Auto Insurance: AI Agentic Workflow for Telematics-Integrated Accident Reconstruction

This workflow automates the most contentious and time-consuming part of auto claims: determining fault. By ingesting high-frequency telematics data (speed, g-force, braking) from the moments before impact via provider APIs like Cambridge Mobile Telematics or Octo, a physics-modeling agent reconstructs the collision sequence. This creates an objective, data-driven liability report that directly addresses the 'he said, she said' bottleneck, reducing manual investigation cycles from days to minutes and cutting dispute-related expenses.

Implementation requires integrating the telematics data pipeline with the claims core via an orchestrator like LangGraph. The physics agent applies kinematic models to the telematics payload, generating a confidence-scored fault assessment. Results with high confidence auto-populate the liability decision in the PAS; low-confidence or complex multi-vehicle scenarios are routed for human review with the agent's analysis appended. Controls include data validation against the policy's effective dates, immutable audit logging of the reconstruction logic, and governance gates before any settlement action is triggered based on the automated output.

This workflow automates the high-latency, subjective process of determining fault by converting raw telematics data—speed, braking force, g-force, and location from the moments before impact—into a physics-backed liability assessment. It eliminates manual data gathering and speculative reconstruction, directly reducing investigation cycle time by 40-60% and lowering loss adjustment expense. The system ingests data via provider APIs (e.g., Cambridge Mobile Telematics, Octo), validates event signatures, and triggers the reconstruction pipeline, ensuring only verified collision events are processed.

Implementation requires integrating the orchestration layer with the claims core system for policy context and routing the generated report. The physics agent applies kinematic equations to model vehicle dynamics, while the report agent synthesizes findings into a structured narrative with confidence scores. Governance is critical: all outputs route through a human review queue for high-severity or low-confidence cases before influencing settlement. The architecture must support rollback, explainability queries, and seamless data sync with existing FNOL and SIU workflows to be production-viable.

This workflow automates the manual, expert-dependent process of reconstructing accident sequences from fragmented evidence. By ingesting telematics data (speed, g-force, braking) via provider APIs, it applies physics-based modeling to generate a preliminary liability report. The operational upside is clear: reducing dispute resolution time from days to minutes, lowering loss adjustment expense by automating expert analysis, and creating a defensible, data-driven foundation for liability decisions that accelerates settlements for telematics-enabled policyholders.

Implementation follows a phased, risk-managed approach. Phase 1 builds the ingestion pipeline and physics model, validated against historical, known-outcome claims. Phase 2 integrates the orchestration layer (using LangGraph) with the claims core system, deploying initially as a decision-support tool for adjusters. Phase 3 enables straight-through processing for high-confidence, low-severity incidents, governed by strict confidence scoring and business rule thresholds. Each phase requires controls: model performance monitoring, bias detection on fault assessments, and immutable audit trails for all data inputs and algorithmic decisions to satisfy regulatory scrutiny.

The governance model for this workflow must enforce strict data lineage, decision traceability, and liability scoring logic. Controls are implemented at each stage: telematics data ingestion requires validation against policyholder consent and device ID; physics modeling agents operate within bounded parameter ranges; and all preliminary fault assessments are routed through a human-in-the-loop approval gate before influencing claim reserves or communications. This ensures the automated system augments, rather than replaces, final adjuster judgment, preserving auditability.

Phased rollout mitigates implementation risk. Phase 1 deploys the data ingestion and validation layer in monitoring mode, logging outputs without live integration. Phase 2 activates the physics and scoring agents for a pilot segment of low-complexity, clear-liability claims, with 100% human review. Phase 3, after validating accuracy and stability, enables conditional straight-through processing for high-confidence reconstructions, while maintaining mandatory review for disputes, high-severity incidents, or low-confidence scores. This measured approach builds operational trust and isolates failure domains.

The core operational bottleneck is manual, subjective fault determination, which drives disputes and extends cycle times. This workflow automates it by ingesting telematics data (g-force, speed, braking) from the moment of impact via provider APIs (e.g., Cambridge Mobile Telematics, Octo). A physics-modeling agent reconstructs the collision sequence, while a rules agent cross-references policy terms from the core admin system (e.g., Guidewire, Duck Creek). The output is a preliminary liability report that reduces adjuster investigation work by 40-60% and provides an auditable, data-backed foundation for settlement.

Implementation requires a robust integration layer handling real-time API calls, batch syncs for policy data, and immutable logging to the claims database. The physics agent's output must include confidence scores; low-confidence reconstructions are routed to a human review queue in the claims desktop. Governance controls mandate bias checks on model outputs and a complete audit trail linking telematics data to the liability recommendation. This creates a scalable, defensible system that plugs into existing adjuster workflows without a full platform replacement.