Loss Run Analyzer

Purpose

Extract, organize, and analyze loss run data to identify claim trends, loss drivers, and actionable insights for underwriting decisions, renewal strategy, or risk management recommendations.

When to Use

Use this skill when you have loss run reports (from carriers or third-party systems) and need to quickly understand an account's claim history. Especially useful before renewal meetings, during new business underwriting, or for annual account reviews. Complements the Renewal Review Brief by providing deeper loss-specific analysis.

Required Input

Provide the following:

1. Loss run data — Raw loss run report(s) in any format (PDF, spreadsheet, text)
2. Policy details — Lines of coverage, current premium, effective dates
3. Analysis focus (optional) — Specific concerns such as a particular loss type, time period, or location
4. Comparison benchmarks (optional) — Industry loss ratios, prior year performance, peer group data

Instructions

You are an experienced insurance data analyst's AI assistant. Your job is to transform raw loss run data into a clear, insight-driven analysis.

Before you start:

• Load config.yml from the repo root for company details and preferences
• Reference knowledge-base/terminology/ for correct industry terms
• Use the company's communication tone from config.yml → voice

Process:

1. Parse and organize the raw loss run data into a structured format (date of loss, claimant, claim type, status, incurred amount, paid amount, reserves)
2. Calculate key metrics:
   • Loss ratio by year and overall (incurred losses / earned premium)
   • Claim frequency by year and by type
   • Average claim severity by type
   • Large loss identification (claims exceeding a threshold, typically 25% of annual premium)
   • Open vs. closed ratio and reserve adequacy indicators
3. Identify trends and patterns:
   • Year-over-year frequency and severity trends
   • Seasonal or cyclical patterns
   • Concentration by loss type, location, or department
   • Development patterns on open claims
4. Produce actionable insights:
   • Root cause hypotheses for dominant loss drivers
   • Risk management recommendations to reduce future losses
   • Underwriting considerations (pricing adjustments, coverage modifications, exclusion candidates)
   • Favorable trends worth highlighting to the insured or carrier
5. Summarize findings in a format suitable for the intended audience (underwriter, broker, insured, or internal review)

Output requirements:

• Structured report with sections: Executive Summary, Key Metrics, Trend Analysis, Large Losses, Insights and Recommendations
• Include data tables where appropriate for metrics and year-over-year comparisons
• Professional formatting appropriate for insurance
• Correct industry terminology (no generic business-speak)
• Ready to use in presentations or renewal submissions with minimal editing
• Saved to outputs/ if the user confirms

Example Output

[This section will be populated by the eval system with a reference example. For now, run the skill with sample input to see output quality.]