Evaluation of German Life Insurers Based on Published Solvency and Financial Condition Reports An Alternative Model

Abstract

German life insurance companies were selected for a complete survey. Quantitative data relevant to the analysis was extracted from the individual Solvency and Financial Condition Reports reports and transferred to a systematic overview. To make the collected observations usable, they were transformed into various growth, profitability, and security ratios cited in the literature, thus enhancing comparability. The data transformed into ratios were presented in histograms with ten intervals each, and a score between one and ten was assigned depending on the ratio value. In addition, four qualitative risk categories were initially identified, based on which the model would be operationalized. These were underwriting risk, market risk, credit risk and operational risk. Artificial intelligence was used to design seven questions on the requirements of Article 295 of Delegated Regulation 2015/35. Specifically, there were questions on the methodology of risk assessment, the description of specific measures, the comprehensibility of the measures, information on risk concentrations and the impact on the risk profile of the company as a whole, and risk mitigation measures and their effectiveness. Points were awarded from one to four, depending on the quality of the text used to answer the questions, based on the Appelfeller and Feldmann maturity model. This was followed by a weighting with individual factors to reflect the importance of each metric. While consumers can, for example, simply use the indexed total score for further consideration, the model equation can also be filled with fictitious values. This makes it possible to predict the risk class of a fictitious life insurer in the German market in 2022.The analyses show that it is in principle possible to extract both quantitative and qualitative assessment elements from the respective solvency reports, transform them into meaningful ratios, and finally combine them in a weighted model to generate a decision-making basis for the model user. It is precisely the dovetailing of the two strands of quantitative and qualitative analysis that makes it possible to make meaningful use of information that was previously not taken into account.