AI War-Risk Routing: Smarter Black Sea Shipping

A 350-mile detour doesn’t sound like a “small operational tweak.” In the Black Sea right now, it’s becoming a rational response to drone threats.

According to vessel-tracking analysis reported this week, some tankers hauling Russian crude from Novorossiysk to the Bosphorus are hugging the coasts of Georgia and Turkey instead of crossing the middle of the sea—adding roughly 70% to that leg of the journey. The goal is straightforward: reduce the odds of being targeted by Ukrainian sea drones.

This is exactly the kind of real-time, risk-based decision-making that supply chain teams and insurers talk about—then struggle to operationalize. Because “take the safer route” isn’t a strategy unless you can price it, insure it, and explain it to everyone who signs off. The practical answer is a tighter loop between routing, risk intelligence, and insurance—and that’s where AI can pull real weight.

Why tankers are rerouting: drones turned the Black Sea into a pricing problem

The key point: drone risk is now an operational cost driver, not just a security headline.

When ships change routes to avoid drone corridors, they’re doing more than protecting hull and crew. They’re trading time, fuel, schedule reliability, and contractual performance against a probability of loss. In shipping terms, this becomes a bundle of measurable variables:

• Longer distance (the reported detour adds about 350 miles)
• More time at sea (exposure hours increase even as threat exposure may decrease)
• Different risk profile (coastal proximity, traffic density, surveillance coverage)
• Potentially different insurance terms (war-risk, kidnap & ransom add-ons, premium adjustments, deductibles)

Here’s the uncomfortable truth: most companies still treat these as separate conversations.

• Ops teams decide the route.
• Treasury worries about cash tied up in transit.
• Legal checks sanctions and contracts.
• Insurance is asked to “confirm coverage” after decisions are already made.

In high-risk corridors, that sequencing is backward. Insurance constraints and war-risk pricing should shape routing options up front, not after the vessel is already underway.

The AIS problem: when “data” is also a tactic

A complicating detail from the reporting: there’s a growing pattern of ships potentially broadcasting false digital positions via AIS (Automatic Identification System). Satellite imagery can show a vessel several nautical miles away from its reported location.

For risk teams, that matters because:

• AI models are only as reliable as the inputs.
• Spoofing isn’t just noise; it’s behavior, and behavior is predictive.

If a vessel is routinely “off” its AIS track, that’s a signal insurers and shippers should treat like a credit score event: not definitive on its own, but strongly correlated with elevated risk (regulatory, sanctions, operational, or physical security).

The hidden cost of “safer routes”: risk moves, it doesn’t disappear

The key point: detours reduce one threat vector but introduce new ones, and AI is useful because it evaluates trade-offs consistently.

Coastal routing can lower exposure to drones operating farther offshore. But coastal routes bring their own hazards:

• Congestion risk: narrower lanes, fishing traffic, local ferries, port approaches
• Navigation complexity: more frequent course changes, hazards, and compliance zones
• Geopolitical sensitivity: proximity to territorial waters increases incident stakes
• Schedule ripple effects: missed slots at straits or terminals amplify demurrage and downstream delays

From a supply chain & procurement lens, the cost isn’t just fuel. It’s a stack of second-order costs:

• Demurrage and detention from late arrival
• Inventory carrying cost from longer transit time
• Price volatility exposure if crude delivery shifts into a new pricing window
• Contractual penalties if delivery terms are breached

This is why “avoid drones” becomes an underwriting problem. If you can’t quantify the trade-offs quickly, you’ll default to blunt rules (avoid region entirely; pay any premium; accept any delay). Blunt rules are expensive.

A practical way to frame it: risk-adjusted route planning

I’ve found the most productive internal conversations happen when teams stop asking “Which route is safest?” and start asking:

Which route has the lowest risk-adjusted total cost for the next 7–10 days?

That question forces a combined view of:

• probability of incident by corridor
• severity distribution (minor damage vs total loss)
• insurance premium and deductible impacts
• expected delay costs and demurrage
• knock-on impacts to downstream supply

AI helps because it can update that estimate every few hours as threat conditions change.

Where AI fits in war-risk insurance (and why underwriters should care)

The key point: AI improves war-risk underwriting by turning scattered signals into decision-grade probabilities.

War-risk insurance has always been data-hungry and time-sensitive. What’s changing is the volume and velocity of signals:

• AIS tracks and anomalies
• satellite imagery and vessel classification
• open-source intelligence on drone attacks and launches
• port call histories, ownership structures, flag changes
• weather and sea-state conditions (which affect drone operability)

An AI-driven underwriting workflow doesn’t replace underwriters. It changes the shape of their work:

1. Signal fusion: Combine AIS, satellite, and intelligence feeds into a single risk view.
2. Entity resolution: Link vessels to beneficial ownership patterns, managers, flags, and prior incidents.
3. Dynamic risk scoring: Update risk scores by route segment (not just “Black Sea = high”).
4. Pricing and terms guidance: Recommend premium ranges, exclusions, deductibles, and warranties.

In practice, the win is speed and consistency. Underwriters can justify decisions with traceable factors rather than “market feel,” and brokers can negotiate with clearer evidence.

What AI can flag that humans often miss

Humans are good at judgment. They’re worse at monitoring thousands of micro-signals.

AI can reliably surface patterns such as:

• AIS irregularities clustered near specific corridors
• route deviation behavior that correlates with later incidents
• rapid flag changes paired with opaque management structures
• unusual loitering outside territorial waters or near choke points

Those signals matter because the reported detours suggest an emerging equilibrium: vessels will keep adapting, and insurers will keep repricing. The firms that learn faster will pay less over time.

From risk to routing: an AI playbook for supply chain teams and brokers

The key point: the best risk mitigation is a closed loop between operations and insurance.

If you’re in procurement, logistics, insurance placement, or risk management, here’s a workable playbook you can implement without boiling the ocean.

1) Build a “route segment” risk model (not a region model)

Stop treating the Black Sea as a single rating bucket. Break routes into segments (e.g., departure-to-coastline, coastal transit, approach to straits) and assign each segment:

• incident likelihood score (rolling 7-day and 30-day)
• severity estimate (expected loss range)
• confidence level (how good the data is)

That confidence level is critical when AIS spoofing is suspected.

2) Put insurance variables into the routing tool

Most routing tools optimize for time and fuel. In high-risk corridors, you need to include:

• war-risk premium estimate by segment
• deductible assumptions
• exclusion triggers (e.g., declared zones)
• expected claims handling friction (salvage availability, port access)

This is where AI can act like a translator between shipping reality and policy language.

3) Use scenario planning for “next incident” effects

Attacks drive pricing spikes fast. Build scenarios such as:

• Scenario A: No incidents for 10 days → premiums stabilize
• Scenario B: One drone strike on an empty tanker → premiums jump; more declared zones
• Scenario C: Strike with cargo or environmental spill risk → underwriting tightens; capacity shrinks

Your AI forecasting doesn’t have to be perfect. It just has to be fast enough to inform decisions before the market reprices.

4) Treat “data integrity” as a risk factor you can price

If AIS spoofing or inconsistent position reporting increases, it should affect:

• route selection (choose lanes with better monitoring)
• requirements (additional reporting, escorted corridors, geofencing)
• policy terms (warranties around tracking, communications, and adherence)

Data integrity isn’t just compliance. It’s underwriting.

What this means for insurers: pricing discipline beats panic

The key point: AI supports pricing discipline when the market gets emotional.

When incidents stack up, war-risk pricing can swing sharply. The temptation is to follow the market upward without a granular view. That’s how insurers lose good risks and keep bad ones.

A stronger stance is:

• price by route segment and behavior, not headlines
• use AI to continuously re-score exposure as routes change
• tighten terms where signals justify it (tracking, deviation, communication)
• preserve capacity for insureds who demonstrate measurable risk controls

This is also a customer experience issue. Better modeling enables clearer explanations:

“Your premium increased because your route includes two high-probability segments and your tracking integrity score dropped this week. If you shift to the coastal corridor and maintain verified position signals, we can offer a lower rate and a smaller deductible.”

That kind of transparency builds retention—especially when budgets are being set for 2026 and risk leaders need defensible narratives.

People also ask: practical questions teams are dealing with right now

Does hugging the coast always reduce drone risk?

No. It can reduce exposure to certain offshore threats, but it may increase congestion, navigation, and geopolitical incident risk. The right answer is route-segment scoring updated daily.

How do insurers validate vessel location if AIS can be spoofed?

They use multiple inputs: satellite imagery, radar, historical track patterns, and anomaly detection. AI helps by correlating discrepancies across time and across vessels.

Can AI reduce war-risk premiums?

AI doesn’t “reduce” premiums by itself. It helps carriers and brokers price more accurately, which rewards demonstrably safer routing, better data integrity, and stronger operational controls.

What to do next: make routing and insurance a single decision

The reported Black Sea detours are a signal: the operating environment is changing faster than traditional insurance workflows were designed for. If your routing decisions don’t include insurance variables, you’re almost certainly paying for risk twice—once in premiums and again in delays and disruption.

If you’re building an AI-driven supply chain risk program, start here: combine route planning, real-time threat intelligence, and underwriting inputs into one dashboard your ops team and broker can both use. That’s the difference between reacting to each incident and managing exposure as a system.

The question to carry into your next planning meeting is simple: If the threat picture changes overnight, can your organization re-route and reprice in hours—or does it take days?