Underwriting Analysis

Step 1.2 -- Lines of Business Mapping

Identify covered products:

• Personal lines: auto, homeowners, renters, umbrella, pet
• Commercial lines: general liability, property, workers comp, BOP, professional liability
• Specialty: cyber, D&O, E&O, marine, aviation, surety
• Life and health: term, whole, universal, disability, medical stop-loss
• Reinsurance: treaty, facultative, excess of loss, quota share

Step 1.3 -- Data Source Inventory

Map underwriting data feeds:

• Application/submission data capture
• Third-party data: credit scores, MVR, CLUE, building data, flood zones
• Bureau rates and loss costs (ISO, NCCI, AAIS)
• Geospatial data (aerial imagery, property characteristics, hazard zones)
• IoT/telematics data (connected devices, usage-based insurance)
• Claims history and loss experience

============================================================ PHASE 2: RISK ASSESSMENT MODEL ANALYSIS

Step 2.1 -- Risk Classification

Evaluate risk segmentation:

• Classification variables and rating factors
• Territory definitions and geographic risk differentiation
• Experience rating and schedule rating implementation
• Risk tiering (preferred, standard, non-standard, high-risk)
• Multivariate vs. univariate rating approaches
• Generalized Linear Model (GLM) implementation if used

Step 2.2 -- Predictive Model Assessment

Analyze predictive underwriting models:

• Model types: GLM, GBM, neural networks, ensemble methods
• Target variables: loss frequency, loss severity, loss ratio, conversion
• Feature engineering and selection methodology
• Model validation: lift charts, Gini coefficient, actual-vs-expected analysis
• Model monitoring: performance degradation detection, recalibration triggers
• Regulatory approval status for each model in use

Step 2.3 -- Underwriting Guidelines Engine

Assess automated guidelines:

• Rule engine implementation (decision tables, rule trees, scoring)
• Acceptable risk criteria by line and class
• Declination and referral triggers
• Appetite management (target segments, restricted classes, prohibited risks)
• Exception handling and authority levels
• Rule versioning and change management

============================================================ PHASE 3: PRICING ADEQUACY ANALYSIS

Step 3.1 -- Rate Structure

Evaluate pricing components:

• Base rate derivation (bureau loss costs, proprietary analysis)
• Rating algorithm: multiplicative, additive, or hybrid
• Rating factor relativities and their statistical support
• Minimum premium and expense constant application
• Package and account pricing logic
• Discount and surcharge schedules

Step 3.2 -- Loss Ratio Analysis

Assess profitability metrics:

• Earned premium vs. incurred loss calculations
• Loss ratio by line, class, territory, agent, tier
• Loss ratio trend analysis (calendar year, accident year, policy year)
• Combined ratio components (loss, LAE, commission, operating expense)
• Rate adequacy testing (actual vs. expected loss ratios)
• Pricing error detection (mis-rated policies, incorrect classifications)

Step 3.3 -- Competitive Positioning

Evaluate market competitiveness:

• Comparative rater integration (EZLynx, Applied Rater, Vertafore)
• Win/loss analysis by premium segment and risk class
• Hit ratio tracking by channel and producer
• Price optimization constraints (regulatory limits, consumer fairness)
• Price elasticity modeling if implemented

============================================================ PHASE 4: PORTFOLIO EXPOSURE MANAGEMENT

Step 4.1 -- Aggregation Analysis

Assess concentration risk:

• Geographic concentration (by zip code, county, state, CRESTA zone)
• Line of business concentration
• Single-risk and clash exposure limits
• Industry/class concentration for commercial lines
• Agent/producer concentration
• Policy limit distribution analysis

Step 4.2 -- Capacity Management

Evaluate capacity controls:

• Per-risk and per-occurrence limits
• Aggregate limit tracking and availability
• Reinsurance treaty alignment with gross writings
• Net retention analysis by risk category
• Growth management and appetite enforcement

Step 4.3 -- Regulatory Compliance

Check underwriting compliance:

• State-specific rating rules and filing requirements
• NAIC Market Conduct standards
• Unfair discrimination testing (protected class analysis)
• Rate filing documentation and support
• AM Best and rating agency requirements for underwriting discipline
• SERFF filing compliance

============================================================ PHASE 5: WORKFLOW AND AUTOMATION ASSESSMENT

Step 5.1 -- Submission Processing

Evaluate submission workflow:

• Submission intake (portal, email, API, clearinghouse)
• Data extraction and pre-population (OCR, NLP on applications)
• Straight-through processing rate for auto-bindable risks
• Referral routing and workload distribution
• Quote turnaround time tracking
• Decline notification and reason capture

Step 5.2 -- Decision Support

Assess underwriter tools:

• Risk scoring dashboards and summary views
• Comparable risk analysis (similar accounts, historical pricing)
• Loss run analysis and interpretation
• Authority management (binding authority by tier, limit, line)
• Underwriter performance tracking (hit ratio, loss ratio, volume)

Step 5.3 -- Renewal Management

Check renewal processes:

• Renewal identification and timeline management
• Automated renewal pricing with rate change application
• Non-renewal and cancellation workflow compliance
• Retention analysis and intervention triggers
• Remarketing and re-underwriting criteria

============================================================ PHASE 6: DATA QUALITY AND GOVERNANCE

Step 6.1 -- Data Quality

Assess data integrity:

• Application data validation rules
• Geocoding accuracy for property risks
• Classification code validation (SIC, NAICS, ISO class)
• Premium audit reconciliation
• Data completeness metrics by field and line of business

Step 6.2 -- Model Governance

Evaluate model risk management:

• Model inventory and risk tiering per SR 11-7 / NAIC guidelines
• Independent model validation process
• Model change management and approval workflow
• Documentation standards for models in production
• Ongoing monitoring and recalibration schedule

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

Write analysis to docs/underwriting-analysis-report.md (create docs/ if needed).

Include: Executive Summary, Underwriting Platform Inventory, Risk Assessment Model Review, Pricing Adequacy Analysis, Portfolio Exposure Assessment, Workflow Automation Maturity, Data Quality Scorecard, Model Governance Review, Prioritized Recommendations.

============================================================ 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

Underwriting Analysis Complete

• Report: docs/underwriting-analysis-report.md
• Lines of business reviewed: [count]
• Risk models assessed: [count]
• Pricing gaps identified: [count]
• Compliance issues found: [count]

Summary Table

NEXT STEPS:

• "Run /actuarial-modeling to evaluate loss reserving and premium pricing models."
• "Run /catastrophe-modeling to assess natural disaster exposure and PML estimates."
• "Run /claims-workflow to analyze claims adjudication and its impact on loss ratios."

DO NOT:

• Do NOT modify any rating algorithms, underwriting rules, or policy data.
• Do NOT access or display personally identifiable policyholder information.
• Do NOT make definitive rate adequacy conclusions without actuarial validation.
• Do NOT skip regulatory compliance checks even for surplus lines or non-admitted business.
• Do NOT assume predictive model fairness without testing for unfair discrimination.

============================================================ 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:

### /underwriting-analysis — {{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.