Purpose This paper aims to present an actuarial model of takaful to cover death and permanent total deficit of contributors benefiting from Mourabaha financing for real estate, taking into account modern takaful systems around the world. The main goal of this paper is to design an optimization program that helps to obtain a marketable and profitable takaful product by using the retakaful as a safety and technical support tool. Design/methodology/approach The study adopts an actuarial approach for determining takaful contributions and optimal choice of an adequate form of retakaful to maximize the technical surplus of a takaful company. A resolution method based on genetic algorithms is also developed to solve the optimization program. Findings This actuarial model allows to provide high added-value support to takaful and Islamic finance and also reassures clients of purchasing real estate through the Mourabaha product. Research limitations/implications It should be noted that the choice of an effective Islamic borrower insurance product (takaful borrower) obviously depends on the strategy adopted by the takaful operator (type of takaful, pricing, experience and statistics observed, etc.), the form of retakaful, the behavior of the participants and the nature of the portfolio. Moreover, the program developed in this paper is suitably affordable and is closely linked to the hypotheses. Originality/value The strong point of the present program relies on the case where the reinsurer establishes itself as a partner of choice and to enrich the takaful fund. In addition, the actuarial tools used in agreement with genetic algorithms have made it possible to optimize with efficiency and ease of calculation.

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The minimization of the probability of ruin is a crucial criterion for determining the effect of the form of reinsurance on the wealth of the cedant and is a very important factor in choosing optimal reinsurance. However, this optimization criterion alone does not generally lead to a rational decision for an optimal choice of reinsurance. This criterion acts only on the risk (minimizing it via the probability of ruin), but it does not affect the technical benefit. That is to say, the insurer should not choose the optimal reinsurance treaty if it is not beneficial. We propose a new reinsurance optimization strategy that maximizes the technical benefit of an insurance company while maintaining a minimal level for the probability of ruin. The objective is to optimize reinsurance with efficiency and ease of computation, using Genetic algorithms.

In this paper, we present a new method for dynamic optimization to achieve maximum technical benefit to the insurer using genetic algorithms. The objective of this work is to select, from a database containing forms of reinsurance, pricing models and credit ratings parameters (risk measurement methods, such as Value at Risk (VaR), Conditional Value at Risk (CVaR) or ruin probability) a form of reinsurance, a way of pricing and a solvency parameter to maximize technical benefits of the insurance company.

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