AI Workflow for Usage-Based Insurance (UBI) Premium Calculation

This workflow automates the monthly or quarterly recalculation of insurance premiums based on actual driving behavior, moving from static actuarial tables to dynamic, behavior-based pricing. It eliminates the manual aggregation of telematics data and the error-prone mapping to policy records, creating a direct operational lever for improving loss ratios and customer retention through fairer pricing. The business case centers on aligning risk with revenue more precisely, reducing adverse selection, and creating a competitive product differentiator in personal lines insurance.

Implementation requires a secure pipeline from telematics providers like Octo or Verizon Connect to a central orchestrator, such as a LangGraph workflow, that first resolves the vehicle ID to a unified customer profile. This identity resolution is critical; the workflow must stitch signals from the telematics platform, the policy administration system (e.g., Guidewire, Duck Creek), and the CRM. The actuarial calculation logic, often containerized for governance, runs within controlled parameters. Exceptions, such as extreme mileage deviations or data quality issues, are routed to human underwriters via a dedicated dashboard, ensuring auditability and control before any premium change is committed to the billing system.

A custom UBI workflow automates the collection of driving behavior from connected vehicles, links it to a policyholder's resolved identity, and executes actuarial logic to calculate monthly premiums. This eliminates manual data reconciliation, reduces rating cycle time from weeks to hours, and enables precise, behavior-based pricing that improves risk selection and customer retention. The operational upside comes from lower administrative cost, reduced leakage from outdated risk profiles, and the ability to scale personalized policies.

Implementation requires integrating with telematics providers (OEM APIs, aftermarket dongles), the core policy administration system, and the customer identity graph. The orchestrator manages data validation, exception routing for anomalous trips, and audit trails for regulatory compliance. Critical controls include actuarial review gates for significant premium changes, data quality checks on ingested signals, and rollback logic for calculation errors before PAS updates. Deployment is typically phased, starting with a pilot cohort to validate logic and system load.

Phase one focuses on building a secure, auditable data ingestion and identity-resolution layer. This initial step automates the collection of raw telematics data from connected vehicles and third-party providers (like Cambridge Mobile Telematics or Octo), then deterministically links it to the correct policyholder in the core Policy Administration System (e.g., Guidewire, Duck Creek). The immediate business value is the elimination of manual data reconciliation, creating a clean, trusted foundation for all downstream calculations while establishing governance over data quality and consent.

Subsequent phases introduce the actuarial rule engine for batch premium simulations, followed by a real-time calculation API for dynamic mid-term adjustments. The final phase integrates a customer self-service portal and full orchestration via LangGraph, linking calculation outputs to billing systems and communication channels. This staged approach allows for controlled validation of calculation logic, exception routing for edge cases, and iterative refinement of risk models, ensuring the system improves loss ratios without introducing regulatory or operational instability.

A UBI workflow automates the ingestion of telematics data, applies actuarial models, and outputs dynamic premiums, but its core value is in controlled, auditable execution. Savings come from eliminating manual data aggregation and rating, while upside emerges from more accurate, behavior-reflective pricing that improves risk selection. Implementation requires a pipeline from connected car platforms (like Otonomo or Smartcar) to the policy admin system (Guidewire, Duck Creek), with calculation logic hosted in a governed rules engine. The architecture must enforce data quality checks, handle missing data gracefully, and route exceptions for manual review.

Phased rollout mitigates risk. Start with a shadow mode: calculate premiums in parallel without applying them, validating model output against existing ratings. Next, implement a pilot for a low-risk cohort, using human-in-the-loop approval for all changes. Final production deployment automates updates for standard cases, with governance controls like premium change ceilings triggering actuarial review. Monitoring must track calculation drift, exception rates, and integration health with tools like Datadog or Splunk. This staged approach ensures financial and regulatory safety while progressively realizing operational leverage.