The standard formula of Solvency II: a critical discussion

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.

INSOLVENCY OF INSURANCE COMPANIES IN UKRAINE: DETECTION AND PROGNOSTICATION

The paper demonstrates the results of research on the problems of insurance company insolvency. The outcomes describe the basic principles of detection and using the early warning system in Ukraine. The paper shows the necessity to reorganize the basic principles of the detection of local insolvent insurers. The study also determines the groups of persons who are directly or indirectly interested in the insurer's solvency assessment. This work presents the system of insurance companies' insolvency indicators, which help to identify insolvency at the early stages. The paper distinguishes precautionary, delayed, internal, and external insolvency indicators of insurers. The study divides the values of insurer's insolvency indicators into "yellow" and "red" zones to increase the flexibility of using various instruments for influencing by the regulator depending on the level of danger of the insurance company. This work argues for taking timely measures to the threat of insolvency of the insurance companies by the insurance supervisor.Key words: insolvency, instability of the insurers, solvency, early warning system, insurance market.

Hoe passen verzekeraars scenario’s toe in het kader van de ORSA?

Per 1 januari 2016 moeten verzekeraars uit hoofde van Solvency II een Own Risk & Solvency Assessment (ORSA) maken waarin zij met scenario’s en stresstesten de belangrijkste risico’s analyseren. Dit artikel beschrijft een onderzoek naar hoe verzekeraars scenario’s en stresstesten toepassen. Aan de hand van de literatuur zijn duidelijke processtappen en succescriteria voor het gebruik van scenario’s te onderscheiden. Dit proces wordt overwegend goed gevolgd door verzekeraars en het bestuur is goed betrokken op verschillende manieren. Dat geldt niet voor alle zogenoemde sleutelfuncties: met name de functies interne audit en compliance zijn veel minder betrokken dan bijvoorbeeld de risicomanagement-functie. De tijdshorizon die verzekeraars gebruiken voor hun langetermijnprognose is over het algemeen drie tot vijf jaar, maar er zijn ook verzekeraars die langere termijnen hanteren. Uit onze analyse blijkt dat verzekeraars veel meer leunen op stresstesten dan op scenario’s. Deze laatste worden in de praktijk nauwelijks toegepast in ORSA. Dit is een gemis voor de effectiviteit van ORSA. Wij constateren tot slot dat het feit dat toezichthouders meekijken een mogelijk systeemrisico creëert en dat is vanuit toezichtsperspectief juist onwenselijk.

Utility-Consistent Valuation Schemes for the Own Risk and Solvency Assessment of Life Insurance Companies

In this paper, we construct new valuation schemes for the liabilities and economic capital of insurance companies. Specifically, we first build a ‘SAHARA’ valuation framework based on Symmetric Asymptotic Hyperbolic Absolute Risk Aversion utility functions. Then, we construct a ‘SAHARA-CPT’ framework that incorporates the previous utility function as a value function and that is based on Cumulative Prospect Theory. The process used for assessing a life insurance company’s own funds consists in replacing the market-consistent parametrization with a utility-consistent parametrization that accounts for the risk aversion of the market and the long-term duration of the company’s commitments. Our illustrations show that this approach leads to a lower value of the Own Risk and Solvency Assessment and to a lower volatility of own funds. The framework that is based on cumulative prospect theory has the advantage over the expected utility theory framework that it considers a precautionary overweighting of extreme events, as a tradeoff for additional model complexity.

Enterprise risk management and Solvency II: the system of governance and the Own Risk and Solvency Assessment

Purpose The purpose of this paper is to analyze how enterprise risk management (ERM), the system of governance and the Own Risk and Solvency Assessment (ORSA) have been boosted with the entry of Solvency II. Design/methodology/approach For this analysis, the authors have undertaken a survey of chief risk officers (CROs) working in Spanish insurance companies. Findings The results show that Solvency II has definitely promoted ERM in the European insurance industry and improved the system of governance of the insurance companies, and that the perceived value of the ORSA for the companies is higher than the cost. It is clear that the quality of ERM implemented by companies is higher in those that face more complex risks and with greater interdependencies – that is, larger companies, foreign insurers and insurers with several lines of business – but is unaffected by the legal form of the entity (mutual/corporation). Originality/value This study conducts primary research with surveys of CROs and develops a measure of the quality of ERM implemented by insurance companies.

Analysis and diagnostics of business risks of the company: forecasting and control of probability of bankruptcy

The article discusses the author’s methodology and analytical tools for assessing business risks and forecasting the business continuity (probability of bankruptcy) of companies with the possibility of its implementation in a computer environment. The proposed methodology consists of four stages: assessment of financial results; solvency assessment; identification, calculation and assessment of financial risks of the company's business continuity; forecasting changes in the level of risk of business continuity using the developed calculator for calculating risk factors indicators. This technique is recommended for use in audit and consulting activities to assess the continuity of the audited companies, to justify the choice of borrowing companies, bidders and traders, as well as in internal audit systems, internal control, financial control of business processes.

Assessment of microcredit borrowers by the fuzzy maximin convolution method

The problem of timely repayment of loans at all times has been and continues to be actual for commercial banks. Overcoming this problem substantially depends on the quality of the solvency assessment of potential borrowers, which is carried out by experts on the basis of retrospective information. In the microcredit system, the assessment of the borrower's credit history is usually carried out by an expert who mainly relies on his heuristic knowledge and intuition, which usually extols subjective considerations that do not have sufficient grounds. In practice, the opinions of different analysts or those responsible for making credit decisions often differ, especially if controversial situations are considered that have many acceptable alternative solutions. As a result, in assessing the solvency of potential microloan borrowers, the subjective opinion of the expert and the incompetent or deliberate interpretation of the information resulting in the adoption of decisions that are detrimental to the microfinance organization are overweight. To increase the degree of objectivity, the paper discusses an approach to assessing the responsibility and solvency of microloan borrowers, based on the use of the fuzzy method of maximin convolution. This approach, given the poorly structured personal data of applicants, allows them to be flexibly and quickly assessed for the provision of microloans. The qualitative assessment criteria applied in this case are weighed based on expert opinions regarding the priority of each of them. An important advantage of the proposed model is that it is simple, convenient to use and able to adapt to the requirements of various micro-financial organizations.

Development of Borrowers’ Solvency Assessment Model: Logistic Regression Application

Borrowers’ solvency assessment models can not only increase company’s profit, but also potentially decrease the impact from the negative economic consequences of the crisis. However, there is no consensus on such models. Considering the flaws in the scientific literature, the main aim of this article was to develop the borrowers’ solvency assessment model, which can be applied in practice. The most appropriate method for developing such models was found to be logistic regression, and this research goal is to identify the best modelling approach to achieve the highest borrowers’ solvency predictability. By implementing the best-chosen model, a nonbank lending company could provide a 42.5% lower total borrowers risk of default than without implementing such a model. Depending on the risk policy of the non-bank lending company, three methodologies were developed based on different assumptions about the significance of type 1 error and type 2 error in the company to determine the exact cut-off value