Catastrophe Risk Modelling in Emerging Markets

Catastrophe risk modelling has become the common language of the global reinsurance market, yet it was built largely around the data-rich markets where most of the world's insured catastrophe losses occur. For cedants in Africa and other emerging markets, the same tools can be used, but they need more care at every stage: the exposure data, the choice of vendor model, the way uncertainty is treated and the way results are translated into a reinsurance programme.

At Reinsurance Solutions we work with insurers across Mauritius, Southern and East Africa, Francophone West Africa and London, and we see the same pattern repeatedly. Cedants that treat modelling as a discipline, not a compliance exercise, get a better structure and a more defensible price. This article sets out how we think about cat modelling in Africa and what cedants can do to get more value from it.

Catastrophe risk modelling and the protection gap

The starting point is the scale of what is uninsured. Swiss Re Institute's sigma 1/2026 release, published on 19 March 2026, put global economic losses from natural catastrophes in 2025 at about USD 220 billion, of which roughly 49% was insured, the highest share on sigma records. The same release notes that in emerging economies, typically 80 to 90% of catastrophe losses are uninsured. In Africa the picture is starker still: Munich Re has noted that insurance penetration on the continent is typically well below 1%.

Recent loss data shows the pattern clearly. Munich Re reported, via coverage of its January 2026 analysis, that natural disasters caused roughly USD 3 billion of economic losses in Africa in 2025, with just over USD 500 million insured, or under 20% of the total. Cyclones, including the storms that struck Madagascar in January 2025 and Mozambique in March 2025, accounted for more than half of the continent's losses. With so little insured experience, the claims history that normally anchors a pricing view simply does not exist.

This is the first reason catastrophe models matter so much in emerging markets. Where insured loss history is thin, a well-built stochastic model is often the only credible way to estimate what a 1 in 100 or 1 in 200 year event could do to a portfolio.

Exposure data quality: where model error usually starts

When we review a new cedant submission, the exposure data is usually the first place we find material uncertainty. Some portfolios arrive with geocoded addresses, construction class, number of storeys and sums insured at risk level. Many more arrive aggregated to town, district or province, with occupancy recorded loosely and construction details missing. Verisk's Ashish Jain has made the same point about other emerging regions: the lack of availability and quality of exposure data remains a key challenge, and modellers fill the gaps by redistributing coarse data to finer resolution and estimating missing attributes such as construction type, building height and year built.

Those assumptions are necessary, but they are not free. A JBA study of flood losses in Queensland, published as a preprint and not yet peer reviewed, ran 210 input combinations and found that exposure and vulnerability assumptions dominated loss uncertainty, ahead of flood depth and climate scenario. The authors caution that the result should not be generalised beyond that test case, and we would echo that, but the direction is consistent with our experience: coarser exposure tends to push modelled losses up and widen them.

In practice, improving exposure data does not require a major systems project. The highest-value steps are usually these:

• Geocode at street or plot level wherever the policy system allows, and flag the records that cannot be geocoded.
• Capture construction class, occupancy, number of storeys and year of build, even approximately, for the largest risks first.
• Review sums insured against current replacement costs, particularly after periods of high inflation, as Verisk recommends.
• Record policy terms consistently, including deductibles, limits, sub-limits and any flood or named storm conditions.

Building and calibrating flood and cyclone models for Africa

A catastrophe model has three components: a hazard module that generates a catalogue of events, a vulnerability module that translates hazard intensity into damage, and a financial module that applies policy terms. Each is calibrated against historical data, and this is where emerging markets face a structural disadvantage. The World Bank and the Insurance Development Forum made this plain in their February 2023 report on flood risk modelling in data-scarce contexts: the main challenges centre on data availability and data quality, and validating a flood model requires detailed historical records of river discharge, water levels and precipitation, which are often short, incomplete or limited to a few locations.

That is why vendor model choice and local calibration matter. For African business we use specialist vendor models, including JBA Risk Management for flood and CoreLogic (now Cotality) for cyclone, alongside other providers for earthquake and hail. No single vendor model is right for every country, and each needs to be checked against what actually happens on the ground. Recent events provide useful benchmarks. The April 2022 floods in KwaZulu-Natal fell on saturated ground, and the South African Weather Service recorded record rainfall at no fewer than eight stations, with Margate registering 311 mm, more than double its previous 24-hour high. Cyclone Freddy in 2023 made landfall in Madagascar on 21 February and again in Mozambique on 11 March before moving into Malawi, with a combined death toll of 522 by 19 March according to Al Jazeera's reporting, and the World Meteorological Organization indicating it was likely the longest-lasting cyclone on record.

Events like these test whether a model's footprint, track behaviour and damage curves are plausible for the region. A model that performs well for coastal housing in one country may under-represent informal construction, riverine flooding behind urban drainage, or the multi-landfall behaviour that Freddy showed. We therefore compare modelled footprints against loss reports wherever possible.

Secondary uncertainty and climate-adjusted views

Every catastrophe model produces a mean loss for each event, and around that mean there is a distribution. This is secondary uncertainty: the spread of possible outcomes for a given event, caused by variation in damage within a portfolio, differences in building response and imperfect knowledge of the inputs. For layers that attach at higher return periods, secondary uncertainty can change the expected loss to the layer materially, and in data-poor markets it is wider than in mature ones. The World Bank report notes that uncertainty is likely to be greater in data-poor contexts.

The practical consequence is that we do not rely on a single point estimate. We look at ranges, test sensitivity to exposure assumptions and compare more than one view of risk where more than one is available.

Climate adds another layer. Swiss Re Institute's 2026 sigma analysis found that exposure growth explains more than 80% of the long-term rise in weather-related insured losses between 1970 and 2025, and that secondary perils such as floods, convective storms and wildfire made up a record 92% of insured natural catastrophe losses in 2025. Our reading is that for most African cedants, growth in exposure, urbanisation and the concentration of value on coasts and floodplains is the dominant driver today, with a warming climate raising the stakes on top. We therefore ask whether a model reflects current climate conditions or long-run history, and we stress test portfolios against more severe scenarios than the baseline.

From model output to reinsurance structure and price

Modelling earns its keep when it changes a decision. For a catastrophe excess of loss programme, the model outputs tell us where to set the retention, how much limit to buy and how far up the loss curve the programme should extend. They also inform the reinstatement terms and the aggregate protection for frequency perils such as flood.

Pricing discussions are much easier when the cedant brings clean data and a clear explanation of its modelling assumptions. Reinsurers loading for uncertainty will do so more heavily when they cannot see how the numbers were produced. In our experience, a well-documented submission with credible exposure data typically reduces the need for blanket loadings and can support better terms at renewal. We also combine catastrophe results with capital and profitability analysis, so the structure serves the cedant's wider objectives.

Parametric solutions sit alongside traditional reinsurance as a complement. The World Bank report gives parametric insurance greater emphasis precisely because of its advantages in data and capacity limited settings. Africa Risk Capacity's October 2025 payout of just over USD 5.4 million to Mozambique and the World Food Programme, triggered by the 2024/25 drought and Tropical Cyclone Chido, shows the speed that predefined triggers can offer, as reported by Artemis. The trade-off is basis risk, and the same report notes that very few sovereign parametric flood solutions have been established so far. Modelling is what allows basis risk to be measured, rather than guessed.

Practical takeaways for cedants

Catastrophe risk modelling in emerging markets rewards preparation. The cedants that benefit most are those that invest early in their data, ask questions of their model providers and bring a clear view of their risk appetite to the table. We would start here:

• Audit your exposure data before renewal, and prioritise geocoding and construction details on your largest and most hazard-exposed risks.
• Ask which vendor models are used and what local data sits behind them, for each peril and each country in your portfolio.
• Look at ranges, not just averages. Request sensitivity runs that show how results move when exposure assumptions change.
• Track your own events. Keep claims data, event footprints and photographs after every flood or storm, so that models can be tested against your experience.
• Consider parametric cover as a complement to traditional programmes where speed of payment or limited loss data are the main constraints.
• Share your modelling openly with reinsurers so uncertainty is explained rather than loaded.

If you would like to discuss how catastrophe modelling could strengthen your next renewal, our reinsurance analytics team would be glad to help.