actuarial-modeling

name	actuarial-modeling
description	Analyzes actuarial modeling systems for loss reserving accuracy, premium pricing methodology, mortality/morbidity tables, stochastic modeling, and capital adequacy per SOA and Solvency II standards. USE THIS SKILL WHEN: - You need to review or audit actuarial models (reserving, pricing, capital) - Someone asks about loss triangle analysis or reserve adequacy - You are evaluating IBNR calculations, chain ladder methods, or Bornhuetter-Ferguson - A project involves insurance pricing, GLM rating models, or ratemaking - You need to assess Solvency II SCR calculations or RBC compliance - Someone mentions actuarial opinions, ASOP compliance, or SOA standards - You are reviewing stochastic models, ESG configurations, or DFA frameworks - A codebase uses actuarial libraries (chainladder, lifetables, ChainLadder R package) TRIGGER PHRASES: "actuarial", "loss reserving", "IBNR", "chain ladder", "premium pricing", "mortality table", "Solvency II", "capital adequacy", "ratemaking", "GLM pricing", "risk-based capital", "reserve analysis", "actuarial opinion"
version	2.0.0
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.

============================================================ PHASE 1: ACTUARIAL SYSTEM DISCOVERY

Step 1.1 -- Technology Stack Detection

Identify actuarial platforms by scanning for these markers:

• *.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 every actuarial model found. For each model, record:

• Model type (loss reserving, pricing, life valuation, capital, catastrophe, reinsurance)
• Risk classification (materiality: high/medium/low, complexity, frequency of use)
• Owner and last review date (from comments, git history, or documentation)
• Input data sources and output consumers

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)

============================================================ PHASE 2: LOSS RESERVING ANALYSIS

Step 2.1 -- Reserving Methodology

For each reserving model, determine the method and assess appropriateness:

• Chain Ladder (paid and incurred development) -- check for stability of development factors
• Bornhuetter-Ferguson (expected loss ratio method) -- check ELR source and reasonableness
• Cape Cod (Stanard-Buhlmann) -- verify weighting methodology
• Generalized linear models for development patterns -- check model fit
• Individual claim-level reserving (case reserves + IBNR) -- verify completeness
• Frequency-severity methods -- check independence assumption
• Berquist-Sherman adjustments -- verify adjustment rationale

Decision criteria: Flag any model using a single method without cross-validation against alternatives.

Step 2.2 -- Triangle Analysis

Assess loss development data quality:

• Triangle construction: verify accident year/quarter, development period, evaluation date alignment
• Data segmentation: confirm line of business, coverage, claim type, state splits are appropriate
• Development factor selection: compare volume-weighted, simple average, medial, optimal selections
• Tail factor selection: verify methodology is documented and reasonable
• Diagonal effects: check for calendar year trends that distort development
• Outlier identification: confirm treatment is documented and consistent

Step 2.3 -- Reserve Adequacy

Evaluate reserve quality against these benchmarks:

• Actual vs. expected analysis (reserve runoff testing) -- flag if AVE ratio deviates > 5% for 2+ years
• Reserve range estimation -- verify point estimate, low, high, and percentile ranges exist
• Discount rate application -- confirm methodology matches regulatory requirements
• Salvage and subrogation offsets -- verify they are not double-counted
• ULAE/ALAE reserve calculations -- check allocation methodology
• Actuarial opinion documentation -- verify NAIC Statement of Actuarial Opinion compliance
• ASOP compliance -- check ASOP 36, 43 (P&C) and ASOP 25 (health)

============================================================ PHASE 3: PREMIUM PRICING METHODOLOGY

Step 3.1 -- Ratemaking Process

Evaluate the pricing pipeline end to end:

• Pure premium vs. loss ratio approach -- confirm appropriate for the data volume
• Loss trend analysis -- verify frequency, severity, and mix shift trends are separated
• Loss development to ultimate -- confirm consistency with reserving ultimates
• Expense loading -- verify fixed, variable, profit, and contingency loads
• Credibility weighting -- check method (classical, Buhlmann, Buhlmann-Straub) and minimum thresholds
• Rate level history -- verify on-level adjustments are complete and accurate
• Indicated rate change -- confirm calculation ties to exhibits

Step 3.2 -- GLM Rating Models

If GLMs are used for pricing, assess each model for:

• Distribution selection appropriateness (Tweedie, Poisson-Gamma, Logistic)
• Link function selection with justification
• Variable selection -- check for multicollinearity and interaction terms
• Model fit statistics (deviance, AIC, BIC, residual analysis) -- flag poor fits
• Relativities stability -- compare across model iterations
• Cross-validation -- confirm out-of-sample testing is performed
• Comparison to one-way and two-way factor analysis for reasonableness

Step 3.3 -- Rate Filing Support

Evaluate regulatory compliance readiness:

• 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 planning (rate capping, grandfathering, transition rules)

============================================================ PHASE 4: LIFE AND HEALTH ACTUARIAL MODELS

Skip this phase if no life/health models are found. Otherwise:

Step 4.1 -- Mortality and Morbidity Tables

Evaluate table usage:

• Table sources: verify SOA tables (2017 CSO, VBT, ILEC) or company experience are current
• Experience study methodology: check exposure calculation, graduation, credibility
• Mortality improvement assumptions: verify Scale MP or custom improvement is applied
• Morbidity assumptions: check by condition and duration
• Lapse and persistency: verify assumptions match recent experience
• Selection vs. ultimate: confirm appropriate period is used

Step 4.2 -- Valuation Models

Assess reserve methodology against applicable standards:

• GAAP (ASC 944), Statutory (VM-20, AG43), IFRS 17 -- confirm correct standard is applied
• Cash flow projections -- verify both deterministic and stochastic runs exist
• Net premium reserve calculations -- check for accuracy
• DAC modeling -- verify amortization methodology
• PBR implementation -- confirm exclusion test and stochastic reserve calculations
• Asset adequacy analysis -- verify cash flow testing scenarios

Step 4.3 -- Product Pricing

Evaluate product pricing models:

• Profit testing methodology (profit margin, IRR, embedded value)
• Assumption sensitivity analysis -- confirm key assumptions are stress-tested
• Product design optimization (benefit structure, rider pricing)
• Reinsurance pricing and treaty optimization
• Competitive positioning analysis

============================================================ PHASE 5: STOCHASTIC MODELING AND CAPITAL ADEQUACY

Step 5.1 -- Stochastic Framework

Evaluate stochastic modeling infrastructure:

• ESG: identify interest rate model (CIR, Hull-White, Black-Karasinski) and calibration
• Monte Carlo engine: check scenario count (minimum 1,000 for screening, 10,000+ for production)
• Convergence testing: verify results stabilize with increasing scenario count
• Correlation structure: confirm risk factor correlations are justified
• Random number generation: check seed management and quasi-random sequence usage
• Runtime performance: assess parallelization and bottlenecks

Step 5.2 -- Capital Modeling

Assess capital adequacy models:

• Risk categories covered: insurance risk, market risk, credit risk, operational risk
• Capital metric: VaR, TVaR/CTE, economic capital, regulatory capital -- confirm appropriate metric
• Confidence level and time horizon: verify alignment with regulatory requirements
• Diversification benefit: check correlation assumptions and methodology
• Stress testing: confirm both prescribed and reverse stress tests exist
• DFA framework: verify Dynamic Financial Analysis integration if present

Step 5.3 -- Regulatory Capital Compliance

Evaluate compliance with applicable capital standards:

• Solvency II: SCR calculation, internal model approval status, ORSA documentation
• NAIC RBC: verify formula components and action level calculations
• IFRS 17: risk adjustment methodology and confidence level
• OSFI (Canadian): capital requirements if applicable
• ORSA: verify Own Risk and Solvency Assessment is current and comprehensive
• Capital allocation: confirm allocation methodology by business unit or product line

============================================================ PHASE 6: MODEL GOVERNANCE AND CONTROLS

Step 6.1 -- Model Risk Management

Assess governance against regulatory expectations (SR 11-7 / SS3/18):

• Model inventory with risk classification -- flag any models not in the inventory
• Development standards and documentation -- check for completeness
• Independent peer review or validation -- verify independence and qualifications
• Change control and version management -- check for audit trail
• Assumption setting governance and sign-off -- verify approval chain
• Model limitation documentation -- confirm limitations are disclosed to users

Step 6.2 -- Actuarial Controls

Evaluate the control framework:

• Data reconciliation: source-to-model tie-out procedures
• Reasonableness checks: automated bounds checking on outputs
• Back-testing: historical validation results and trending
• Audit trail: assumption change logging with justification
• SOX controls: financial reporting model controls documented and tested
• Certification process: actuarial opinion sign-off workflow and timeline

============================================================ PHASE 7: WRITE REPORT

Write analysis to docs/actuarial-modeling-analysis.md (create docs/ if needed).

Structure the report as:

1. Executive Summary -- 3-5 bullet points of critical findings
2. Model Inventory -- table of all models with risk classification
3. Loss Reserving Assessment -- methodology evaluation and adequacy findings
4. Pricing Methodology Review -- ratemaking and GLM assessment
5. Life/Health Model Evaluation (if applicable)
6. Stochastic Modeling Capabilities -- ESG and Monte Carlo assessment
7. Capital Adequacy Assessment -- regulatory compliance status
8. Model Governance Review -- control gaps and recommendations
9. Prioritized Recommendations -- with actuarial standards references (ASOP, SOA, Solvency II)

============================================================ SELF-HEALING VALIDATION (max 2 iterations)

After producing output, validate data quality and completeness:

1. Verify all output sections have substantive content (not just headers).
2. Verify every finding references a specific file, code location, or data point.
3. Verify recommendations are actionable and evidence-based.
4. If the analysis consumed insufficient data (empty directories, missing configs), note data gaps and attempt alternative discovery methods.

IF VALIDATION FAILS:

• Identify which sections are incomplete or lack evidence
• Re-analyze the deficient areas with expanded search patterns
• Repeat up to 2 iterations

IF STILL INCOMPLETE after 2 iterations:

• Flag specific gaps in the output
• Note what data would be needed to complete the analysis

============================================================ OUTPUT

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.

============================================================ SELF-EVOLUTION TELEMETRY

After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:

• Look for the project path in ~/.claude/projects/
• If found, append to skill-telemetry.md in that memory directory

Entry format:

### /actuarial-modeling — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}

Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.