UC46: Reinsurance Treaty Analysis & Placement Support

The Problem

Reinsurance treaty analysis is complex: extracting terms from dense contracts, identifying coverage gaps, modeling loss scenarios to assess treaty performance. Treaty placement requires analyzing market pricing, comparing carrier appetite, negotiating terms. Current process: 40-60 hours per treaty (reading contract, extracting key terms, modeling loss impacts, drafting placement summary). Treaty placement teams work with 5-20+ contracts/year. Analysis quality varies; some treaties lack comprehensive gap assessment. Misunderstood treaty terms create disputes at claims time.

What the Agent Does

Extracts treaty terms from complex reinsurance contracts: coverage limits, deductibles, attachment points, reinstatement provisions, premium rate, conditions
Identifies gaps and overlaps in coverage: gaps between treaty limit and potential loss scenarios; overlaps with other treaties (double recovery risk)
Simulates loss patterns (historical losses, catastrophe scenarios) against treaty; models net retention impact
Compares current treaty pricing to market pricing benchmarks; identifies pricing optimization opportunities
Drafts placement summary for placement team: treaty performance summary, market terms, recommended negotiation strategy
Reduces treaty analysis time from 40-60 hours to 5-10 hours; improves gap identification

Data Requirements

Data Sources:

Reinsurance Contracts (PDF; manual upload or document management system) , Treaty terms, conditions, definitions
Premium Receipts/Invoices (Reinsurance system: Endurance, Endprise, internal) , Premium amounts, receipt schedules
Historical Loss Data (claims triangles, catastrophe losses) , Claims experience under treaties, loss payments
Catastrophe Models (AIR, RMS, Eqecat) , Potential loss scenarios, catastrophe exposure, loss projections
Market Pricing Data (reinsurance brokers: Aon, Willis, Marsh; market publications) , Market pricing for similar treaties, market appetite
Treaty Comparison Database (internal) , Prior treaties, performance metrics
Reinsurer Appetite Database (internal or sourced) , Reinsurer appetite by line, capacity, pricing preferences
Financial Modeling Tools (Excel, Prophet, proprietary) , Scenario analysis, pricing models

Data Classification:

Public: Market pricing (if published)
Internal Structured: Reinsurance contracts (confidential), premium data, loss data, treaty database
Internal Unstructured: Broker correspondence, negotiation notes
External Third-Party: Broker intelligence (Aon, Willis, Marsh; confidential), catastrophe model outputs
Sensitive/Regulated: Reinsurance contracts (proprietary), pricing (confidential), negotiation strategy

Data Quality Requirements:

Treaty extraction accuracy: 99%+ (contract terms must be exact). Loss data accuracy: 100% (financial reporting depends on accuracy). Catastrophe model outputs: 95%+ reliability (models carry inherent uncertainty). Market pricing accuracy: ±10% variance acceptable (market pricing fluctuates).

Integration Complexity: High , Requires PDF extraction for contract parsing (ABBYY, Tesseract; may need manual review for complex terms), catastrophe model integration, loss data aggregation, market pricing database integration, financial modeling capability (integration with Excel VBA or Python/numpy), treaty comparison database access. Reinsurance domain expertise required for gap identification and placement strategy.

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	3	0.45
Error Reduction	10%	3	0.30
Cost Avoidance	10%	3	0.30
Strategic Leverage	5%	4	0.20
Data Availability	15%	3	0.45
Process Clarity	15%	2	0.30
Ease of Implementation	10%	2	0.20
Fallback Available	10%	5	0.50
Audience (Int/Ext)	10%	5	0.50
Composite	100%		3.45

Why It Scores Well

Treaty analysis is routine (5-20+ treaties/year). Clear time savings (40-60 hours → 5-10 hours per treaty). Risk reduction: better gap identification prevents claim disputes. Data is available (contracts + loss data + market pricing). Fallback is straightforward: placement team manually analyzes. Internal audience. Clear ROI: faster placements, better treaty terms, reduced claim disputes. Strategic benefit: improved capital efficiency (optimized net retention).

Score is lower (3.45) due to: (1) High domain expertise required (reinsurance terms are complex; agent recommendations require expert validation); (2) Contract complexity (some contracts use non-standard language; OCR/extraction may fail); (3) Market data availability (reinsurance market data is fragmented; pricing varies by broker).

Regulatory Alignment

Solvency II (Capital Adequacy): Treaty analysis supports capital requirement calculations; agent-assisted analysis must be validated by CFO/actuaries
Reinsurance Accounting (ASC 944, IFRS 17): Treaty terms analysis supports financial statement classification and accounting treatment

Sprint Factory Fit

Sprint 0 (2 weeks) + 3 build sprints (6 weeks)

Sprint 0: Reinsurance contract structure analysis, key term taxonomy, gap analysis framework, loss modeling strategy, market pricing framework

Build Sprints 1-3: PDF extraction for contract parsing (ABBYY or manual review process for complex contracts), key term extraction, coverage mapping, gap identification logic, catastrophe model integration, loss scenario simulation, net retention calculation, market pricing database integration, placement summary generation, broker communication workflow

Comparable Implementations

Earnix: agentic AI insurance use cases: Reinsurance analytics and placement support
Roots AI: insurance AI predictions 2026: Treaty analysis and capital optimization

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