jìn)一步機(jī)制分析表明，保費(fèi)下降主要?dú)w因于醫(yī)療服務(wù)價(jià)格的降低，而非覆蓋范圍或風(fēng)險(xiǎn)結(jié)構(gòu)的變化。本研究為探索公私合作機(jī)制在降低醫(yī)療費(fèi)用方面的作用提供了經(jīng)驗(yàn)證據(jù)，并對相關(guān)政策的推廣與優(yōu)化具有現(xiàn)實(shí)啟示意義。

原文鏈接：https://onlinelibrary.wiley.com/doi/10.1111/jori.12507

Textual analysis of insurance claims with large language models

利用大型語言模型對保險(xiǎn)理賠文本的分析研究

作者

Dongchen Li（華東師范大學(xué)統(tǒng)計(jì)學(xué)院，教育部統(tǒng)計(jì)與數(shù)據(jù)科學(xué)前沿理論與應(yīng)用重點(diǎn)實(shí)驗(yàn)室），Zhuo Jin（澳大利亞麥考瑞大學(xué)，精算與商業(yè)分析系），Linyi Qian（華東師范大學(xué)統(tǒng)計(jì)學(xué)院，教育部統(tǒng)計(jì)與數(shù)據(jù)科學(xué)前沿理論與應(yīng)用重點(diǎn)實(shí)驗(yàn)室；華東師范大學(xué)中國普惠養(yǎng)老金融研究中心），Hailiang Yang（西交利物浦大學(xué)數(shù)學(xué)與物理學(xué)院，金融與精算數(shù)學(xué)系）

摘要：This study proposes a comprehensive and general framework for examining discrepancies in textual content using large language models (LLMs), broadening application scenarios in the insurtech and risk management fields, and conducting empirical research based on actual needs and real-world data. Our framework integrates OpenAI's interface to embed texts and project them into external categories while utilizing distance metrics to evaluate discrepancies. To identify significant disparities, we design prompts to analyze three types of relationships: identical information, logical relationships and potential relationships. Our empirical analysis shows that 22.1% of samples exhibit substantial semantic discrepancies, and 38.1% of the samples with significant differences contain at least one of the identified relationships. The average processing time for each sample does not exceed 4 s, and all processes can be adjusted based on actual needs. Backtesting results and comparisons with traditional NLP methods further demonstrate that our proposed method is both effective and robust.

本研究提出了一個(gè)基于大型語言模型（LLMs）的通用性文本差異分析框架，拓展了其在保險(xiǎn)科技與風(fēng)險(xiǎn)管理領(lǐng)域中的應(yīng)用場景，并結(jié)合實(shí)際需求與真實(shí)數(shù)據(jù)開展了實(shí)證研究。該框架通過集成OpenAI接口對文本進(jìn)行嵌入表示，并投射至外部分類體系中，同時(shí)利用距離度量方法評估文本差異。為識別關(guān)鍵性語義差異，本文設(shè)計(jì)提示詞以分析三類文本關(guān)系：信息一致性、邏輯關(guān)系與潛在關(guān)聯(lián)關(guān)系。實(shí)證結(jié)果表明，22.1%的樣本存在明顯語義差異，其中38.1%的顯著差異樣本包含至少一種識別出的文本關(guān)系類型。每個(gè)樣本的平均處理時(shí)長不超過4秒，整體流程可根據(jù)實(shí)際需求靈活調(diào)整?；販y分析與與傳統(tǒng)自然語言處理（NLP）方法的對比結(jié)果進(jìn)一步驗(yàn)證了所提出方法的有效性與穩(wěn)健性。

原文鏈接：https://onlinelibrary.wiley.com/doi/10.1111/jori.70004

Regulating risk culture in the insurance industry using machine learning

利用機(jī)器學(xué)習(xí)規(guī)范保險(xiǎn)行業(yè)的風(fēng)險(xiǎn)文化

作者

Aparna Gupta（倫斯勒理工學(xué)院，Lally管理學(xué)院），Abena Owusu（蒙特克萊爾州立大學(xué)，F(xiàn)eliciano商學(xué)院）

摘要：We investigate whether the price paid for insurance explains dishonesty in reporting an insurance claim. In our laboratory experiment, participants earn money in a real-effort task but risk losing some of this income through one of four randomly assigned, privately observed loss amounts. Before observing their loss, participants indicate their reservation price for insurance that pays an indemnity equal to their stated loss. Participants are insured if their randomly assigned premium is less than their stated reservation price. This mechanism provides data on each participant's consumer surplus from insurance. After receiving their cash earnings minus their assigned loss in private, participants report their loss. We find that the insured report modestly but statistically insignificant larger losses than the uninsured. Among the insured, we find no clear evidence that their reporting of excess losses increases in the randomly assigned price of insurance or decreases in the consumer surplus from insurance.

本研究運(yùn)用文本分析和機(jī)器學(xué)習(xí)方法，探討保險(xiǎn)行業(yè)中風(fēng)險(xiǎn)文化與監(jiān)管之間的關(guān)系。通過分析公司10-K年度報(bào)告，我們將企業(yè)劃分為不同的風(fēng)險(xiǎn)文化集群，并發(fā)現(xiàn)保險(xiǎn)公司的風(fēng)險(xiǎn)文化受到不確定風(fēng)險(xiǎn)戰(zhàn)略、在風(fēng)險(xiǎn)定義、執(zhí)行與報(bào)告方面的限制、訴訟決策以及風(fēng)險(xiǎn)管理實(shí)踐等因素的顯著影響。時(shí)間序列預(yù)測分析顯示，相較于風(fēng)險(xiǎn)文化正在改善的公司，維持較差風(fēng)險(xiǎn)文化趨勢的大型保險(xiǎn)公司更難實(shí)現(xiàn)逆轉(zhuǎn)。此外，Dodd–Frank法案實(shí)施后，保險(xiǎn)公司的風(fēng)險(xiǎn)文化有所提升。我們的研究結(jié)果強(qiáng)調(diào)了監(jiān)管措施在監(jiān)督和管理保險(xiǎn)公司風(fēng)險(xiǎn)實(shí)踐方面可能帶來的積極作用。

原文鏈接：https://onlinelibrary.wiley.com/doi/10.1111/jori.70009

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