AI Workflow for Real-Time Premium Adjustment in Shipping & Cargo Insurance

This foundational component ingests and normalizes high-frequency, multi-format data from external APIs and feeds. It handles AIS for vessel location and speed, weather services for storm paths and sea states, geopolitical risk indices, and port authority APIs for congestion and labor strike alerts. The pipeline must validate, timestamp, and geocode each signal, creating a unified event stream for the risk-scoring agents. Data quality checks and anomaly detection are built-in to prevent garbage-in, garbage-out pricing decisions.

Orchestrated agents (e.g., using LangGraph) specialize in different peril domains. A Geospatial Agent correlates vessel location with storm polygons and high-risk piracy zones. A Vessel Integrity Agent assesses the ship's age, flag, and historical incident data. A Cargo Sensitivity Agent evaluates the commodity's perishability, value density, and theft appeal. Each agent outputs a sub-score and confidence level. An Orchestrator Agent synthesizes these into a composite, time-stamped risk score for the specific voyage leg, which is passed to the pricing engine.

The core logic applies actuarial models and business rules to the live risk score. It calculates a premium delta or a completely new rate, considering the base policy terms, accumulated exposure, and portfolio-level risk appetite. This engine must integrate with the legacy policy administration system (e.g., Guidewire, Duck Creek) or a modern rating engine via API. A Rule Arbiter component enforces guardrails—such as maximum increase per day or regulatory caps—and flags any proposed adjustment that breaches thresholds for human review.

Not all adjustments can be fully autonomous. The workflow requires configurable approval gates. For example, premium increases above 15% for a key client, or any adjustment triggered during an active claim, might be routed to an underwriter's workbench in Salesforce or ServiceNow. The system provides the underwriter with the complete rationale: the triggering signals, agent scores, and the proposed change. Their approve/deny/modify decision is fed back into the system, closing the loop and providing training data for refining automated thresholds.

For compliance and dispute resolution, every premium adjustment must have a complete, immutable audit trail. This layer logs the input signals, the version of each scoring agent and pricing model used, the calculated adjustment, any rule-based overrides, and the final approved premium. It also generates a plain-language explanation for clients and brokers, citing the specific factors (e.g., "Diverted around Tropical Storm X, adding 48 hours to voyage") that drove the change. This is critical for regulated insurance operations and maintaining trust.

Operational control requires real-time visibility. This component monitors the health of data pipelines (latency, error rates), agent performance (score drift, confidence metrics), and business outcomes. Dashboards track metrics like the percentage of voyages repriced, the average adjustment, and the correlation between risk scores and subsequent claims. Alerts are configured for system failures or for when key metrics deviate from expected ranges, enabling rapid intervention by the DevOps or actuarial teams.

The core real-time signal. Integration with AIS (Automatic Identification System) feeds provides live vessel location, speed, heading, and destination. This pipeline must handle high-frequency global data, map vessels to insured policies, and detect deviations from planned routes that indicate piracy risk, unscheduled port calls, or mechanical issues. Data quality checks are critical to avoid pricing on stale or erroneous positions.

Orchestrates calls to multiple specialized APIs to build a composite peril score. This includes:

• Weather routing services (wind speed, wave height, storm tracks)
• Geopolitical risk databases (war zone, piracy, port strike alerts)
• Port congestion and delay indices The workflow must normalize disparate data formats, apply geofencing logic to the vessel's current location and planned route, and weight each signal based on the cargo type (e.g., perishables are highly sensitive to delay).

The system of record for policy terms, limits, and current premium. The dynamic pricing engine acts as a sidecar to the legacy PAS (e.g., Guidewire, Duck Creek, SAP FS-RI). It must query for the base policy, apply real-time adjustments, and write back endorsed premiums or surcharges via approved APIs or integration middleware. This is often the most complex integration, requiring careful handling of versioning, audit trails, and fallback procedures if the PAS is unavailable.

Connects live signals to probabilistic financial models. The workflow doesn't just add a flat surcharge; it triggers on-demand execution of actuarial models or catastrophe simulation platforms (e.g., RMS, AIR) using the vessel's precise location as an input. The output is a probabilistic loss cost update, which is then fed into the pricing logic. This requires a robust, containerized model execution environment with version control.

The central nervous system built on frameworks like LangGraph or Temporal. This layer sequences the workflow: trigger on location update → fetch data → score risk → execute model → calculate premium delta → check against business rules (guardrails) → route for approval if required → update PAS. It manages state, handles retries, and enforces mandatory human-in-the-loop gates for adjustments beyond a predefined threshold (e.g., >15% increase).

Non-negotiable for regulated financial decisions. Every premium adjustment must generate an immutable explainability record: the vessel's location, the raw risk signals received, the model outputs, the applied rules, and the approving entity (system or human). This data must be logged to a dedicated, secure datastore (e.g., a data lake) for real-time monitoring (dashboard of portfolio exposure) and retrospective audit by regulators and internal compliance.