UC43: Loss Run Analysis & Exposure Modelling

The Problem

Underwriters must reconcile loss runs from multiple prior carriers , sometimes 10+ different insurers have covered the same risk. Loss runs come in dozens of formats (PDF, Excel, proprietary). Manual reconciliation: identify overlapping coverage periods, extract loss data (date, amount, line type), consolidate frequency/severity data, identify trends, model exposure. Time per risk: 2-4 hours for complex commercial risks. Reviews often incomplete; loss drivers missed; exposure modeling incomplete. Kolena research: 60% more data consumed and 30% faster analysis with AI.

What the Agent Does

Ingests loss run PDFs from multiple carriers; standardizes formats via OCR and templating
Identifies loss frequency/severity/drivers: "Commercial General Liability: 3 losses in past 5 years, average severity $125K; most losses due to property damage (60%) and bodily injury (40%)"
Consolidates safety protocols and risk control measures from loss run narratives
Identifies non-obvious exposure patterns: "Loss frequency spiking in Q3 (seasonal pattern); severe losses concentrated among specific job sites/locations"
Models exposure: Projects future claims frequency based on historical pattern, identifies high-risk scenarios (flood exposure, worker safety gaps, equipment obsolescence)
Shortens review from weeks to minutes; improves accuracy of exposure assessment

Data Requirements

Data Sources:

Loss Run PDFs (from multiple carriers; manual upload or sourced via broker) , Historical loss data, line items, claim details
Carrier Loss Run Standards (ACORD formats, industry templates) , Standardized loss run schema
Safety Manuals & Risk Control Documents (submitted with loss runs or sourced) , Loss prevention protocols, risk control measures
Claim Detail Records (internal if prior insured) , Full claim narratives, investigation findings, causal factors
Industry Benchmarks (NCCI, ISO, Loss Prediction Models) , Industry-standard loss frequencies/severities by exposure type
Actuarial Tables (internal or third-party: Towers Watson, Milliman) , Loss development factors, loss tail factors
Exposure Data (payroll, square footage, vehicle count, etc.) , Risk exposure metrics
Risk Rating Tables (internal) , How to assess risk based on loss history and exposure

Data Classification:

Public: Industry benchmarks (NCCI, ISO), actuarial tables
Internal Structured: Loss run standards, risk rating tables, exposure data
Internal Unstructured: Loss run PDFs, carrier correspondence
External Third-Party: Broker-sourced loss runs (confidential), industry benchmarks
Sensitive/Regulated: Loss details (insurance confidentiality), claim narratives (sensitive details)

Data Quality Requirements:

Loss run data accuracy: 100% (financial data must be exact). OCR accuracy for PDFs: 95%+. Industry benchmark currency: updated quarterly or annually. Exposure data accuracy: 95%+ (payroll, square footage, vehicle count must be reasonably accurate).

Integration Complexity: Medium-High , Requires PDF extraction/OCR (ABBYY, Tesseract), loss run standardization logic, NCCI/ISO benchmark data integration, actuarial table integration, exposure data integration. NLP for extracting claim causal factors from narratives adds complexity. Loss trend modeling and projection requires statistical/actuarial expertise.

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	4	0.60
Error Reduction	10%	4	0.40
Cost Avoidance	10%	3	0.30
Strategic Leverage	5%	4	0.20
Data Availability	15%	4	0.60
Process Clarity	15%	4	0.60
Ease of Implementation	10%	3	0.30
Fallback Available	10%	5	0.50
Audience (Int/Ext)	10%	5	0.50
Composite	100%		3.90

Why It Scores Well

Loss run analysis is routine and high-frequency (20-50+ loss runs/month for commercial UW team). Clear time savings (2-4 hours → 15-30 minutes per loss run). Accuracy improvement (more comprehensive analysis = better UW decisions = fewer unexpected losses). Data is available (loss run PDFs, benchmarks). Fallback is straightforward: underwriter manually analyzes. Internal audience. Clear ROI: improve UW decision quality, reduce unexpected losses. Regulatory benefit: demonstrates systematic loss review process.

Regulatory Alignment

Actuarial Standards (ASOPs): Loss analysis and modeling must follow actuarial standards; agent-assisted analysis supported by expert review
Fair Lending: Loss history analysis must not discriminate based on protected characteristics

Sprint Factory Fit

Sprint 0 (2 weeks) + 2 build sprints (4 weeks)

Sprint 0: Loss run format taxonomy, standardization templates, loss driver classification, exposure modeling framework, actuarial methodology

Build Sprints 1-2: PDF/OCR extraction, loss run standardization, loss frequency/severity calculation, claim causal factor extraction, industry benchmark integration, exposure modeling, loss projection, risk assessment template generation, underwriter review workflow

Comparable Implementations

Kolena: automated loss run review: AI-assisted loss run analysis and exposure modeling
Moody's: 60% more data consumed, 30% faster: Advanced analytics for data-driven underwriting

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