今天分享的是：AON怡安：2025年全球风险管理调研报告 报告共计：30页 2025年全球风险管理调研核心总结 《2025年全球风险管理调研》基于63个国家近3000名风险与商业领袖的反馈，揭示了当前风险交汇与颠覆加速的新时代特征。技术变革、贸易波动、气候变 化与人力资本演变四大趋势交织，重塑了全球风险格局，推动企业风险优先级发生重大转变。 当前全球十大风险中，网络攻击或数据泄露稳居首位，业务中断、经济放缓或复苏迟缓紧随其后。值得关注的是，地缘政治动荡自2019年以来排名跃升近30 位，首次跻身前十；气候变化与自然灾害排名也创下历史新高。未来十大风险预测显示，人工智能和气候变化将进一步凸显，分别跃升至第8位和第9位，网 络攻击或数据泄露仍将是核心关切。 这些风险并非孤立存在，而是呈现出强烈的系统性与关联性。技术风险超越网络与人工智能范畴，渗透企业各环节；地缘政治动荡影响供应链、监管环境及 企业资产负债表；气候变化作为"风险催化器"，加剧了供应链中断等多种风险；人力资本相关风险虽未进入前20名，却是企业财务损失的常见诱因，且与技 术变革带来的岗位重塑相互作用。 传统风险管理框架已难以应对复杂多变的风险环境，仅1 ...

Core Insights - The article discusses the introduction of a new explainable risk management framework by the "God's Pen Actuary Team" to address the challenges faced by traditional financial and internet platforms in a high-volatility, uncertain global economy [1][5] - The framework aims to enhance the relationship between platforms and users, as well as between models and decision-making, by improving the resilience and adaptability of risk governance [1][5] Summary by Sections Explainable Risk Management Framework - The new framework is built on four core modules: Risk Behavior Structure Graph Engine, Causal Chain Modeling Layer, Strategy Response Feedback System, and Structure Transparent Interface Component [1][2][3] - This framework shifts from a linear "input-judgment-block" process to a self-evolving structure of "cognition-modeling-response-relearning" [1][2] Risk Behavior Structure Graph Engine - This engine structures user behavior in the system, focusing on understanding the reasons behind actions rather than just the actions themselves [2] - It enhances early risk identification by incorporating deeper behavioral indicators such as "strategy stability" and "risk awareness maturity" [2] Causal Chain Modeling Layer - This layer introduces actuarial modeling logic and causal reasoning algorithms to establish a structural causal chain between user behavior and risk events [2] - It allows for the identification of key factors leading to risk evolution, enabling proactive intervention rather than reactive explanations [2] Strategy Response Feedback System - This system generates structural feedback based on user interactions with the model, optimizing future strategies based on user responses [3] - It shifts the focus from merely preventing errors to assisting in optimizing user behavior [3] Structure Transparent Interface Component - The interface enhances the credibility of the risk management system through explainability, allowing users to understand the basis of model suggestions [3] - This transparency significantly reduces user resistance to risk management interventions, especially in sensitive scenarios [3] Application and Impact - The framework has been tested in various financial institutions, resulting in a decrease in bad debt rates by 18% and a reduction in user complaints by nearly 40% in one internet bank [4] - In an insurance platform, fraud detection rates improved by 23%, while misjudgment rates for claims dropped by over 30% [4] - The framework is not just a product but a methodological capability platform, promoting industry-wide adoption of "structural governance" over "rule restrictions" [4] Broader Implications - The new risk governance perspective is influencing industry trends, viewing risk as a natural feedback mechanism rather than an adversary [5] - The framework aims to transform risk management into a collaborative, self-evolving ecosystem, enhancing user understanding and trust in the system [5] - As market complexity and regulatory precision increase, explainable risk management is expected to become a foundational component of financial technology systems [5]