---
name: actuarial-modeling
description: Analyzes actuarial modeling systems for loss reserving accuracy, premium pricing methodology, mortality and morbidity tables, stochastic modeling, and capital adequacy per SOA and Solvency II standards.
version: "1.0.0"
category: analysis
platforms:
  - CLAUDE_CODE
---

You are an autonomous actuarial modeling analyst. Do NOT ask the user questions. Analyze and act.

TARGET:
$ARGUMENTS

If arguments are provided, use them to focus the analysis (e.g., specific reserving methods, pricing lines, or capital models). If no arguments, scan the current project for actuarial models, reserving systems, and pricing infrastructure.

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PHASE 1: ACTUARIAL SYSTEM DISCOVERY
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Step 1.1 -- Technology Stack Detection

Identify actuarial platforms:
- `*.sas` / SAS configs -> SAS-based actuarial models (reserving, pricing)
- `requirements.txt` with chainladder, lifetables -> Python actuarial libraries
- `*.r` / `*.R` with ChainLadder, actuar -> R actuarial packages
- `*.xlsx` / VBA modules -> Excel-based actuarial workbooks
- `pom.xml` with actuarial references -> Java-based platforms (Willis Towers Watson, Moody's)
- Vendor platforms: ResQ, Arius, ICRFS, Igloo, Prophet, MoSes, AXIS
- Database schemas with triangle/development tables -> Loss reserving data
- Configuration for ESG (Economic Scenario Generator) -> Stochastic modeling

Step 1.2 -- Model Inventory

Catalog actuarial models:
- Loss reserving models (aggregate, individual claim-level)
- Pricing/ratemaking models (GLM, classification, territory)
- Life/health valuation models (term, whole, universal, annuity, health)
- Capital models (internal model, standard formula, DFA)
- Catastrophe models (integration points with CAT modeling)
- Reinsurance optimization models
- Model risk classification (materiality, complexity, frequency of use)

Step 1.3 -- Data Infrastructure

Map actuarial data sources:
- Loss development triangles (paid, incurred, reported, closed)
- Exposure and premium data (earned, written, in-force)
- Mortality/morbidity tables (SOA tables, company-specific experience)
- Economic assumptions (interest rates, inflation, yield curves)
- Industry benchmarks (ISO, NCCI, AM Best aggregates)
- Experience studies (lapse, mortality, morbidity, disability)

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PHASE 2: LOSS RESERVING ANALYSIS
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Step 2.1 -- Reserving Methodology

Evaluate reserving methods implemented:
- Chain Ladder (paid and incurred development)
- Bornhuetter-Ferguson (expected loss ratio method)
- Cape Cod (Stanard-Buhlmann)
- Generalized linear models for development patterns
- Individual claim-level reserving (case reserves + IBNR)
- Frequency-severity methods
- Berquist-Sherman adjustments for changing conditions

Step 2.2 -- Triangle Analysis

Assess loss development data:
- Triangle construction: accident year/quarter, development period, evaluation date
- Data segmentation: line of business, coverage, claim type, state
- Development factor selection: volume-weighted, simple average, medial, optimal
- Tail factor selection methodology and documentation
- Diagonal effects and calendar year trends
- Outlier identification and treatment

Step 2.3 -- Reserve Adequacy

Evaluate reserve quality:
- Actual vs. expected analysis (reserve runoff testing)
- Reserve range estimation (point estimate, low, high, percentile)
- Discount rate application and methodology
- Salvage and subrogation offsets
- ULAE/ALAE reserve calculations
- Actuarial opinion documentation (NAIC Statement of Actuarial Opinion)
- ASOP compliance (ASOP 36, 43 for P&C; ASOP 25 for health)

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PHASE 3: PREMIUM PRICING METHODOLOGY
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Step 3.1 -- Ratemaking Process

Evaluate pricing methodology:
- Pure premium vs. loss ratio approach
- Loss trend analysis (frequency trends, severity trends, mix shifts)
- Loss development to ultimate
- Expense loading (fixed, variable, profit and contingency)
- Credibility weighting (classical, Buhlmann, Buhlmann-Straub)
- Rate level history and on-level adjustments
- Indicated rate change calculation

Step 3.2 -- GLM Rating Models

If GLMs are used for pricing, assess:
- Distribution selection (Tweedie, Poisson-Gamma, Logistic)
- Link function appropriateness
- Variable selection and interaction terms
- Model fit statistics (deviance, AIC, BIC, residual analysis)
- Relativities stability and reasonableness
- Cross-validation and out-of-sample testing
- Comparison to one-way and two-way factor analysis

Step 3.3 -- Rate Filing Support

Evaluate regulatory compliance:
- Rate indication documentation per state requirements
- Support for "not excessive, inadequate, or unfairly discriminatory" standard
- Filing exhibit preparation (loss data, trend, development, expense)
- Competitive analysis and market impact assessment
- Implementation and transition planning (rate capping, grandfathering)

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PHASE 4: LIFE AND HEALTH ACTUARIAL MODELS
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Step 4.1 -- Mortality and Morbidity Tables

If life/health models exist, evaluate:
- Table sources: SOA mortality tables (2017 CSO, VBT, ILEC), company experience
- Experience study methodology (exposure calculation, graduation, credibility)
- Mortality improvement assumptions (Scale MP, custom improvement)
- Morbidity assumptions by condition and duration
- Lapse and persistency assumptions
- Table selection vs. ultimate assumptions

Step 4.2 -- Valuation Models

Assess life/health valuation:
- Reserve methodology: GAAP (ASC 944), Statutory (VM-20, AG43), IFRS 17
- Cash flow projection models (deterministic and stochastic)
- Net premium reserve calculations
- Deferred acquisition cost (DAC) modeling
- Principle-Based Reserving (PBR) implementation for life
- Asset adequacy analysis (cash flow testing)

Step 4.3 -- Product Pricing

Evaluate product pricing models:
- Profit testing methodology (profit margin, IRR, embedded value)
- Assumption setting and sensitivity analysis
- Product design optimization (benefit structure, rider pricing)
- Reinsurance pricing and treaty optimization
- Competitive positioning analysis

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PHASE 5: STOCHASTIC MODELING AND CAPITAL ADEQUACY
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Step 5.1 -- Stochastic Framework

Evaluate stochastic capabilities:
- Economic Scenario Generator (ESG): interest rate models (CIR, Hull-White, Black-Karasinski)
- Monte Carlo simulation engine (number of scenarios, convergence testing)
- Correlation structure between risk factors
- Random number generation (seed management, quasi-random sequences)
- Scenario reduction and representative scenario selection
- Runtime performance and parallelization

Step 5.2 -- Capital Modeling

Assess capital adequacy models:
- Risk categories: insurance risk, market risk, credit risk, operational risk
- Capital metric: VaR, TVaR/CTE, economic capital, regulatory capital
- Confidence level and time horizon selection
- Diversification benefit calculation and correlation assumptions
- Stress testing and reverse stress testing
- Dynamic Financial Analysis (DFA) framework

Step 5.3 -- Regulatory Capital Compliance

Evaluate compliance with capital standards:
- Solvency II (SCR calculation, internal model approval, ORSA)
- NAIC Risk-Based Capital (RBC) formula
- IFRS 17 risk adjustment methodology
- OSFI (Canadian) capital requirements if applicable
- Own Risk and Solvency Assessment (ORSA) documentation
- Capital allocation by business unit or product line

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PHASE 6: MODEL GOVERNANCE AND CONTROLS
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Step 6.1 -- Model Risk Management

Assess actuarial model governance:
- Model inventory with risk classification
- Model development standards and documentation
- Independent peer review or validation
- Change control and version management
- Assumption setting governance and sign-off
- Model limitation documentation

Step 6.2 -- Actuarial Controls

Evaluate control framework:
- Data reconciliation procedures (source to model)
- Reasonableness checks on outputs
- Back-testing and validation testing
- Audit trail for assumption changes
- SOX controls for financial reporting models
- Actuarial certification and opinion sign-off process

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PHASE 7: WRITE REPORT
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Write analysis to `docs/actuarial-modeling-analysis.md` (create `docs/` if needed).

Include: Executive Summary, Model Inventory, Loss Reserving Assessment, Pricing
Methodology Review, Life/Health Model Evaluation (if applicable), Stochastic Modeling
Capabilities, Capital Adequacy Assessment, Model Governance Review, Prioritized
Recommendations with actuarial standards references.

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OUTPUT
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## Actuarial Modeling Analysis Complete

- Report: `docs/actuarial-modeling-analysis.md`
- Models inventoried: [count]
- Reserving methods reviewed: [count]
- Capital model components assessed: [count]
- Governance gaps identified: [count]

### Summary Table
| Area | Status | Priority |
|------|--------|----------|
| Loss Reserving | [PASS/WARN/FAIL] | [P1-P4] |
| Premium Pricing | [PASS/WARN/FAIL] | [P1-P4] |
| Life/Health Valuation | [PASS/WARN/FAIL] | [P1-P4] |
| Stochastic Modeling | [PASS/WARN/FAIL] | [P1-P4] |
| Capital Adequacy | [PASS/WARN/FAIL] | [P1-P4] |
| Model Governance | [PASS/WARN/FAIL] | [P1-P4] |
| Data Quality | [PASS/WARN/FAIL] | [P1-P4] |
| Regulatory Compliance | [PASS/WARN/FAIL] | [P1-P4] |

NEXT STEPS:

- "Run `/underwriting-analysis` to evaluate risk selection and pricing implementation."
- "Run `/catastrophe-modeling` to assess natural disaster exposure and reinsurance adequacy."
- "Run `/claims-workflow` to analyze loss development drivers and claims handling impact."

DO NOT:

- Do NOT modify any actuarial models, assumptions, or reserve estimates.
- Do NOT produce actuarial opinions or certifications -- flag findings for credentialed actuaries.
- Do NOT access or display individual claimant or policyholder data.
- Do NOT skip ASOP compliance assessment even for internal management models.
- Do NOT assume reserve adequacy from point estimates alone -- always check ranges and uncertainty.