Assumption Setting

Assumption Setting

When to Use

• Define or update assumption sets for pricing, reserving, capital, or ALM models
• Document assumption rationale, sources, and effective dates for governance or audit
• Classify assumptions by category (economic, demographic, behavioral, operational)
• Apply selection methodology (experience, industry tables, judgment, blending)
• Design sensitivity and scenario grids tied to material drivers
• Establish governance: owners, approval tiers, change control, version history
• Build assumption packs differentiated by use case (rate indication vs valuation vs ORSA)
• Reconcile emerging experience vs last-approved assumptions before model runs
• Benchmark assumptions against industry or regulatory reference points (overview)

When NOT to Use

• Execute full pricing indications, IBNR triangles, or reserve ultimates as primary deliverable → actuary
• Scope actuarial consulting engagements, SOW, due diligence, or opinion support governance → actuarial-consulting
• Teach P&C coverages, FNOL, or underwriting without assumption governance lens → property-casualty-insurance
• Teach life/health products, benefits, or distribution without assumption pack focus → life-health-insurance
• Pension funding, ERISA, or retirement plan design → pension-retirement-funds
• Corporate budgets, investor metrics, or non-insurance FP&A → financial-analyst (if installed)
• Executive strategy without actuarial/risk assumption workstream → business-consultant
• Legal interpretation of policy wording, filings, or statutory sign-off → commercial-counsel
• IFRS note legal wording or general ledger recognition → ifrs
• SOC 2 / ISO control evidence without actuarial models → compliance-engineer

Related skills

Core Workflows

1. Scope the assumption exercise

1. Purpose — Pricing, valuation, capital, ALM, planning, or stress testing
2. Model — Name, version, owner; which outputs consume each assumption
3. Materiality — Drivers ranked by impact on key metrics (LR, reserves, SCR, surplus)
4. Population — In-force, new business, closed block; segment dimensions
5. Effective date — Align with valuation date, rate filing, or budget cycle

See references/assumption_setting_scope_and_principles.md.

2. Inventory categories and sources

1. List assumptions by category (economic, demographic, behavioral, operational)
2. Map each to source (company experience, industry table, vendor, judgment)
3. Flag prescribed vs company-specific and jurisdictional constraints
4. Note dependencies (e.g., lapse tied to crediting rate; trend tied to inflation)
5. Record data period and quality caveats per assumption

See references/assumption_categories_and_sources.md.

3. Select and justify assumptions

1. Run or reference experience studies (A/E); coordinate with actuary for technical fitting
2. Apply credibility blending where data is thin
3. Document expert judgment when overriding data
4. Set level, trend, and volatility components separately when material
5. Compare to benchmarks and prior approved set; explain deltas

See references/selection_methodology_and_judgment.md.

4. Govern, document, and control changes

1. Assign owner and approver per assumption or pack tier
2. Version the assumption pack (ID, date, model compatibility)
3. Maintain change log: prior → proposed → approved, with rationale
4. Attach exhibits: A/E tables, sensitivity summary, peer review sign-off
5. Archive reproducible data cuts per model risk policy

See references/governance_documentation_and_change_control.md.

5. Sensitivities, scenarios, and stress

1. Identify top drivers from materiality or prior sensitivity work
2. Define base, adverse, favorable, and regulatory/stress scenarios
3. Keep grids parsimonious—avoid combinatorial explosion
4. Align scenarios with risk appetite and ORSA/capital narrative (overview)
5. Document correlations or explicit independence assumptions

See references/sensitivity_scenarios_and_stress.md.

6. Line-of-business assumption packs

1. Select pack template: P&C, life/health, pension, or multi-line
2. Pull LOB-specific drivers (e.g., development factors vs mortality improvement)
3. Cross-check pack against product and regulatory context skills
4. Separate packs for pricing vs reserving vs capital when conventions differ
5. Hand off model execution to actuary after pack is approved

See references/line_of_business_assumption_packs.md.

Assumption pack checklist

Before models run, confirm:

• Every material driver has owner, source, and effective date
• Pricing vs valuation vs capital basis differences documented
• Changes from prior pack bridged with quantified impact where possible
• Sensitivities on top 3–5 drivers completed or scheduled
• Judgment overrides explicitly approved and time-bounded if interim
• No legal, statutory, or accounting sign-off claimed by the agent

Deliverable standards

State uncertainty and data limitations. Do not present outputs as appointed-actuary, legal, or audit opinions.

When to load references

• Scope and principles → references/assumption_setting_scope_and_principles.md
• Categories and sources → references/assumption_categories_and_sources.md
• Selection and judgment → references/selection_methodology_and_judgment.md
• Governance and change control → references/governance_documentation_and_change_control.md
• Sensitivity and stress → references/sensitivity_scenarios_and_stress.md
• LOB packs → references/line_of_business_assumption_packs.md