Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms

This paper is focused on solving different hard optimization problems that arise in the field of insurance and, more specifically, in reinsurance problems. In this area, the complexity of the models and assumptions considered in the definition of the reinsurance rules and conditions produces hard black-box optimization problems -problems in which the objective function does not have an algebraic expression, but it is the output of a system - usually a computer program, which must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in this kind of mathematical problem, so new computational paradigms must be applied to solve these problems. In this paper, we show the performance of two evolutionary and swarm intelligence techniques -evolutionary programming and particle swarm optimization-. We provide an analysis in three black-box optimization problems in reinsurance, where the proposed approaches exhibit an excellent behavior, finding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods

Multi-Objective Stochastic Optimization Programs for a non-Life Insurance Company under Solvency Constraints

In the paper, we introduce a multi-objective scenario-based optimization approach for chance-constrained portfolio selection problems. More specifically, a modified version of the normal constraint method is implemented with a global solver in order to generate a dotted approximation of the Pareto frontier for bi- and tri-objective programming problems. Numerical experiments are carried out on a set of portfolios to be optimized for an EU-based non-life insurance company. Both performance indicators and risk measures are managed as objectives. Results show that this procedure is effective and readily applicable to achieve suitable risk-reward tradeoff analysis

Investment strategies of a non-life insurance company under Solvency II

Mestrado em Ciências ActuariaisNeste trabalho é feita a otimização da carteira de uma empresa de seguros não vida, que utiliza a fórmula standard definida no regime de Solvência II para calcular os requisitos de capital, com o objetivo de encontrar a alocação dos ativos financeiros que minimizam o risco de mercado e, simultaneamente, maximizam o retorno da carteira. A solução é obtida a partir de um processo de otimização multi-objetivo. Para analisar o desempenho da carteira e o risco do capital investido, calculamos a rentabilidade ajustada ao risco (RoRAC), que é o rácio entre o retorno esperado e o valor de Solvência II relativo ao risco de mercado. Os resultados mostram que é possível definir uma estratégia de investimento no regime de Solvência II que permita atingir os objetivos em retorno e requisitos de capital.On this study we develop a portfolio investment optimization process for a non-life insurance company, where capital requirement is calculated using the standard formula defined by Solvency II. The optimization aims to find the minimum solvency capital requirements for market risk and, simultaneously, maximize portfolio returns. The optimal investment strategy set is obtained using a multi-objective optimization process. To analyse the performance of the portfolio and the capital at risk, we compute the return on risk adjusted capital (RoRAC), that is the expected profit over the Solvency II market capital charge. Results show that is possible to define a set of investment strategies under Solvency II regime that accomplish the objectives on return and capital requirements.info:eu-repo/semantics/publishedVersio

An Analysis of black-box optimization problems in reinsurance : evolutionary-based approaches

Black-box optimization problems (BBOP) are de ned as those optimization problems in which the objective function does not have an algebraic expression, but it is the output of a system (usually a computer program). This paper is focussed on BBOPs that arise in the eld of insurance, and more speci cally in reinsurance problems. In this area, the complexity of the models and assumptions considered to de ne the reinsurance rules and conditions produces hard black-box optimization problems, that must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in BBOP, so new computational paradigms must be applied to solve these problems. In this paper we show the performance of two evolutionary-based techniques (Evolutionary Programming and Particle Swarm Optimization). We provide an analysis in three BBOP in reinsurance, where the evolutionary-based approaches exhibit an excellent behaviour, nding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods

Efficient reinsurance strategies considering counterparty default risk

Insurance companies pursue the objective of increasing their technical profit, but in doing so, they expose themselves to more risks, increasing the variability of their result. In order to balance the potential profitability deriving from the underwriting activity with the related risks, insurers typically resort to reinsurance treaties. In this context arises the problem of finding the optimal treaty which jointly satisfies multiple objectives, typically represented by risk and return metrics. The classical approaches consider only the characteristics of the treaty, neglecting the ones of the reinsurance provider. However, this approach could lead to sub-optimal choices, since it does not consider counterparty default risk. The purpose of this thesis is threefold. Firstly, we extend classical formulas of technical profit of an insurance company to a partial internal model of Solvency II, including the potential default of the reinsurance counterparty. Secondly, we develop a stochastic simulation approach that includes counterparty default risk and potentially other features, for estimating the efficient frontier of reinsurance strategies for a non-life insurance company. Finally, we propose the application of a neural network model for finding the efficient frontier in a multi-objective optimization problem, requiring limited observations and preserving the possibility of deriving the strategies which generate the Pareto front. Numerical applications are performed assuming a multi-line non-life insurer with parameters from the Italian market. The results show the importance of the rating of reinsurers, i.e. counterparty default risk, for the assessment of the optimal reinsurance strategies. Moreover, we show how this risk could become an opportunity in case the reinsurer with high risk offers a discounted price that more than compensate the potential default effect. Finally, the neural network model offers another perspective for determining optimal reinsurance strategies, which can be especially useful in case of high number of potential combinations defining each strategy

Intelligente Methoden im Integrierten Risikomanagement

In dieser Arbeit wird ein multikriterielles Modell zur Integration des Risikomanagements auf Basis von Kredit-, Markt- und operationellem Risiko konzipiert. Der Ansatz approximiert die Lösungen des Problems mittels multikriterieller evolutionärer Algorithmen. Seine Anwendung wird für eine Beispielbank aufgezeigt mit besonderem Fokus auf die ansprechende Visualisierung der Ergebnisse

Stochastic Optimization Models of Actuarial Mathematics

The paper overviews stochastic optimization models of actuarial mathematics and methods for their solution from the point of view of the methodology of multicriteria stochastic programming and optimal control. The evolution of the capital of an insurance company is considered in discrete time. The main random parameters of the models are insurance payouts, i.e., the ratios of paid insurance claims to the corresponding premiums per unit time. Optimization variables are the structure of the insurance portfolio (gross premium structure) and amount of dividends. As efficiency criteria, indicators of the profitability of the insurance business are used, and, as risk indicators the ruin probability and the recourse capital necessary to prevent the ruin are taken. The goal of the optimization is to find Pareto-optimal solutions. Methods for finding these solutions are proposed

Key performance indicators for sustainable manufacturing evaluation in automotive companies

The automotive industry is regarded as one of the most important and strategic industry in manufacturing sector. It is the largest manufacturing enterprise in the world and one of the most resource intensive industries of all major industrial system. However, its products and processes are a significant source of environmental impact. Thus, there is a need to evaluate sustainable manufacturing performance in this industry. This paper proposes a set of initial key performance indicators (KPIs) for sustainable manufacturing evaluation believed to be appropriate to automotive companies, consisting of three factors divided into nine dimensions and a total of 41 sub-dimensions. A survey will be conducted to confirm the adaptability of the initial KPIs with the industry practices. Future research will focus on developing an evaluation tool to assess sustainable manufacturing performance in automotive companies