Programme
Validate a new exposure unit with the market that has to price it.
Holonom.xyz is the public actuarial workbench for testing a proposed exposure unit for AI-enabled institutional liability. The Lloyd's Lab objective is to validate whether holonoms can become a usable rating, reserving, and portfolio-accumulation basis across financial institutions, professional liability, cyber, D&O, and technology E&O. Evidence-infrastructure implementation sponsor: Bankabil.
Programme outputs
Holonom theory note
Establishes the mathematical primitive.
Exposure-unit definition
Makes it actuarially usable.
Rating-factor worksheet
Lets actuaries test it.
Claims friction model
Connects to reserving and LAE.
LOB mapping
Shows where it applies.
Evidence bundle checklist
Connects scoring to real underwriting data.
Target actuarial audience
| Audience | Pain point |
|---|---|
| P&C actuaries | Price AI liability without mature loss history |
| Specialty actuaries | New exposure units for algorithmic accountability |
| Cyber actuaries | Separate cyber failure from AI governance failure |
| Technology E&O actuaries | Better measures of model-driven professional harm |
| Financial institutions actuaries | Evaluate AI in lending, fraud, compliance, underwriting |
| Reinsurance actuaries | Portfolio-level accumulation views of AI decision risk |
| ERM actuaries | Board-level metrics for institutional AI exposure |
Positioning
Holonom gives actuaries a candidate exposure unit for AI liability where historical loss data is insufficient, model-level metrics are incomplete, and institutional evidence quality materially affects claim severity.