Corporate Demand for Insurance: Empirical Evidence from Germany

Several studies have tested theoretical arguments for corporate insurance purchasing behaviour with mixed evidence. Additionally, there are rarely studies focussing on European markets even though these belong to the largest markets for commercial insurance and reinsurance. This paper presents the first empirical analysis on the property insurance demand in Germany using a dataset of 2160 companies. The analysis suggests that the demand for insurance is influenced by the firm’s size and its ownership structure. Additionally, higher premium payments lead to a lower demand for insurance whereas the number of insurance contracts is positively associated with the insurance demand. No clear evidence was found for the loss-ration or the number of losses. This study also sheds light on the importance of the supply side and the design of the insurance contract for the demand of insurance

Prediction of claims in export credit finance: a comparison of four machine learning techniques

This study evaluates four machine learning (ML) techniques (Decision Trees (DT), Random Forests (RF), Neural Networks (NN) and Probabilistic Neural Networks (PNN)) on their ability to accurately predict export credit insurance claims. Additionally, we compare the performance of the ML techniques against a simple benchmark (BM) heuristic. The analysis is based on the utilisation of a dataset provided by the Berne Union, which is the most comprehensive collection of export credit insurance data and has been used in only two scientific studies so far. All ML techniques performed relatively well in predicting whether or not claims would be incurred, and, with limitations, in predicting the order of magnitude of the claims. No satisfactory results were achieved predicting actual claim ratios. RF performed significantly better than DT, NN and PNN against all prediction tasks, and most reliably carried their validation performance forward to test performance