Marine Technical Pricing with GLMs and ML

By Jonas Osman Abdelghafour.

Ask a marine underwriter how a hull risk is priced and the honest answer in much of the market is still: last year's rate, adjusted for claims, market direction and negotiation. Rating factors exist — vessel type, age, tonnage, classification society, trading limits, deductible structure — but they are often applied as judgment overlays rather than as coefficients in a fitted model. The result is systematic cross-subsidy: well-run modern fleets overpay, and the worst risks in the portfolio are undercharged precisely because no model exists to say so.

The actuarial toolkit for fixing this is mature. Generalised linear models, the workhorse of personal-lines pricing since the 1990s, decompose expected loss cost into multiplicative factors estimated jointly from data — frequency modelled with Poisson-type structures, severity with gamma or log-normal, or pure premium via Tweedie. Applied to a marine book, a GLM turns anecdote into evidence: does vessel age actually drive machinery claims once tonnage and trade are controlled for? Is a particular flag a genuine risk signal or a proxy for operator quality? The model answers with confidence intervals rather than war stories.

The marine data problem — and how to work around it

The standard objection is data volume. A marine insurer may write a few thousand vessel-years and a few hundred claims annually — nothing like the millions of exposures behind a motor GLM. This is a real constraint but not a disqualifying one. The actuarial response is threefold: pool data across years and, where possible, across market facilities and industry databases; keep models parsimonious, with few, well-chosen factors and heavy use of credibility to shrink unstable estimates toward portfolio means; and split the problem, modelling attritional losses statistically while handling large losses through exposure curves and event loadings rather than asking a thin dataset to estimate the tail.

Cargo pricing illustrates the layered approach. Attritional cargo losses — theft, wetting, handling damage — are frequent enough to model against commodity class, packing, route, conveyance and season. Large-loss potential — a vessel casualty carrying accumulated containers, a warehouse fire at a transshipment hub — is better handled through accumulation modelling and explicit catastrophe loads. Stitching the layers together into a single technical price, with a defensible profit and capital margin, is precisely the kind of assembly work pricing actuaries do in every other specialty class.

Where machine learning genuinely helps

Gradient boosting machines (GBMs) and related ensemble methods consistently outperform GLMs on predictive accuracy in insurance applications, because they capture non-linearities and interactions automatically — the way age interacts with vessel type, or how deductible level interacts with operator quality. In marine, their most immediate value is not replacing the rating model but mining it for structure: fitting a GBM alongside a GLM, comparing lift, and using the machine-learned interactions to inform which terms belong in the transparent model that underwriters and regulators actually see. This GLM-plus-challenger pattern, now standard in sophisticated pricing teams, suits marine well because it preserves explainability where data is thin.

Machine learning also unlocks feature sets that classical rating never touched. AIS vessel-tracking histories yield behavioural variables: time spent in high-risk waters, port-call patterns, speed profiles, dark-activity gaps. Casualty and detention databases yield operator-quality signals: port state control deficiencies, class survey findings, management company track records. These are high-dimensional, messy inputs — exactly what tree-based ensembles digest well and hand-built rating tables cannot. An underwriter who knows a vessel spent forty days loitering near a sanctioned transshipment zone prices differently from one who knows only its age and tonnage.

The destination

The destination is not a black box that spits out a binding premium. It is a pricing architecture in which every marine risk receives a technical price built from fitted models, enriched by behavioural data, and clearly separated from the commercial price the underwriter chooses to charge — with the gap between the two measured, reported and managed. Markets that build this architecture will select against those that do not: the mispriced good risks will migrate to the modellers, and the mispriced bad risks will remain with everyone else. That is how technical pricing conquered motor insurance, and there is no structural reason marine will end differently.