The Global Risks Alliance (GRA)
The research behind AI and Financial Model Risk: the questions it asks, the methods it uses and how evidence is reviewed before it is published.
GRA · In development · service level R0
AI and Financial Model Risk research is the evidence work behind the platform: questions such as model risk governance and credit and pricing models, answered with artificial intelligence and machine learning as well as AI evaluation and assurance engineering and reviewed before publication. Every result carries an evidence label, from observed to modelled or expert judgement, and missing data is shown as missing, never filled silently.
Fig.How a finding is reviewed before it is published. Design, not a record.
For professional research, in GRA's spatial-finance reading, the map opens on night lights, population density and true-colour imagery with night lights tonight from NASA GIBS, framed on California; 3 further NASA layers wait in the list.
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NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
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For professional research, in GRA's spatial-finance reading, the map opens on night lights, population density and true-colour imagery with night lights tonight from NASA GIBS, framed on California; 3 further NASA layers wait in the list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
Layers and dates
Inventory, ownership and validation.
Finance-specific automated decisions.
Use cases and failure controls.
Interpretation, bias and accountability.
Changes over time and review thresholds.
Authority and approvals.
| Question | Evidence used |
|---|---|
| How to govern generative AI? | model records and monitoring data |
| Do explanations help? | SAR flood extents |
| How do common models herd? | validation methods |
AI and Financial Model Risk research concerns model and AI risk. It is written for model risk managers and validators and data scientists in finance, carries an evidence label on every value and never performs any institution's own analysis of fairness opinions.
Results are labelled as observed, officially reported, estimated, modelled, forecast, scenario or expert judgement. Missing data is shown as missing, never filled silently.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Research integrity | Conduct of research in line with principles of reliability, honesty, respect and accountability, as the European Code of Conduct for Research Integrity sets them out. |
| Evidence label | A tag naming the kind of evidence, such as observed or modelled, that should survive every summary. |
| Reproducibility | Obtaining consistent results using the same data and analysis code as the original study. |
| Probability of default | The likelihood that a borrower defaults within a period, usually one year. |
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.