随着人工智能和大数据技术在银行业的深度应用，商业银行模型风险管理面临前所未有的挑战与机遇。本文基于当前银行业模型风险管理现状，系统探讨科技赋能的实施路径与对策。研究表明，商业银行模型应用已从效率提升工具向核心决策系统演进，但在模型可解释性、数据治理及全生命周期管理等方面仍存在显著不足。通过构建模型全生命周期管理平台、强化数据治理、应用可解释AI技术以及建立人机协同风控体系，可有效提升模型风险的管控能力。本研究为商业银行实现风险可控基础上的业务创新与高质量发展提供了参考路径。

With the wider use of AI and big data in banking, model risk management faces new challenges and opportunities. This paper explores strategies for using technology to enhance model risk management. Findings show that bank models are now core to decision-making, but issues remain in explainability, data governance, and lifecycle management. Building a full lifecycle management platform, improving data governance, using explainable AI, and creating human-AI risk control systems can significantly enhance control over model risks. This study provides a framework for banks to innovate safely and achieve sustainable growth.