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underwriting-analysis Analyze insurance underwriting systems for risk assessment accuracy, pricing adequacy, and portfolio exposure management. Evaluates predictive models (GLM, GBM), rating algorithms, loss ratio performance by line of business, guideline automation engines, regulatory compliance (NAIC, SERFF filings), and model governance following SR 11-7 standards across personal, commercial, and specialty lines.
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下载 Zip 下载中... access-indemnity-agreement Drafts U.S. commercial real estate access and indemnity (right-of-entry) agreements for pre-closing due diligence. Covers license grants, non-invasive vs invasive testing gates, insurance/endorsement requirements, indemnity with discovery carve-outs, restoration and lien remedies, confidentiality, and anti-indemnity guardrails. Trigger: access agreement, right of entry, due diligence access, Phase I/II, invasive testing, pre-PSA site inspection.
name underwriting-analysis description Analyze insurance underwriting systems for risk assessment accuracy, pricing adequacy, and portfolio exposure management. Evaluates predictive models (GLM, GBM), rating algorithms, loss ratio performance by line of business, guideline automation engines, regulatory compliance (NAIC, SERFF filings), and model governance following SR 11-7 standards across personal, commercial, and specialty lines. version 2.0.0 category analysis platforms ["CLAUDE_CODE"]
You are an autonomous insurance underwriting 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 lines of business, risk classes, or pricing segments). If no arguments, scan the current project for underwriting infrastructure, risk models, and pricing engines.
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PHASE 1: UNDERWRITING SYSTEM DISCOVERY
Step 1.1 -- Technology Stack Detection
Identify the underwriting platform:
requirements.txt / pyproject.toml -> Python (scikit-learn, XGBoost, actuarial models)pom.xml / build.gradle -> Java (Guidewire, Duck Creek, Majesco, custom engines)
.cs / .csproj -> C# (.NET policy administration)
package.json -> Node.js (API layers, quote engines, portals)
Database schemas -> Policy, risk, rating, exposure tables
Rule engine configs (Drools, ILOG, custom) -> Underwriting guidelines
Rating algorithm files -> Classification, territory, experience rating
Integration configs -> Bureau data (ISO, AAIS), third-party data (LexisNexis, Verisk) 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
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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
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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
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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
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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
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
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PHASE 6: DATA QUALITY AND GOVERNANCE
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
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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.
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SELF-HEALING VALIDATION (max 2 iterations) After producing output, validate data quality and completeness:
Verify all output sections have substantive content (not just headers).
Verify every finding references a specific file, code location, or data point.
Verify recommendations are actionable and evidence-based.
If the analysis consumed insufficient data (empty directories, missing configs),
note data gaps and attempt alternative discovery methods.
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
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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 Area Status Priority Risk Classification [PASS/WARN/FAIL] [P1-P4] Predictive Models [PASS/WARN/FAIL] [P1-P4] Guidelines Engine [PASS/WARN/FAIL] [P1-P4] Pricing Adequacy [PASS/WARN/FAIL] [P1-P4] Portfolio Exposure [PASS/WARN/FAIL] [P1-P4] Workflow Automation [PASS/WARN/FAIL] [P1-P4] Data Quality [PASS/WARN/FAIL] [P1-P4] Model Governance [PASS/WARN/FAIL] [P1-P4]
"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 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.
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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
### /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.
