Establishing a standard formula (SF) for the regulation of European insurance companies is a Herculean task. It has to acknowledge very different business models and national peculiarities. In addition, regulatory authorities—as a stakeholder on their own—have a number of supervisory objectives the SF should incentivize. With the intervention of the SF in economic activities, the principle of equal treatment must be maintained. The large circle of users makes its procedural simplicity indispensable to ensure that it is applied and implemented in a proportionate manner. Above all, the SF should be risk-sensitive. Compared to Solvency I, the SF of Solvency II is considered a significant improvement, as many of the aforementioned desiderata have been much better realized. The following analysis and survey of model-theoretical aspects of the SF shows that these improvements could be achieved above all with regard to epistemic uncertainties. The stochastic model underneath the SF is still subject to considerable uncertainties; so that the probability functional of the SF is exposed to significant model risk. As part of the Own Risk and Solvency Assessment (ORSA), insurance companies must prove the adequacy of the SF for their company. The vague prior knowledge represented by the stochastic component of the SF is not sufficient for an SF intrinsic validation of the aleatoric component.

为欧洲保险公司的监管建立标准公式 (SF) 是一项艰巨的任务。它必须承认非常不同的商业模式和国家特性。此外，监管机构——作为他们自己的利益相关者——有许多 SF 应该激励的监管目标。在SF介入经济活动的情况下，必须坚持平等对待的原则。庞大的用户圈使得其程序的简单性必不可少，以确保以相称的方式应用和实施。最重要的是，SF 应该具有风险敏感性。与偿付能力 I 相比，偿付能力 II 的 SF 被认为是一项重大改进，因为许多上述需求已得到更好的实现。以下对 SF 的模型理论方面的分析和调查表明，这些改进首先可以在认知不确定性方面实现。SF 下的随机模型仍然存在相当大的不确定性；从而使 SF 的概率泛函暴露于显着的模型风险中。作为自身风险和偿付能力评估 (ORSA) 的一部分，保险公司必须证明 SF 对其公司的充分性。SF 的随机分量表示的模糊先验知识不足以对任意分量进行 SF 内在验证。从而使 SF 的概率泛函暴露于显着的模型风险中。作为自身风险和偿付能力评估 (ORSA) 的一部分，保险公司必须证明 SF 对其公司的充分性。SF 的随机分量表示的模糊先验知识不足以对任意分量进行 SF 内在验证。从而使 SF 的概率泛函暴露于显着的模型风险中。作为自身风险和偿付能力评估 (ORSA) 的一部分，保险公司必须证明 SF 对其公司的充分性。SF 的随机分量表示的模糊先验知识不足以对任意分量进行 SF 内在验证。