AI for Black Sea War-Risk Underwriting in Real Time

A 350-mile detour doesn’t sound like much—until you’re a tanker operator watching sea-drone strikes hit vessels on your route, and you’re paying marine war-risk premiums that can change faster than the weather.

That’s the reality now in the Black Sea. Recent vessel-tracking patterns show Russian crude tankers hugging the coasts of Georgia and Turkey on the way to the Bosphorus instead of taking the straight line across open water. The detour adds roughly 70% to the Novorossiysk-to-straits journey. It’s a tactical move meant to reduce exposure to Ukrainian sea drones—but it also creates a underwriting headache: risk is shifting by the hour, and the data is messy.

For insurers and brokers, this is a clean case study in why traditional marine underwriting (static questionnaires, periodic pricing reviews, manual monitoring) is falling behind. And for readers of our AI in Payments & Fintech Infrastructure series, it’s a reminder that the same AI stack used to route payments safely—real-time scoring, anomaly detection, entity resolution, and decision automation—maps surprisingly well to maritime war-risk insurance.

Why tanker route detours matter to insurers (beyond fuel and time)

Route changes are an insurance signal, not just an operational choice. When tankers add hundreds of miles to stay near coastlines, they’re effectively broadcasting a change in threat model: open-water exposure is being priced as more dangerous than proximity to territorial waters.

From an underwriting perspective, that shift touches multiple lines and clauses:

• Marine hull: higher probability of physical damage from hostile action, plus longer time at sea.
• War risk: the core issue—targeting risk, escalation risk, and aggregation risk in a constrained geography.
• P&I (Protection & Indemnity): collision risk, environmental liability, crew safety, and salvage.
• Cargo: delays, deviation clauses, and supply chain knock-on effects.

Here’s what most teams miss: the risk isn’t just the drone. It’s the second-order impact—changes in traffic density along the Turkish coast, congestion near straits, more opportunities for AIS spoofing, and more volatility in loss-adjusted pricing.

The AIS “truth problem” is now an underwriting problem

The article notes something underwriters increasingly deal with: vessels may provide false digital positions, and verification may require satellite imagery or corroborating signals.

When location data can be manipulated, insurers face two hard questions:

1. What is the vessel actually doing (route, speed, loitering, rendezvous)?
2. Is misreporting itself a risk indicator that should change pricing, coverage terms, or claims scrutiny?

A practical stance: AIS inconsistency should be treated like a fintech fraud signal. It doesn’t prove wrongdoing, but it raises the probability of adverse selection and operational risk.

The new war-risk underwriting reality: dynamic risk, dynamic pricing

Marine war-risk underwriting is moving from “rate cards” to “risk engines.” The Black Sea is a contained theater, but the variables are too numerous to manage with a weekly market email and a spreadsheet.

Consider what’s changing in days—not quarters:

• Attack frequency and tactics (empty vessels struck vs. laden vessels targeted)
• Likely target profiles (flags, ownership ties, trading patterns)
• Route adaptation (coastal hugging, timing changes, convoy-like behavior)
• Geopolitical signaling (threats of retaliation, expansion of eligible targets)

If you’re pricing war risk with a static view, you’re either:

• Overcharging good risks (losing business), or
• Undercharging bad risks (discovering your error at claims time).

A simple framework: Threat × Exposure × Behavior

One model that works in practice is to score risk as a function of:

• Threat: intensity of hostile capability in a corridor (drone range, recent incidents, escalation indicators)
• Exposure: time spent inside the corridor, proximity to coastline/ports/chokepoints, seasonality (night operations, winter visibility)
• Behavior: AIS integrity, route deviation, speed changes, loitering, fleet patterns, and ownership opacity

The detour described in the source changes Exposure (more miles, different lanes) and possibly Behavior (if it correlates with spoofing or “dark” periods). Underwriting needs to reflect that immediately, not at renewal.

Where AI actually fits: the same toolkit used in payments risk

AI in payments and fintech infrastructure is built for adversarial environments. Fraudsters probe systems; attackers adapt; signals are noisy; decisions must be fast. That is exactly what war-risk underwriting looks like now.

Below are AI applications that translate cleanly from payments to maritime insurance.

1) Real-time risk scoring (like transaction authorization)

In payments, a transaction gets scored in milliseconds using behavioral signals. In marine war risk, a voyage can be scored continuously using:

• AIS tracks (with quality scoring)
• Satellite detections and discrepancies
• Proximity to recent incident locations
• Traffic patterns (convoys, clustering, unusual rendezvous)
• Port calls and timing patterns

Output: a voyage risk score that can drive pricing, referral rules, or coverage conditions.

Practical underwriting use:

• Auto-approve low-risk transits under defined limits
• Flag high-risk voyages for senior referral
• Trigger mid-voyage endorsements or additional premium (where contractually appropriate)

2) Anomaly detection (the “AIS spoofing” problem)

If a ship’s reported position is miles away from a plausible track—or inconsistent with satellite imagery—that’s an anomaly. AI systems that detect card-present fraud patterns can also detect:

• Implausible jumps in location
• Identity mismatches (dimensions/paint/deck layout vs. expected vessel profile)
• Route patterns inconsistent with declared purpose

This matters because data integrity is part of risk. When you can’t trust the telemetry, you can’t trust the submission.

3) Entity resolution (beneficial owner “unknown” isn’t a dead end)

The article highlights a vessel whose insurer and beneficial owner are unknown, sailing under a flag of convenience.

Entity resolution is a bread-and-butter fintech infrastructure problem: connecting identities across messy datasets. In marine underwriting, AI-assisted entity resolution can connect:

• Vessel IMO numbers, managers, and fleet relationships
• Historical naming changes and reflagging events
• Repeated trading corridors and counterparties
• Claims history and inspection records

Insurers don’t need perfect certainty. They need enough confidence to price and to manage aggregation.

4) Decision automation that underwriters actually trust

AI doesn’t replace underwriters. It replaces the low-value steps that drain their time:

• chasing data inconsistencies
• manually reviewing tracks
• compiling incident summaries
• re-keying vessel details

The winning pattern I’ve seen is “automation with receipts”:

A risk engine is only as useful as its ability to show the signals that drove the score.

Underwriters will use AI when they can see why it’s worried.

What insurers should change now (a practical playbook)

You don’t need a moonshot platform to get value. You need a workflow that treats war risk like a live feed, not a PDF attachment.

Step 1: Build a minimum viable “voyage dossier”

Create a standardized, machine-readable dossier per voyage:

• Vessel identity packet (IMO, flag, manager, ownership confidence score)
• Last 30–90 days of track behavior and AIS integrity score
• Route corridor definition and time-in-zone estimate
• Incident proximity score (distance/time to recent attacks)
• Chokepoint exposure (Bosphorus approaches, loitering risk)

This is the underwriting equivalent of a payments transaction payload.

Step 2: Move from static pricing to trigger-based pricing

Instead of arguing over one flat rate, define triggers that adjust premiums or terms:

• Entering a defined high-risk corridor
• AIS goes dark for X minutes in-zone
• Detected deviation beyond Y miles from declared route
• Satellite mismatch events

This is similar to fintech routing rules that change when fraud pressure spikes.

Step 3: Create escalation tiers and referral logic

Not every voyage needs the same scrutiny. Create tiers:

1. Straight-through: low score, clean data, stable behavior
2. Underwriter review: moderate score or mild anomalies
3. Senior referral: high score, significant anomalies, ownership opacity
4. Decline / exclude / require warranties: extreme cases

This is how payments teams keep authorization fast without ignoring risk.

Step 4: Treat aggregation as a first-class problem

The Black Sea is geographically tight. That means aggregation risk can sneak up—multiple insured vessels in the same corridor, same time window, same threat level.

AI helps by continuously estimating:

• fleet exposure by hour
• corridor density
• correlated loss potential

That’s not academic. That’s capital management.

People also ask: the questions clients are asking right now

“If tankers hug the Turkish coast, is the risk actually lower?”

Lower in one dimension, higher in others. You may reduce exposure to certain drone tactics in open water, but you increase time at sea, traffic concentration, and chokepoint dependency. Pricing should reflect net risk, not one-factor improvements.

“Can we underwrite war risk without trusting AIS?”

Yes, but only with corroboration. Combine AIS with satellite, port intelligence, and behavioral baselines. Also, price the uncertainty: if telemetry is unreliable, the risk premium should rise.

“Will AI make war-risk pricing less volatile?”

It should make volatility more explainable and better targeted. The goal isn’t to pretend the Black Sea is stable; it’s to avoid blanket pricing that punishes good risks and subsidizes bad ones.

What this means for the broader AI-in-infrastructure story

Here’s the bridge back to our AI in Payments & Fintech Infrastructure series: modern financial infrastructure depends on real-time trust decisions—is this transaction safe, is this counterparty real, is this behavior normal, should we route or block?

Marine war-risk underwriting has become the same kind of problem, just with steel hulls instead of card numbers.

Insurers that build AI-powered risk engines—grounded in explainable signals, entity resolution, and anomaly detection—will quote faster, manage aggregation better, and defend pricing decisions when clients push back.

If you’re leading underwriting, pricing, or risk at a carrier, MGA, or broker, the immediate next step is simple: pick one corridor (like the Black Sea), define a voyage dossier, and operationalize a live risk score. You’ll learn more in 30 days than you will in a year of committee meetings.

Where do you want your team to be by spring renewal season: debating last month’s incidents, or pricing the next transit with real-time intelligence?