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🛡️ P&C Insurance · Motor Claims · Full Lifecycle

Motor Claims Lifecycle Automated from FNOL to Subrogation, with 30–40% Less Fraud

Full Lifecycle Motor Claims — 80% Less Effort, 30–40% Fraud Reduction

Before · During · After — Risk DNA profiling, autonomous FNOL decisioning, subrogation recovery, and lifecycle intelligence orchestrated as one continuous loop.

Solution design · numbers modeled on the operation described

80%
Reduction in operational effort across full claim lifecycle
70%
Faster workflow execution from FNOL to settlement
30–40%
Fraud reduction through full lifecycle pattern detection
Claims, fraud, underwriting, and recovery operated independently — each with their own data, their own timeline, their own blind spots. We were running four disconnected operations instead of one intelligent lifecycle.

// the problem

Motor claims evidence — photos, police reports, repair estimates, medical records, telematics data — arrives across channels in unstructured formats with no unified view. Underwriting, claims, fraud, and recovery teams operate in silos, each blind to the others' data and timelines. Rising litigation costs and social inflation compress margins — reactive handling cannot anticipate these trends. Claimants interact across phone, email, app, and chat but context does not travel between channels, forcing repetition. Prevention, decisioning, and recovery are treated as three separate problems, missing the feedback loops that reduce future losses.

// what CLU does

CLU orchestrates motor claims as one continuous Before → During → After intelligence loop. Before: Risk DNA profiling from telematics and claims history powers dynamic pricing and prevention signals. During: FNOL intake across all channels, cognitive evidence reasoning, and confidence-gated auto-settlement with human escalation for edge cases. Real-time omnichannel communication maintains full context — transparent status, repair coordination, rental car orchestration. After: Automated subrogation identification, salvage optimization, and recovery tracking close the financial loop. Every claim feeds back into the Risk DNA — updating fraud patterns and prevention signals for the next cycle.

// the agents, in order

  1. 1
    Risk DNA Agent
    Profiles telematics, driving behavior, and claims history pre-claim — generates dynamic pricing signals and prevention alerts before FNOL ever happens
  2. 2
    FNOL Intake Agent
    Captures first notice of loss across all channels (phone, email, app, chat) — unified evidence profile created instantly, zero context loss between channels
  3. 3
    Evidence Reasoning Agent
    Processes photos, police reports, repair estimates, and medical records with contextual reasoning — detects anomalies and generates fraud risk signals
  4. 4
    Fraud Detection Agent
    Cross-references behavioral patterns across the full claim lifecycle — flags staged accident indicators, billing inflation, and organized ring patterns
  5. 5
    Settlement Agent
    Calculates payable amount with confidence scoring — auto-settles above threshold, routes to human adjuster with full context for complex or disputed cases
  6. 6
    Recovery Agent
    Identifies subrogation opportunities automatically, coordinates salvage optimization, and tracks recovery to close the financial loop on every claim

// systems it talks to

  • Telematics API
  • FNOL Multi-Channel Intake
  • Photo / Evidence Reasoning Engine
  • Fraud Pattern DB
  • Payment Gateway
  • Subrogation Tracker

// the economics

80% operational effort reduced · 70% faster FNOL-to-settlement · 30–40% fraud reduction · Risk DNA continuously updated

// CLU · the agent factory

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