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

Deploy This Use Case with the Sprint Factory

From zero to a governed, production agent in 6 weeks.

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Governance Risks to Consider

Before deploying this use case, review these agentic AI risks from the Corvair Risk Catalogue. Each is scored on the DAMAGE framework and mapped to regulatory expectations.