人壽保險業涉及長期契約責任與消費者權益保障，屬高度監理產業，惟現行生成式人工智慧研究多偏概念性分析，對其保險業應用情境之風險辨識與管理仍缺乏具體探討。有鑑於此，本研究旨在探討人壽保險公司應用生成式人工智慧之風險辨識與治理機制，並從其導入後的生命週期與角色分工角度建構其風險治理架構。本研究採質性研究方法，透過國際監理制度比較與我國人壽保險業半結構式訪談分析，以補充現有文獻於金融產業應用層面之不足。
研究發現，生成式人工智慧風險已由傳統資訊技術範疇，擴展至資料治理、模型決策品質及責任分工等面向，應用過程中會隨模型建置、部署與營運持續變化，且有跨階段交錯之特性，顯示傳統以單一控制節點或線性流程為基礎之風險管理模式存在侷限。因此，本研究提出以生成式人工智慧生命週期為核心，涵蓋設計、部署、營運及退場四階段，並結合保險公司、模型供應者及客戶三方角色之治理架構。此觀點可作為人壽保險業應用生成式人工智慧時之風險管理基礎，並提供監理單位研擬相關規範及制度設計之參考。

The life insurance industry involves long-term contractual obligations and consumer protection and is therefore highly regulated. However, existing studies on generative artificial intelligence are mostly conceptual and provide limited discussion of the risks arising from its use in insurance. This study examines risk identification and governance mechanisms for generative artificial intelligence in life insurance companies. It adopts a qualitative approach by comparing international regulatory frameworks and analyzing semi-structured interviews conducted in Taiwan’s life insurance sector.
The findings show that generative artificial intelligence risks extend beyond traditional information technology risks to data governance, model decision quality, and the allocation of responsibilities. These risks may change throughout model development, deployment, operation, and decommissioning and may overlap across different life-cycle stages. This limits the effectiveness of traditional risk management approaches based on a single control point or linear process. Accordingly, this study proposes a life-cycle-based governance framework covering four stages: design, deployment, operation, and decommissioning. It also considers the roles of insurance companies, model providers, and customers. The framework may support risk management in the life insurance industry and provide a reference for supervisory authorities when developing related regulations and institutional arrangements.

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