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GRA · AI and Financial Model Risk

Spatial financeWhere capital meets hazard, exposure and transition

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.

Pirsek bike Back to STEM foundation of AI and Financial Model Risk
01Qeyd

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

Method recordSTEM method
  1. RêbazÎhtîmal û statîstîk
  3. Applied inAI and Financial Model Risk
  6. Canonical pageSTEM Commons
02Serlêdan

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 pergalan. They make the evidence behind risk decisions explicit and reproducible. The canonical description is on the 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.

03Exposure, loss and capital

What it means for exposure, loss and capital.

The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.

Transmission channels

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 climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
04Dane

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.

DaneRolNîşan
Decision and outcome recordsPerformance testingÇAVDÊRÎKIRÎ
Score distributionsDrift detectionÇAVDÊRÎKIRÎ
Reference portfoliosBenchmarksREPORTED
05Pêvajo

Pêvajo çawa dimeşe.

Process explorer · Process

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

10 steps5 qonax

Pêvajo

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. 01Event catalogueDataset · 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. 02RûbirûbûnDataset · 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. 08Exceedance probability 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.

06Erêkirin

How results are checked.

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

Kontrol bikinHow 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.

08Stewardship

Who maintains the method.

Zanista pergalan û modelkirin stewards probability and statistics for the Consortium and reviews how it is applied across platforms.

Canonical page: Probability and statistics

09Girêdayî

Ev rûpel li ser Konsorsiyûmê te ber bi kû ve dibe.

10Pirs

Pirs û bersiv.

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?
Çavdêrxaneya canonical method page lists every platform that applies it.
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rêzeya Pergalên Stratejîk 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.

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