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.