[ Tarification d’un contrat de Réassurance: Cas d’un portefeuille d’assurance Habitation ]
Youssef Diouri1 and Zahi Jamal2
1 Laboratoire de Modélisation Mathématique et de Calcul Economique, Faculté d’Economie et de Gestion, Université Hassan 1er, Settat, Morocco
2 Faculté des sciences juridiques, économique et sociales, Université Hassan 1er, Settat, Maroc
Original language: French
Copyright © 2024 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Insurance, as a fundamental pillar of the economy, offers essential financial protection against a multitude of unforeseen events. This research focuses on home insurance, a sector where guaranteed amounts significantly increase insurers’ exposure to risk. Insurers commit to covering financial losses in exchange for premiums, but accurately estimating the likely indemnity burden remains a challenge due to the random nature of events such as fires or property damage. Misjudging these risks can lead to severe financial consequences and compromise insurers’ solvency. To manage these risks and ensure their solvency, insurers turn to reinsurance, which allows them to cap their commitments, stabilize their financial results, and comply with regulatory requirements. The primary objective of this study is to develop a pricing model for reinsurance contracts, enabling insurers to better negotiate with their reinsurers. Before concluding a reinsurance agreement, insurers must conduct their own pricing to assess the costs and benefits of potential treaties. This involves modeling the annual number of claims and associated costs using various statistical laws and applying Monte Carlo simulation, a robust method for solving complex numerical problems. This study aims to provide insurers with a comprehensive tool to evaluate reinsurance contracts, ensure balanced financial results, and thus enhance their ability to protect policyholders against unforeseen losses.
Author Keywords: Monte Carlo simulation, risk, management, statistical laws, liability,.
Youssef Diouri1 and Zahi Jamal2
1 Laboratoire de Modélisation Mathématique et de Calcul Economique, Faculté d’Economie et de Gestion, Université Hassan 1er, Settat, Morocco
2 Faculté des sciences juridiques, économique et sociales, Université Hassan 1er, Settat, Maroc
Original language: French
Copyright © 2024 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Insurance, as a fundamental pillar of the economy, offers essential financial protection against a multitude of unforeseen events. This research focuses on home insurance, a sector where guaranteed amounts significantly increase insurers’ exposure to risk. Insurers commit to covering financial losses in exchange for premiums, but accurately estimating the likely indemnity burden remains a challenge due to the random nature of events such as fires or property damage. Misjudging these risks can lead to severe financial consequences and compromise insurers’ solvency. To manage these risks and ensure their solvency, insurers turn to reinsurance, which allows them to cap their commitments, stabilize their financial results, and comply with regulatory requirements. The primary objective of this study is to develop a pricing model for reinsurance contracts, enabling insurers to better negotiate with their reinsurers. Before concluding a reinsurance agreement, insurers must conduct their own pricing to assess the costs and benefits of potential treaties. This involves modeling the annual number of claims and associated costs using various statistical laws and applying Monte Carlo simulation, a robust method for solving complex numerical problems. This study aims to provide insurers with a comprehensive tool to evaluate reinsurance contracts, ensure balanced financial results, and thus enhance their ability to protect policyholders against unforeseen losses.
Author Keywords: Monte Carlo simulation, risk, management, statistical laws, liability,.
Abstract: (french)
L’assurance, en tant que pilier fondamental de l’économie, offre une protection financière essentielle contre une multitude d’événements imprévus. Cette recherche se concentre sur l’assurance habitation, un secteur où les montants garantis augmentent considérablement l’exposition au risque des assureurs. Les assureurs s’engagent à couvrir les pertes financières en échange de primes, mais estimer précisément le fardeau d’indemnisation probable reste un défi en raison de la nature aléatoire des événements tels que les incendies ou les dommages matériels. Une mauvaise évaluation de ces risques peut entraîner des conséquences financières graves et compromettre la solvabilité des assureurs. Pour gérer ces risques et garantir leur solvabilité, les assureurs recourent à la réassurance, qui leur permet de plafonner leurs engagements, de stabiliser leurs résultats financiers et de se conformer aux exigences réglementaires. L’objectif principal de cette étude est de développer un modèle de tarification pour les contrats de réassurance, permettant aux assureurs de mieux négocier avec leurs réassureurs. Avant de conclure un accord de réassurance, les assureurs doivent effectuer leur propre tarification pour évaluer les coûts et les bénéfices des traités potentiels. Cela implique de modéliser le nombre annuel de sinistres et les coûts associés à l’aide de diverses lois statistiques et d’appliquer la simulation de Monte Carlo, une méthode robuste pour résoudre des problèmes numériques complexes. Cette étude vise à fournir aux assureurs un outil complet pour évaluer les contrats de réassurance, assurer des résultats financiers équilibrés, et ainsi renforcer leur capacité à protéger les assurés contre les pertes imprévues.
Author Keywords: simulation Monte Carlo, management, risque, lois statistiques, engagement.
