Explains the BCBS 239 principles for risk data
Principles for effective risk data aggregation and risk reporting: governance (1), data architecture and IT infrastructure (2), accuracy and integrity (3), completeness (4), timeliness (5), adaptability (6), and accuracy, comprehensiveness, clarity and distribution of risk reports (7-11); principles 12-14 are for supervisors (BCBS 239, 2013).
Can AI do this?
AI does this well Most of the tested AI models master this. 3 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 99% | Mastered |
| gemini-3.5-flash-lite | Low cost | 10% | Not mastered |
| gpt-6-luna | Low cost | 99% | Mastered |
| claude-haiku-5-5 | Low cost | 99% | Mastered |
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: Principles for effective risk data aggregation and risk reporting: governance (1), data architecture and IT infrastructure (2), accuracy and integrity (3), completeness (4), timeliness (5), adaptability (6), and accuracy, comprehensiveness, clarity and distribution of risk reports (7-11); principles 12-14 are for supervisors (BCBS 239, 2013).
How it is assessed: Answers a written question on the BCBS 239 principles correctly
Related knobits
Needed for
All 10 knobits of Select the critical data elements.
Source: Practice the stage 1 reference and review checklist rest on