UC37: Subrogation & Recovery Optimisation

The Problem

Subrogation identifies third-party liability opportunities , chances to recover claim payments from responsible parties or their insurers. Current process: claims adjusters manually review closed/open claims for liability signals (police reports, witness statements), manually cross-reference legal liability frameworks (state comparative negligence rules, contractual liability, insurance policy subrogation clauses), manually identify and pursue recovery actions. High caseloads (50-100+ claims per adjuster) lead to missed opportunities and lapsed statutes of limitation. Subrogation leakage: $500K-$2M+/year per mid-size insurer.

What the Agent Does

Automatically scans closed/open claims database for third-party liability signals (police reports, dashcam footage, medical records indicating causation)
Maps claims to applicable legal frameworks by jurisdiction (comparative negligence, joint and several liability, contractual liability)
Pre-fills arbitration/litigation filings with claim details and liability evidence
Ranks recovery opportunities by Expected Value (probability of recovery × recovery amount)
Escalates high-value opportunities (>$100K expected value) to subrogation specialist for human oversight
Generates monthly subrogation pipeline report: opportunities by category, expected recovery, statute of limitation status
99% reduction in miscategorization; higher recovery yields (15-25% increase in recovery rate)

Data Requirements

Data Sources:

Claims Management System (Guidewire ClaimCenter, Majesco) , Claim records, status, settlement amounts, adjuster notes
Police Reports (state/local APIs, if available; manual upload) , Incident details, fault determination, liability assessment
Dashcam/Photo Evidence Database , Video/image evidence of incidents, causation
Claim Narratives (natural language in claims system) , Adjuster descriptions, circumstance details
Historical Recovery Rate Database (internal) , Prior subrogation outcomes, recovery rates by type/jurisdiction
Utility Outage Logs (for property claims) , Infrastructure failure context
Weather Data (historical) , Weather conditions at time of loss, impact on liability determination
Legal Framework Database (state-by-state liability law) , Comparative negligence rules, subrogation statutes, contractual liability frameworks
Third-Party Insurer Database (internal or sourced) , Responsible party insurance information

Data Classification:

Public: Police reports (typically public), weather data, legal frameworks
Internal Structured: Claims data, recovery rates, legal mapping rules
Internal Unstructured: Adjuster claim narratives, subrogation attorney notes
External Third-Party: Police reports (government), weather data (public), third-party insurer contacts
Sensitive/Regulated: Claim details (insurance confidentiality), medical records (HIPAA if applicable)

Data Quality Requirements:

Police report accuracy: 100% sourced from government agencies. Claim narrative completeness: 95%+ of claims have narrative descriptions. Historical recovery rate accuracy: tracked and validated against actual recovery outcomes. Legal framework accuracy: must match current state law (updated with regulatory changes).

Integration Complexity: Medium-High , Requires ClaimCenter/Majesco API integration, police report API integration (if available; may require manual upload), dashcam/photo database access, NLP for claim narrative analysis, legal framework database maintenance, historical recovery rate database (internal or sourced).

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	4	0.60
Error Reduction	10%	5	0.50
Cost Avoidance	10%	4	0.40
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%	3	0.30
Audience (Int/Ext)	10%	5	0.50
Composite	100%		3.85

Why It Scores Well

Claims data is readily available (internal). Recovery opportunities are high-value (average subrogation recovery: $5K-$50K per successful case). Automation is high-impact (most opportunities currently missed due to manual process limitations). Fallback is straightforward: adjuster manually identifies opportunities. Internal audience. Clear ROI: recover $500K-$2M+/year per insurer. Regulatory benefit: systematic subrogation process improves audit readiness and demonstrates financial control.

Regulatory Alignment

Arbitration/Litigation Rules: Agent pre-filled subrogation filings must comply with court rules; human attorney review required before filing
Fair Debt Collection Practices Act (FDCPA): If recovery involves third-party collection, FDCPA compliance required
Audit Trail: Subrogation decisions must be documented and auditable

Sprint Factory Fit

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

Sprint 0: Claims database schema analysis, liability signal taxonomy, legal framework mapping, recovery opportunity ranking logic, third-party contact database

Build Sprints 1-3: ClaimCenter API integration, police report ingestion, dashcam/photo database access, NLP for claim narrative analysis, liability mapping rules, recovery ranking algorithm, subrogation filing template generation, specialist escalation workflow, recovery outcome tracking and feedback loop

Comparable Implementations

Camelot Consulting: 40% claims automation, 99% accuracy: Claims automation including subrogation opportunity identification
Insurnest Subrogation Opportunity Finder: AI agent for subrogation opportunity discovery

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.