Automation Workflow for Commercial Auto Fleet Underwriting

Manual fleet underwriting requires weeks to collect, normalize, and analyze disparate data from brokers, telematics providers, and MVR services. A custom workflow automates this ingestion via APIs and browser agents, applies scoring models in parallel, and generates a structured risk profile. This compresses initial assessment from 2-3 weeks to under 4 hours, accelerating quote turnaround and improving win rates in competitive submissions.

Eliminating manual data entry, cross-referencing spreadsheets, and chasing external reports reduces the operational cost of underwriting a fleet policy by over 60%. The workflow's orchestration layer (e.g., LangGraph) coordinates specialized agents for data extraction, validation, and calculation, allowing senior underwriters to focus on complex risk evaluation and broker relationships instead of administrative tasks.

Automated workflows apply consistent, data-driven scoring across all submissions, reducing variability and subjective bias. By integrating real-time telematics for driving behavior and predictive models for aggregate loss potential, the system identifies high-risk fleets more reliably. This leads to sharper risk segmentation, more accurate pricing, and an estimated 3-5 point improvement in portfolio loss ratio over 24 months.

A manual underwriting team hits a hard capacity limit based on headcount. An automated, agentic system scales elastically with submission volume. The architecture handles concurrent risk evaluations, dynamically routing work based on complexity. This enables the underwriting department to grow written premium by 40%+ without a linear increase in staff, fundamentally improving operating leverage.

Manual processes are prone to transcription errors and missed data points from complex submission packets. The automated workflow validates all ingested data against source APIs (e.g., for VIN decoding, driver records), flags discrepancies for review, and maintains a complete audit trail. This reduces errors that lead to mispricing or coverage issues, lowering E&O exposure and improving the defensibility of underwriting decisions.

Traditional underwriting struggles to leverage live data from fleet telematics and management platforms (e.g., Samsara, Geotab). A custom workflow includes dedicated integration agents that maintain OAuth connections, fetch normalized behavioral data, and feed it directly into risk models. This creates a dynamic pricing advantage and allows for continuous policy optimization post-bind, moving from static annual reviews to responsive risk management.

This agent acts as the workflow's intake layer, automatically ingesting submission packets from broker portals, emails, or APIs. It uses layout-aware OCR and LLMs to decompose heterogeneous documents—vehicle lists in Excel, driver MVR PDFs, loss runs—into a normalized, structured risk profile. It validates submission completeness against a checklist and routes incomplete packets back to the broker, eliminating manual data entry and triage.

This agent orchestrates parallel API calls to external data vendors and fleet management systems (e.g., Samsara, Geotab). It fetches real-time telematics (hard braking, mileage), pulls motor vehicle records (MVRs), and retrieves claims history (CLUE reports). The agent reconciles this data with the application, flags discrepancies (e.g., unreported drivers), and enriches the risk profile with dynamic behavioral signals unavailable in static submissions.

The core decisioning agent applies actuarial models and underwriting rules to the enriched profile. It calculates an aggregate risk score by weighting vehicle age, driver safety records, geographic exposure, and loss history. Based on configurable thresholds, it assigns the fleet to a pricing tier (e.g., Preferred, Standard, Non-Standard). This agent generates the initial technical premium and surfaces key decision drivers, such as a sub-tier driven by a concentration of young drivers.

This control agent monitors for exceptions that breach pre-defined guardrails: ambiguous data, high-risk scores, referral triggers (e.g., hazardous materials transport), or model low-confidence flags. It packages the full case context—data, scores, rationale—and routes it to the appropriate human underwriter's queue in the PAS (e.g., Guidewire) or workbench. It ensures auditability and maintains underwriting quality while maximizing straight-through processing for standard risks.

Upon final approval (automated or manual), this execution agent generates the formal quote document, applies final rates and terms, and initiates the bind transaction. It orchestrates the write-back to the Policy Administration System (PAS), creates the policy in the system of record, and triggers downstream actions: issuing certificates of insurance (COIs), updating the document management system (DMS), and notifying the broker via portal or email—closing the loop without manual processing.

Running concurrently, this governance agent immutably logs every action: data points ingested, API calls made, model scores, rule triggers, and human decisions. It constructs a defensible, chronological audit trail for each submission, linking to source documents. This log supports regulatory examinations, model risk management (MRM) validation, and fair lending analysis, embedding essential governance directly into the automated workflow's architecture.