Transparency of Insurance Regulation in Taiwan

Author(s):  
Chun-Yuan Chen
Keyword(s):  
2019 ◽  
Vol 19 (19) ◽  
pp. 105-186
Author(s):  
徐聿芊 徐聿芊 ◽  
陳俊元 陳俊元

金融產業由於涉及市場秩序與消費者保護,在我國向屬於高度監理之產業。保險業由於涉及保戶大眾之權益,更為監理之重心所在。然而,如果監理密度過高,但裁罰之發動或標準並不明確,可能導致業者難以預期評估,更可能導致業者動輒得咎,致生經營上之困難,對於市場並非有利。近來許多保險裁罰案件中,多涉及裁罰基礎不明確之爭議,可見本問題之嚴重性。因此,如何平衡監理與業者之經營自由,瞭解保險監理之特徵與趨勢,乃為亟待釐清之議題。本文乃從金融監理之有效性與透明性切入,並針對本文所蒐集之近年保險業裁罰案進行法律實證研究。本文將各種裁罰案歸納為七種主要類型,以檢討其是否妥適。之後再針對所有之案件進行統計分析,以明瞭保險業裁罰案件之特徵、趨勢、以及問題。最後,總結全文之法律與實證分析,對我國保險業裁罰提出建議。 Financial industry is highly regulated in Taiwan because of concerning market and consumer protection. Insurance enterprise is even more involving with insured and therefore at the core of regulation. However, intense regulation with vague standard and trigger of sanction, may undermine the expectation of insurer, cause the difficulty in business, and finally hurt the market. Thus, it is a critical issue to realize the characteristic and tendency of insurance regulation, and then strike a balance between regulation and business. This paper will start by regulatory effectiveness and transparency, and then conduct an empirical legal study on insurance sanction cases in recent years in Taiwan. This study categorizes administrative sanctions against insurance enterprise into seven groups and then examines the appropriateness. Afterwards, this paper will use empirical methods to clarify the characteristic, tendency, and issue of insurance sanction. Finally, we will conclude all legal and empirical analyses, and then provide suggestions for relevant administrative sanctions in Taiwan.


2021 ◽  
Author(s):  
Paul Embrechts ◽  
Alexander Schied ◽  
Ruodu Wang

We study issues of robustness in the context of Quantitative Risk Management and Optimization. We develop a general methodology for determining whether a given risk-measurement-related optimization problem is robust, which we call “robustness against optimization.” The new notion is studied for various classes of risk measures and expected utility and loss functions. Motivated by practical issues from financial regulation, special attention is given to the two most widely used risk measures in the industry, Value-at-Risk (VaR) and Expected Shortfall (ES). We establish that for a class of general optimization problems, VaR leads to nonrobust optimizers, whereas convex risk measures generally lead to robust ones. Our results offer extra insight on the ongoing discussion about the comparative advantages of VaR and ES in banking and insurance regulation. Our notion of robustness is conceptually different from the field of robust optimization, to which some interesting links are derived.


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