The Global Centre for Risk and Innovation (GCRI)
Risk Academy, a Nexortium programmeRisk Science and MethodsDepartment of Quantitative Risk Analysis
Learning from data without hiding the error
Machine learning entered risk analysis through its methods, not its headlines: emulators that stand in for slow hazard and catastrophe models, classifiers of damage from imagery, anomaly detectors on sensor streams and foundation models trained on satellite archives.
risksacademy.comrisk-sciencediscipline
Twin figures for the central Apennines: L'Aquila, Amatrice and Norcia, counted from its layers at their stated dates.
For AI and machine learning for risk analysis: the faculty's twin of L'Aquila on the planet map, the globe and in 3D. Pick a generator, switch layers, run the instruments; every layer prints its provider and date.
Drag to move, right drag or Shift with a drag to turn and tilt, Ctrl with the wheel to zoom. Keyboard: arrows move, Shift with arrows turn and tilt, + and - zoom, 0 resets, V steps through the views, Escape clears.
Flat plan of the same data: relief, the three reference epicentres with magnitude and depth, and the numbers below; the sampler's figure shows one precomputed field with its parameters. The 3D twin opens on devices with WebGL once its data and library have been checked.
The map loads from this site as it nears the view (MapLibre 6.9.1, checked by hash).
The discipline teaches those methods with their uncertainty: calibrated probabilities, conformal prediction sets with stated coverage, validation on held-out events, drift monitoring, and the model risk governance that PRA SS1/23 (in effect since May 2024) and SR 11-7 apply to every model in use. A graduate can train and validate a model on a risk task, state its coverage and say where its training data stop.
This area covers the machine-learning methods risk analysts now use: classification and regression of risk outcomes, emulators of slow physical models, anomaly detection and the quantification of uncertainty in model outputs. It is taught in the department of Quantitative Risk Analysis, Faculty of Risk Science and Methods, through courses still to be authored.
9 subtopics, taught across its courses and labs.
News and research as they are published, per discipline, with the faculty's other tools.
The newest research, preprints, news and humanitarian reports for AI and machine learning for risk analysis, each with its source and date, load here. Open the research and news hub
No course has this as its primary discipline yet. It is listed so the gap stays visible.
Courses also taught here and the connected disciplines teach the methods it will build on.
One for each of the 0 courses with this primary discipline, when you pass its assessed activities; 0 can be earned now.
Earn two discipline achievements in the Department of Quantitative Risk Analysis. In preparation.
Achievements record learning completed here; they do not certify competence. How recognition works.
No ecosystem page covers this subject yet. A page has been requested; no loosely related page is cited in its place.
Nexus Foundry system pages describe what each system and its rooms are designed to do; they are not a service on offer, and studying here gives no access to them.
Open courses in connected disciplines teach the methods this discipline will build on.