Holonom Actuarial Workbench · v0.1

A geodesic exposure unit for institutional AI liability.

Actuaries need an exposure basis for AI liability where loss history is thin and model-level metrics are insufficient. The holonom measures normalized avoidable institutional distortion accumulated by an AI-enabled decision as it moves through banking, insurance, legal, audit, and evidentiary pathways — designed for rating, reserving, and portfolio accumulation.

Proposed exposure unit · not yet an accepted actuarial standard
The primitive
dAI-enabled decision
γactual institutional path
γ*minimum-distortion path
κnormalized underwriting unit
Positioning

A holonom is a normalized exposure unit for avoidable institutional distortion in AI-enabled decision pathways.

Rating factor

P = P₀ + λ·ΣH. A candidate additive risk charge grounded in evidence quality, defensibility, and coverage ambiguity.

Reserving lens

R = R₀ + α·H_claims. Path-dependent claim friction feeds LAE, defense-cost load, and uncertainty margin.

Portfolio accumulation

ΣH rolls up by LOB, use case, severity, evidence quality, and defensibility class — an aggregate exposure view, not a governance score.

Line-of-business mapping

Applies wherever AI-enabled decisions transport into insurable outcomes.

Financial Institutions
Tech E&O
Professional Indemnity
Cyber
D&O
Management Liability
A missing exposure basis

Existing AI risk proxies are weak. Holonoms are stronger.

Existing proxy
Number of modelsIgnores institutional use
Number of decisionsIgnores severity and evidence
RevenueToo blunt
UsersNot liability-specific
API callsActuarially shallow
Governance maturityQualitative, not exposure-based
Proposed exposure basis
Holonoms

Measures normalized avoidable institutional distortion per transported decision. Path-dependent. Evidence-aware. Designed for rating, reserving, and portfolio accumulation where historical loss data is sparse.

Roadmap
01
Paper product

Theory note, rating worksheet, LOB mapping, claims appendix.

02
Calculator

This web tool — score decisions, indicate premium, reserving lens.

03
SDK

Automated ingestion of AI decision logs and evidence bundles.

04
Portfolio engine

Reinsurance-grade aggregate H, correlation, capital load.

Open call for advisors

Actuarial, mathematical, legal, and insurance advisors are invited to critique the unit definition.

Holonom v0.1 is an indicative exposure model, not a filed rating plan or an actuarial standard of practice. The framework hardens through adversarial review before commercialization.

From model risk to measurable exposure units.

Open the workbench, score a sample portfolio, and see the holonom pricing and reserving indications update live.

Holonom.xyz defines the unit. Evidence-infrastructure implementation: Bankabil.