Marine and Geopolitical Risk

Definition

Marine and geopolitical risk modelling quantifies the financial consequences of conflict, state action, sanctions, blockade and security events for exposures at sea and in adjacent supply chains. It sits between political-science analysis and actuarial measurement, and the translation between the two is where most of the difficulty lies.

The distinguishing features are non-stationarity, clustering, sparse loss data and a strong dependence on qualitative context that resists direct statistical encoding.

Why it matters

• War-risk rates can move by an order of magnitude within days of an event. Underwriters need a defensible basis for repricing, not only market sentiment.
• A single transit corridor can concentrate exposure across an entire portfolio simultaneously.
• Boards asked to approve appetite for conflict-exposed business need loss potential expressed in capital terms, not in narrative descriptions of tension.

Professional focus of Jonas Osman

• Structured translation from event data and open-source intelligence to frequency and severity parameters.
• Self-exciting and regime-switching processes as representations of clustering and abrupt state change.
• Accumulation monitoring across vessels, voyages, cargo and adjacent lines of business.
• Explicit, quantified treatment of model uncertainty where data is thin.

Key methodologies

• Event classification frameworks. Consistent taxonomy of incident type, severity, actor and location as the foundation for any frequency estimate.
• Hawkes and self-exciting processes. Representation of clustering where one event raises the short-term intensity of further events.
• Hidden Markov and regime-switching models. Latent risk states that shift the baseline rate, with transition probabilities estimated from observable indicators.
• Severity modelling from hull and cargo values. Loss distributions conditioned on incident type and exposure characteristics.
• Accumulation and scenario testing. Corridor-closure and multi-vessel scenarios run against the live portfolio.

Practical management applications

• Technical pricing inputs for war-risk and related covers, expressed as a range with stated uncertainty.
• Per-transit and per-corridor limits, and aggregate appetite by region.
• Early-warning indicators linked to defined underwriting actions.
• Capital assessment for conflict-exposed portfolios under adverse scenarios.

Governance and limitations

• Conflict data sources differ in coverage, definition and reporting lag; source choice materially affects estimated frequency.
• Historical frequency is a poor guide when the political regime has changed. Model output should always be read alongside current qualitative assessment.
• Sanctions and legal constraints can change insurability independently of physical risk.
• Model results support underwriting judgement and do not constitute a prediction of specific events.