Skip to content

GRA · هوش مصنوعی و ریسک الگوی مالی

مالی فضاییجایی که سرمایه به مخاطره، مواجهه و گذار برمی‌خورد

Probability and statistics for AI and financial model risk

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.

ثبت پرس‌وجو Back to STEM foundation of AI and Financial Model Risk
01ثبت‌خانه

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

Method recordSTEM method
  1. روشاحتمال و آمار
  3. Applied inهوش مصنوعی و ریسک الگوی
  4. داده3 inputs
  6. Canonical pageSTEM Commons
02درخواست

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 سامانه‌ها. 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.

03مواجهه، زیان و سرمایه

What it means for exposure, loss and capital.

نماهای مواجهه، زیان، سناریو و سرمایه که یک مدیر ریسک، ناظر یا وزارت دارایی می‌پرسد، همراه با چارچوب‌هایی که بر پایهٔ آن‌ها گزارش می‌دهد. ساختارها از رویهٔ نظارتی و اکچوئری پیروی می‌کنند. مقادیر توضیحی است: هرگز قیمت، رتبه یا توصیه نیست.

مجاری انتقال

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.
سناریوهای اقلیمی NGFS · شبکهٔ سبزسازی نظام مالیIFRS S2 افشاهای مرتبط با اقلیم · ISSBتوصیه‌های TCFD · هیئت ثبات مالی TCFDاصول مدیریت مؤثر ریسک‌های مالی مرتبط با اقلیم · کمیتهٔ بازل برای نظارت بانکی
04داده

What the method consumes.

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 the source register.

دادهنقشبرچسب
Decision and outcome recordsPerformance testingمشاهده‌شده
Score distributionsDrift detectionمشاهده‌شده
Reference portfoliosBenchmarksگزارش‌شده
05فرایند

نحوهٔ اجرای فرایند.

Process explorer · Process

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

10 steps5 مرحله

فرایند

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

Catastrophe models usually have hazard, vulnerability, exposure and financial modules, and produce loss tables, average annual loss, exceedance curves, PML and TVaR.

  1. 01 Hazard

    1. 01فهرست رویدادهاDataset · Model developerThe hazard module holds a large catalogue of simulated events that represents a wide range of plausible scenarios.
    2. 03Hazard moduleModel · Model developerFor each event the hazard module gives the local hazard intensity that the vulnerability module reads.

  2. 02 Exposure and damage

    1. 02مواجههDataset · MRM committeeThe exposure module houses the portfolio data, such as location-specific information.
    2. 04Vulnerability moduleModel · Model developerThe vulnerability module quantifies the expected damage to the exposures from an event.

  3. 03 Financial

    1. 05Financial moduleModel · MRM committeeThe financial module calculates losses from all event scenarios. Ground-up, gross and net losses differ as shown below.
    2. 06Event loss tableDataset · MRM committeeAn event loss table lists each event with its annual occurrence probability and its loss for the portfolio.
    3. 07Year loss tableDataset · MRM committeeSome models also sample simulated years from event losses. This outline does not describe their format.

  4. 04 Metrics

    1. 08احتمال فراگذشت curvesModel · MRM committeeThe occurrence curve gives the chance that the largest loss in a year exceeds an amount. The aggregate curve does so for the sum of losses.
    2. 09AAL, PML and TVaRModel · MRM committeeAverage annual loss sums event probability times loss. PML is one point on the occurrence curve.

  5. 05 Use of results

    1. 10Use of resultsDecision · MRM committeeThe tail metrics are used when considering a portfolio’s exposure and the level of catastrophe reinsurance an insurer requires.

Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval.

06اعتبارسنجی

How results are checked.

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

بررسیHow it is done
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.

07Connections

Topics that rely on this method.

08تولیت

Who maintains the method.

علوم سامانه‌ها و مدل‌سازی stewards probability and statistics for the Consortium and reviews how it is applied across platforms.

Canonical page: Probability and statistics

09متصل

جایی که این صفحه در سراسر کنسرسیوم به آن می‌رسد.

10پرسش‌ها

پرسش و پاسخ.

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.
How are results checked?
Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.
Are the values on this page real?
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.
Where else is this method applied?
رصدخانهٔ canonical method page lists every platform that applies it.
گزارش‌هامشارکت

از این صفحه شواهد مشارکت دهید.

گزارش‌ها 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.

⁨Global Risks Alliance⁩ریسک هوش مصنوعی و مدل‌های مالیروش‌ها و فنیفناوری نمایی

هرگز داده شخصی، جزئیات حساس زیرساخت یا چیزی را که حق اشتراکش را ندارید نفرستید. هر مشارکت پیش از استفاده داوری می‌شود؛ ارسال آن حق مؤلفی یا ویرایشی نمی‌دهد و حمایت مالی هرگز یافته‌ای را نمی‌خرد.

ریسک هوش مصنوعی و مدل‌های مالی

یک پرسش دربارهٔ مواجهه یا سرمایه را نزد اتحاد مطرح کنید.

اطلاعات و درخواست‌ها امروز باز است. هر درخواست یک شمارهٔ پیگیری می‌گیرد و گام بعدی را مشخص می‌کند؛ درخواست‌های عضویت، پرداخت‌ها، تخصیص رایانش و ثبت‌نام‌ها تنها زمانی باز می‌شوند که تیم، شرایط و ظرفیت داوری آن‌ها فراهم باشد.

یک آزمایش آمادگی پیشنهاد دهید دیدن همهٔ مسیرهای تماس
حقوقی

نقشه‌ها، داده‌ها و ارقام این سایت برای زمینه و اطلاعات عمومی نشان داده می‌شوند. توصیه، پیش‌بینی یا هشدار نیستند؛ حدود این اطلاعات در سلب مسئولیت‌ها و اعلامیه‌های داده بیان شده است.