06 Catastrophic risk
Insurance for Fish Farms
Salmon & Tilapia.
- Client
- Aquaculture insurer / insurtech
- Location
- Canada, Norway, Scotland, Chile, Brazil
- Type of Risk
- Fish stock mortality — climate, disease, pollution
- Product
- Aquaculture mortality insurance
- Species
- Atlantic Salmon, Tilapia
Problem: Insurance penetration in aquaculture is low, and where cover exists the rates don't reflect actual loss experience — lenders price in a risk premium that constrains industry growth, because they can see what insurers are ignoring.
Solution: A mortality risk model calibrated separately for each country and species combination, using Monte Carlo simulation to generate full loss distributions for actuarial pricing, deductible structuring, and reinsurance programme design, with climate-adjusted projections built in explicitly.
Impact: Delivered actuarially defensible pricing across five countries for the first time on a common methodological basis, and quantified the extent of underpricing in several geographies for the first time.
01The challenge
The aquaculture insurance market is structurally underpriced, and the tools used elsewhere in insurance do not translate to this risk profile.
- The industry is underinsured and premiums are underpriced. Insurance penetration in aquaculture is low, and where cover exists the rates don't reflect actual loss experience. Lenders price in a risk premium that constrains industry growth — because they can see what insurers are ignoring.
- The risk drivers are broad and getting worse. Mortality comes from climate events, disease, industrial pollution, and operational failures. A 2021 heatwave and storm in British Columbia — both linked to climate change — caused major salmon losses. Existing models anchored to historical data are already out of date.
- Standard mortality tables don't work here. Loss dynamics in fish farming — with clustering of events within a production year, correlation with environmental triggers, and species-specific behaviour — are fundamentally different from livestock or life insurance. Off-the-shelf actuarial tools don't apply.
02The solution
Monthly mortality rates were modelled as a function of climate signals, disease incidence patterns, and operational risk factors — calibrated separately for each country and species combination. Monte Carlo simulation then generated a large number of synthetic annual loss trajectories, producing the full loss distribution needed for actuarial pricing, deductible structuring, and reinsurance programme design.
- Modelled monthly mortality rates as a function of climate signals, disease incidence patterns, and operational risk factors.
- Calibrated the model separately for each country and species combination, preserving country-specific characteristics while enabling comparable risk metrics across geographies.
- Ran Monte Carlo simulation to generate a large number of synthetic annual loss trajectories, producing the full loss distribution needed for actuarial pricing, deductible structuring, and reinsurance programme design.
- Built in climate-adjusted loss projections explicitly, so the pricing does not rely solely on historical data that may no longer represent the forward risk.
03Deliverables
The engagement delivered actuarially defensible pricing across five countries for the first time on a common methodological basis.
- Actuarial pricing: Defensible premium rates for salmon and tilapia mortality insurance across Canada, Norway, Scotland, Chile, and Brazil — for the first time on a common methodological basis.
- Underpricing quantification: Identified that existing market premiums in several geographies were materially below the technically adequate rate, quantifying the extent of underpricing for the first time.
- Reinsurance-ready output: A Monte Carlo framework enabling cedants to structure reinsurance programmes with confidence in the underlying loss model.
- Climate-adjusted projections: Forward-looking loss projections giving insurers a credible basis for rate adequacy reviews as the physical risk environment changes, reducing the risk of adverse development on long-term books.
Illustration: seasonal block resampling
Fig. 01
Fig. 02
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