Accurate forecasting of insurance claims is of the utmost importance for insurance activity as the evolution of claims determines cash outflows and the pricing, and thus the profitability, of the underlying insurance coverage. These are used as inputs when the insurance company drafts its business plan and determines its risk appetite, and the respective solvency capital required (by the regulators) to absorb the assumed risks. The conventional claim forecasting methods attempt to fit (each of) the claims frequency and severity with a known probability distribution function and use it to project future claims. This study offers a fresh approach in insurance claims forecasting. First, we introduce two novel sets of variables, i.e., weather conditions and car sales, and second, we employ a battery of Machine Learning (ML) algorithms (Support Vector Machines—SVM, Decision Trees, Random Forests, and Boosting) to forecast the average (mean) insurance claim per insured car per quarter. Finally, we identify the variables that are the most influential in forecasting insurance claims. Our dataset comes from the motor portfolio of an insurance company operating in Athens, Greece and spans a period from 2008 to 2020. We found evidence that the three most informative variables pertain to the new car sales with a 3-quarter and 1-quarter lag and the minimum temperature of Elefsina (one of the weather stations in Athens) with a 3-quarter lag. Among the models tested, Random Forest with limited depth and XGboost run on the 15 most informative variables, and these exhibited the best performance. These findings can be useful in the hands of insurers as they can consider the weather conditions and the new car sales among the parameters that are considered to perform claims forecasting.

This paper studies the impact of internal and external factors on the profitability and financial strength of insurance groups across the European Union, focusing on the effect of the European sovereign debt crisis and the Solvency II Directive, which has not been addressed by the literature. The findings reveal that the efficiency of accounts receivable management and the state of the economy have a significant positive impact, whereas the underwriting risk and size have a significant negative impact on profitability and financial strength. Furthermore, long-term interest rates exert a significant positive influence, whereas the purchase of reinsurance, size of the domestic market, market structure, and inflation exert a significant negative influence on profitability. The European sovereign debt crisis has a significant negative impact on profitability and a positive but insignificant impact on financial strength. Solvency II has no statistically significant impact either on profitability or on financial strength. This paper contributes to the literature through the identification of the impact of the European sovereign debt crisis and Solvency II, which has not been depicted so far, as well as the impact of a series of internal and external factors on profitability and, for the first time, on financial strength.

Successive crises in the early twenty-first century prompted regulators around the world to ask financial institutions to implement a series of regulations. These measures aimed to increase transparency, improve consumer and investor protection, restructure financial capital, stabilize insurance and pension markets, and improve solvency. The Solvency II framework introduced in the European Union applied these principles to insurance companies. This study attempts to predict the solvency of an insurer within a set of European insurers. The dataset consists of 29 insurance groups that operate across the European Union with a country of origin within the European Union for the period 2016 to 2020. The variables were constructed from annual financial statements retrieved from (Thomson Reuters) DataStream. The solvency capital requirement ratios were obtained manually from the solvency financial condition reports of each group. Regularized linear regression applying a ℓ₁/ least-absolute-shrinkage-and-selection-operator penalty showed that the reinvestment rate, cash and equivalents, long term investment, and losses-benefits-and-adjustments expenses have the greatest predictive impact on the solvency of insurers. The contribution of this paper lies in the identification of determinants that allow insurance companies to maintain strong solvency capital requirement ratios so that they can maintain internal operations with minimal interruption.

This study attempts to structure methodologically the health insurance underwriting process by applying Multi-criteria Decision-making (MCDM) analysis in health insurance underwriting. This is done by assigning a score to each health insurance applicant which can be used to determine whether he or she is accepted, rejected or accepted with special terms and conditions (such as exclusions, additional waiting periods and/ or surcharge). The introduction of MCDM approaches in health insurance underwriting enables the quantification of the selection criteria, the increased standardization and automation of the process and its alignment through quantitative indicators with the risk tolerance/ risk appetite of the insurer, and there lie the novelties of this research. The proposed methodology can be readily implemented by insurers with added value in the underwriting, risk management and distribution (sales & marketing) functions, as well as in the profitability of the company or the level of premium paid by the insured.