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GRA · 人工智能与金融模型风险

空间金融资本与危害、风险暴露和转型的交汇之处

面向人工智能与金融模型风险的概率与统计

Probability and statistics underpin financial models: estimating default and claim rates, testing calibration, measuring discrimination and detecting drift. On this platform they supply the measures and tests that show whether a model is performing as intended.

提出咨询 Back to STEM foundation of AI and Financial Model Risk
01登记

Probability and statistics for AI and financial model risk on the record.

方法记录STEM 方法
  1. 方法概率与统计
  3. 应用于AI 与金融 模型风险
  4. 数据3 inputs
  6. 规范页面STEM 公共资源
02申请

What probability and statistics does here.

Probability and statistics provide the mathematical framework for describing uncertainty and learning from data. They underpin estimation, testing and prediction in every risk discipline. In artificial intelligence and financial stability, statistical methods quantify the likelihood and uncertainty of losses across interconnected 系统. They make the evidence behind risk decisions explicit and reproducible. The canonical description is on STEM Commons.

Calibration testing

Predicted default or claim probabilities are compared with observed rates by band, using binomial and other tests suited to the sample size.

Drift statistics

Population stability and distribution distance measures are computed between periods to detect shifts in model inputs and scores.

Group outcome comparison

Outcome and error rates are compared across groups with confidence intervals, so differences are not read from noise.

03风险暴露、损失与资本

What it means for exposure, loss and capital.

风险官、监管者或财政部会要求的风险暴露、损失、情景和资本视图，以及它们据以报告的框架。结构遵循监管和精算实务。数值为说明性：绝非价格、评级或建议。

传导渠道

How a shock in Probability and statistics for AI and financial model risk reaches credit, market, underwriting, liquidity and operational risk, and feeds back.

Channel structure after the Basel Committee’s work on climate-related risk drivers and transmission channels; links shown are typical paths, not estimated exposures.
NGFS 气候情景 · 央行与监管机构绿色金融网络IFRS S2 气候相关披露 · ISSBTCFD 建议 · 金融稳定理事会 TCFD有效管理气候相关金融风险的原则 · 巴塞尔银行监管委员会
04数据

该方法使用的输入。

Datasets and records used by probability and statistics on this platform, with the role each plays and the label its values carry. Sources are listed in 来源登记册.

数据角色标签
Decision and outcome recordsPerformance testing观测
Score distributionsDrift detection观测
Reference portfoliosBenchmarks已报告
05流程

流程如何运行。

Process explorer · Process

Probability and statistics for AI and financial model risk: Catastrophe model pipeline

10 steps5 个阶段

流程

Probability and statistics for AI and financial model risk: Catastrophe model pipeline

巨灾模型通常包括危害、脆弱性、风险暴露与财务模块，并生成损失表、 年均损失、超越曲线、PML 与 TVaR。

  1. 01 危害

    1. 01事件目录数据集 · 模型开发者危害模块包含一个大型模拟事件目录，代表多种多样的合理情景。
    2. 03危害模块模型 · 模型开发者对于每个事件，危害模块给出当地的 危害强度 ，供脆弱性模块读取。

  2. 02 风险暴露与损害

    1. 02风险暴露Dataset · MRM committee风险暴露模块存放组合数据，例如特定地点的信息。
    2. 04脆弱性模块模型 · 模型开发者脆弱性模块量化某一事件对风险暴露对象造成的预期损害。

  3. 03 财务

    1. 05财务模块Model · MRM committee财务模块计算所有事件情景造成的损失。基础损失、总损失与净损失的区别如下所示。
    2. 06事件损失表Dataset · MRM committee事件损失表列出每个事件的年发生概率及其对组合造成的损失。
    3. 07年度损失表Dataset · MRM committee有些模型还会从事件损失中抽样模拟年份。本概述不描述其格式。

  4. 04 指标

    1. 08超越概率 曲线Model · MRM committee发生曲线给出一年中最大损失超过某一金额的概率。累积曲线针对损失总和给出同样的概率。
    2. 09AAL、PML 与 TVaRModel · MRM committee年均损失是事件概率与损失乘积之和。PML 是发生曲线上的一个点。

  5. 05 结果的使用

    1. 10结果的使用Decision · MRM committeeThe tail metrics are used when considering a portfolio’s exposure and the level of catastrophe reinsurance an insurer requires.

依据公开来源对某一外部标准或方法的概述。它描述的是该框架的内容，而非任何运行记录。它并不表明 Nexus 或任何合作伙伴执行、认证或批准某一步骤。关口是该方法的一个步骤，绝不是 Nexus 的批准。

06验证

结果如何核查。

Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.

核查如何开展
Sample sizeTests state power and minimum sample sizes
Multiple testingMany simultaneous tests are corrected for chance findings
Out-of-samplePerformance is reported on data not used for fitting

Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.

07关联

依赖该方法的主题。

08管理责任

由谁维护该方法。

系统科学与建模 stewards probability and statistics for the Consortium and reviews how it is applied across platforms.

Canonical page: Probability and statistics

09已关联

本页在整个联合体中通向何处。

10问题

问题与解答。

What are the limits of probability and statistics in this field?
Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.
结果如何核查？
Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.
本页面上的数值是真实的吗？
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
该方法还应用于何处？
维持社会运转的各个 规范方法页面 列出应用该方法的每个平台。
报告贡献

从本页贡献证据。

报告 is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

全球风险联盟（Global Risks Alliance）人工智能与金融模型风险方法与技术指数级技术

切勿发送个人数据、敏感基础设施细节或您无权分享的任何内容。贡献在使用前会经过审查；发送贡献不赋予署名权或编辑权，赞助也绝不能换取研究结论。

人工智能与金融模型风险

向联盟提出风险暴露或资本问题。

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