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GRA · Yapay Zekâ ve Finansal Model Riski

Mekânsal finansSermayenin tehlike, maruziyet ve geçişle buluştuğu yer

Finansal disipline genel bakış

Model riski management is the discipline of controlling the risks that arise from using quantitative models and AI sistemleri in financial decisions. It developed in banking around valuation and capital models and now covers credit, insurance pricing, reserving, trading, fraud detection, anti-money-laundering, customer service and generative AI tools. This page describes the discipline as research rather than execution: where models are used, how they are governed and what shared understanding the industry needs.

STEM yöntemleri
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Bu alanın uyguladığı temel yöntemler
Konular
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Alandaki konu sayfaları
Muhataplar
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Ağ genelinde ortak alanlardaki platformlar
Rapor serileri
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01Where models are used

The model landscape in finance.

AlanTypical modelsMain risks
Kredi vermeCredit scoring, probability of default, affordabilityUnfair outcomes, poor performance in new conditions
SigortaPricing, reserving, catastrophe and claims modelsMispricing, reserve shortfalls, discrimination
PiyasalarValuation, trading, piyasa riskiMis-valuation, correlated behaviour in stress
Sermaye ve likiditeRegulatory and economic capital, stress testingUnderstated risk, false comfort
Finansal suçFraud and transaction monitoringMissed crime, excessive false alerts
Operations and serviceGenerative AI assistants, document processingInaccurate output, data leakage, dependency
02İzleme

Alana genel bakış.

Bir ekibin bu konuyu izlemek için kullanacağı ekran: ölçütleri, durumları ve kararları. Adlar örnektir ve değerler açıklayıcıdır.

Field overview: the screen’s structure, terms and states for AI and model risk.Örnek adlar ve örnekleyici değerler; bir tasarımdır, kayıt veya canlı bir sistem değildir.
03Model uses

Where models drive financial decisions.

KullanModel typeMain risk
Credit decisionsScorecards and machine learningBias, drift
Insurance pricing and reservingGLMs, machine learning, catastrophe modelsNon-stationarity, opacity
Capital and stress testingInternal models, scenario modelsAssumption risk
Fraud and AMLAnomaly detectionFalse positives and negatives
Operations and customer serviceÜretken yapay zekâHallucination, data leakage

Shocks travel between these parts, and AI and Financial Model Riski studies the links: When does a model fail, and who notices? The evidence base is model records and monitoring data.

04How models are governed

The core of the discipline.

  1. 01

    Inventory and tiering

    Knowing every model in use, its owner and its materiality. See model risk governance.

  2. 02

    Kalkınma standartları

    Documented design, data and assumptions, with testing before use.

  3. 03

    Independent validation

    Effective challenge by people independent of the developers.

  4. 04

    İzleme

    Ongoing checks of performance and drift, with thresholds for review. See model monitoring and drift.

  5. 05

    Use controls

    İnsan gözetimi and limits on where a model may be used. See human-in-the-loop controls.

05Research, not execution

What shared understanding the industry needs.

Each institution governs its own models. What none can see alone is how model riski accumulates across the sector: how many firms rely on similar data, vendors or techniques, how methods for validating machine learning and generative AI compare, and where common failures are emerging. That precompetitive picture is the focus of this platform. How model failures can spread is set out on financial-system transmission.

Methods include uncertainty quantification and model validation, AI evaluation and assurance engineering, machine learning and statistics. Sector scenarios appear on the scenarios page as they are reviewed.

Nothing on this platform validates, approves or certifies any model, vendor or institution, and nothing is legal, regulatory or investment advice. Analysis is sector-level research.

07Sayfalar

Pages in Financial discipline overview.

08Sorular

Sorular ve yanıtlar.

Does GRA advise firms on their models?
GRA, finansal hizmetler sektörü için kâr amacı gütmeyen bir iş birliği örgütüdür. Borç vermez, sigortalamaz, derecelendirmez, yatırım yapmaz, işlemler hakkında danışmanlık yapmaz veya işlem düzenlemez; hazırlık çalışması onay anlamına gelmez.
Is model risk only a banking concern?
No. It applies wherever models inform financial decisions, including insurance, asset management, payments and fintech, and increasingly where generative AI is used in operations.
Why does AI change model risk management?
Because machine learning models can be numerous, frequently retrained, harder to explain and more sensitive to shifts in data, and generative AI can be adopted informally across an institution. Frameworks built for a smaller number of statistical models must adapt their inventories, validation methods and monitoring.
Disiplin hangi yöntemleri kullanır?
Artificial intelligence and machine learning, AI evaluation and assurance engineering, uncertainty quantification and model validation, and probability and statistics. See STEM temeli sayfasındadır.
RaporlarKatkıda bulunun

Bu sayfadan kanıt katkısında bulunun.

Raporlar 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 AllianceYapay Zekâ ve Finansal Model RiskiYöntemler ve TeknikÜstel Teknoloji
Yapay Zekâ ve Finansal Model Riski

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