AI Risk Models for War-Risk Shipping and Insurance

A 350-mile detour doesn’t sound like much until you’re operating an oil tanker on a tight schedule, burning extra fuel, and paying for every hour of exposure in a conflict zone. Yet that’s exactly what’s happening in the Black Sea: some tankers carrying Russian crude have started hugging the coastlines of Georgia and Turkey instead of crossing open water, aiming to reduce the chance of being targeted by Ukrainian sea drones.

From an insurance perspective, this isn’t just “a route change.” It’s a real-time signal that risk has shifted, that operators are adapting, and that underwriting assumptions made even a month ago may already be stale. And from an AI in Payments & Fintech Infrastructure perspective, it’s a clean case study of how AI can fuse messy, high-velocity data into decisions that affect pricing, claims, and capital.

What the tanker detours are really telling insurers

The simplest read is also the most important: operators are pricing war risk into their operations before insurers can fully re-price it. When tankers add roughly 70% to a key leg of the journey (Novorossiysk to the Turkish straits), they’re effectively paying an “operational premium” to reduce attack probability.

That matters because insurance doesn’t exist in a vacuum. War-risk shipping decisions ripple into:

• Marine war risk insurance pricing and capacity
• Hull & machinery exposure (more time at sea, more navigational complexity)
• P&I exposure (collision risk rises near coasts and chokepoints)
• Cargo exposure (delay, spoilage for some commodities, contract disputes)
• Claims severity and accumulation risk (multiple vessels converging on the same safer corridors)

Here’s the uncomfortable truth: when everyone crowds into the same “safer” route, that route becomes its own risk cluster.

Coastal routing reduces one risk—and increases others

Hugging the Turkish coast can reduce open-water exposure to sea drones, but it can also create a new stack of issues that should show up in underwriting and risk engineering:

• Traffic density: more near-misses, more collisions, more groundings
• Human factors: longer voyages, fatigue, watchkeeping errors
• Operational friction: more coordination with local authorities and straits traffic systems
• Compliance scrutiny: documentation, port-state controls, and beneficial ownership questions

A vessel like the Jumbo (flagged Sierra Leone, insurer and beneficial owner unclear in public reporting) also highlights another reality: counterparty opacity is becoming a core risk variable, not a compliance footnote.

AIS spoofing, “ghost risks,” and why AI has to get practical

The article notes a key complication: ships may broadcast false digital positions, a practice that has become more common in sanctions-pressured trade. Satellite imagery can sometimes confirm a vessel’s true location, but insurers can’t rely on a human analyst manually reconciling discrepancies at scale.

This is where AI earns its keep—if it’s deployed in a way that underwriters and claims teams can actually use.

A usable definition: “Ghost risk” in marine underwriting

Ghost risk is exposure that exists physically but is misrepresented digitally. In marine war-risk shipping, ghost risk shows up when:

• AIS tracks are inconsistent with speed/heading physics
• reported positions diverge from satellite detections
• vessel identity attributes don’t match imagery (dimensions, deck layout)
• patterns suggest deliberate loitering, route masking, or identity swapping

AI models can flag these anomalies quickly, but the win isn’t just detection. The win is translating detection into an underwriting action.

Underwriting actions AI should trigger (not just dashboards)

If your AI system can’t recommend an action, it’s mostly a reporting tool. In this environment, high-signal triggers should connect to clear next steps:

1. Pricing adjustment: apply route-specific war-risk loadings based on verified track behavior
2. Warranties/conditions: require verified AIS integrity, defined exclusion zones, or convoy protocols
3. Security requirements: mandate onboard procedures for drone awareness and incident response
4. Capacity controls: limit aggregate exposure in a coastal corridor or chokepoint window
5. Claims readiness: pre-stage adjuster guidance and incident playbooks for drone-related events

That’s the operational bar: fewer “interesting alerts,” more “do this now.”

AI-driven risk prediction: what data actually matters

War-risk shipping is a harsh teacher: if a model needs perfect data to work, it won’t work. The best systems thrive on partial truth and still produce decisions you can defend.

A strong AI risk stack for marine war risk insurance typically pulls from five data layers:

1) Movement and route behavior

Answer first: route behavior is the fastest-changing risk signal in conflict zones.

Useful features include:

• coastal vs open-water routing
• speed variance and loitering
• chokepoint dwell time (approaches to straits)
• time-of-day patterns (night transits)
• proximity to prior incidents and threat corridors

2) Identity confidence scoring

Answer first: identity confidence is now a pricing input.

Models should score confidence based on:

• AIS consistency
• MMSI/IMO history anomalies
• flag changes, name changes, manager changes
• mismatch between declared vessel specs and observed characteristics

3) Threat intelligence and event ingestion

Answer first: war risk changes weekly; static maps don’t keep up.

Even without perfect attribution, models can ingest:

• reported attacks and near-misses
• geopolitical escalations affecting targeting behavior
• maritime advisories and straits controls
• satellite-detected damage events

4) Exposure clustering and accumulation

Answer first: “safer routes” can concentrate risk.

AI can identify when a “low-risk” corridor becomes overcrowded and raise accumulation alerts for:

• multiple insured vessels in the same grid cell/time window
• correlated losses from a single incident type (e.g., drone strike pattern)
• reinsurer aggregation thresholds

5) Financial and payments signals (where fintech infrastructure fits)

Answer first: in sanctions-pressured trade, payment patterns can be risk signals.

In the AI in Payments & Fintech Infrastructure lens, insurers and brokers can use AI to:

• detect suspicious payment routing and counterparties (premium financing, claims payments)
• flag unusual settlement velocity (rush payments before high-risk transits)
• screen entities with evolving risk profiles without freezing legitimate operations

This isn’t about turning insurers into banks. It’s about acknowledging that money movement is part of the risk map.

Route optimization isn’t just logistics—it’s underwriting strategy

If you’re underwriting marine and war risks, route optimization is no longer a ship operator’s problem. It’s a shared operating system: the operator chooses a route, the insurer prices it, the reinsurer aggregates it, and claims has to respond to what happens there.

AI-driven route optimization can connect those dots—if you define the objective function correctly.

What “optimal” means in a war-risk environment

The cheapest route isn’t the optimal route. The fastest route often isn’t either. Optimal routing in this context balances at least four variables:

• attack probability (threat exposure)
• incident severity (what happens if targeted)
• navigational risk (coastal congestion, grounding)
• insurance economics (war-risk premium, deductibles, terms)

A practical stance I’ve seen work: treat underwriting as a constraint, not an afterthought. If the policy terms require certain behaviors (AIS integrity, exclusion zones), route optimization should incorporate those constraints upfront.

A concrete example: underwriting a “coastal detour” endorsement

Instead of treating the detour as a one-off, insurers can productize it.

A specialized endorsement could:

• price separately for open-water crossing vs coastal routing
• require periodic position verification (satellite or third-party)
• apply different deductibles based on threat corridor proximity
• include “loss prevention credits” for verified compliance

AI is the engine that makes this scalable: it verifies behavior and keeps pricing aligned with reality.

How insurers can deploy AI without creating new liabilities

AI introduces its own risk: explainability, governance, and model drift in a fast-moving conflict environment. If you can’t explain a pricing decision to a broker, insured, or regulator, you’ll hesitate to use the model—and then it becomes shelfware.

A pragmatic governance approach for war-risk AI

Answer first: governance should be lighter than financial-model governance, but stricter than marketing analytics.

A workable middle path:

• Human-in-the-loop underwriting for adverse decisions (declines, major surcharges)
• Model cards that specify what the model can and can’t infer
• Drift monitoring tied to incident rates and routing behavior shifts
• Audit trails for data inputs used in a quote and binding decision

What to automate first (high ROI, low controversy)

If you’re starting in 2026 planning season, begin with automation that’s easy to justify:

1. AIS anomaly detection and identity confidence scoring
2. Exposure clustering alerts for accumulation management
3. Dynamic risk scoring to support mid-term adjustments and renewals
4. Claims triage rules for drone/war-related events (severity prediction)

Then expand into pricing optimization and product redesign.

Practical takeaways for underwriting, claims, and risk teams

These are the actions I’d want a marine war-risk team to implement based on what we’re seeing in the Black Sea.

For underwriters

• Stop treating route as a static descriptor. Require track-based verification for high-risk zones.
• Separate “unknown ownership/insurance” from ordinary risk. Opacity should carry a measurable load.
• Price congestion risk. Coastal routing isn’t free safety; it’s risk migration.

For claims leaders

• Build a drone incident playbook. Fast cause-of-loss classification reduces leakage.
• Pre-arrange evidence capture. Satellite snapshots, AIS logs, and onboard footage should be requested immediately.
• Model severity early. Total loss vs repairable damage decisions affect salvage economics quickly.

For finance and operations (the fintech connection)

• Instrument payment flows. Premium and claims payments can signal stress and counterparty risk.
• Automate entity resolution. Name/flag/manager changes shouldn’t break your screening logic.
• Design controls for speed. War-risk decisions often can’t wait for a weekly committee.

Memorable rule: In conflict zones, risk doesn’t disappear—it relocates to wherever everyone runs next.

What comes next: from detours to data-driven war-risk insurance

The Black Sea detours are a reminder that shipping adapts faster than insurance paperwork. That gap is where underpriced risk accumulates—and where AI is genuinely useful.

For teams building modern insurance operations, the AI in Payments & Fintech Infrastructure angle is straightforward: the same pattern-recognition systems used for transaction routing and fraud detection can also support risk pricing, identity confidence scoring, and real-time exposure management in marine war-risk shipping.

If you’re responsible for underwriting, portfolio management, or insurance operations, the question to ask going into 2026 isn’t “Should we use AI?” It’s narrower—and more urgent: Which decisions are you still making with last month’s risk picture?