Tools (models)

The model bench. Choose the actuarial models that turn your soft data into hard results — by hand, or let Scelo suggest a set for your data.

The canvas

Tools is a node canvas:

• A Dataset Hub node at the centre represents your loaded data.
• Model nodes hang off it, each showing its family (forecast, capital, reserving, climate, …), name, and a tiny key-parameter summary.
• A model library strip lets you add models with a click.

Choosing models

By hand — add from the library:

• Reserving — Chain Ladder, Mack Chain Ladder, Bornhuetter-Ferguson, Bootstrap (IBNR).
• Mortality / longevity — Lee-Carter, Cairns-Blake-Dowd, Life Contingencies.
• Pricing / GLM — GLM · frequency, and more.
• Forecast / capital / climate — WMTR forecast, Economic Scenario Generator, CLIMADA climate hazard exposure.

AI-suggested — click identify models. Scelo reads your dataset's shape and domain and proposes a set, with a short rationale per pick. You can accept, add to, or swap them.

Per-model controls

Each model node has:

• A scoped chat (ASK SCELO ▸) — swap chain-ladder for Mack, explain this model's assumptions, compare models.
• A ↻ rerun and × remove.
• An expand for theory/details on the Hard stage.

Model notation renders mathematically — e.g. a WMTR rationale reads "α_M / α_T / α_R triplet detected".

Other actions

Action	What it does
identify models	AI-suggest a model set for the data
regenerate	Re-run the AI suggestion
re-layout	Snap nodes back to the default layout
export · code	Export the model setup as a script
← back: soft / next: hard →	Move through the pipeline

When your model set looks right: next: hard →.