Inside Climate X’s New Global Wildfire Analytics Platform
Climate X, the climate resilience analytics firm, has released Global Wildfire, a probabilistic model designed to fill a gap that many insurers and asset managers have struggled with: consistent, detailed wildfire risk data that works across borders. The tool evaluates exposure down to the individual asset level, using a 30-metre resolution grid and local landscape, building, and ignition-risk factors.
Wildfire losses are climbing fast. Swiss Re recently estimated the global bill could hit $40 billion in 2025, yet most existing models have been built around high-risk regions such as the United States or Australia and offer only relative risk scores. Climate X says its new platform generates absolute burn probabilities and quantifiable financial impacts, covering 93% of global GDP across eight regions with the same methodology.
The model is built on machine learning trained with satellite data to assess local fire susceptibility, including terrain, vegetation, and the wildland-urban interface. Ignition probabilities are then fed into physical fire-spread simulations that run thousands of possible events per location, producing future risk pathways out to 2100 under various climate scenarios.
“For the first time, institutions have defensible, globally consistent, location-level analytics that translate hazard into potential loss,” said Lukky Ahmed, Climate X’s co-founder and CEO. The company positions Global Wildfire as a step beyond index-driven or purely historical assessments, targeting underwriting, credit risk, stress testing, and regulatory scenario analysis.
What the Global Wildfire Model Means for Risk and Capital
Where This Leaves Insurers and Reinsurers
The launch matters most for the (re)insurance industry, where wildfire has become one of the hardest perils to price. By delivering absolute burn probabilities rather than qualitative rankings, Climate X’s model could reshape how insurers set technical premiums, allocate reinsurance capacity, and manage accumulation risk. A property insurer with scattered exposures in France, Chile, and Oregon can now compare wildfire peril on a consistent basis, moving away from the regional silos that have dominated the market.
Competing with the Established Modelling Giants
Incumbent catastrophe modellers like RMS, AIR, and CoreLogic have long invested in wildfire research, but their products have often started with U.S. or Australian footprints. Climate X is betting that a globally native model, built on satellite-derived ML and cellular-automata fire spread, will attract firms managing diversified international portfolios. The risk for Climate X is execution: the model’s credibility will be tested during the upcoming fire seasons, and any notable divergence from actual losses could slow adoption.
Regulatory and Capital Implications
Regulators in Europe and parts of Asia are increasingly insisting that insurers quantify climate-related risk in their solvency and scenario analysis. A model that projects wildfire risk under different IPCC pathways to 2100 gives firms a defendable way to meet those expectations. It may also feed into internal capital models, potentially lowering capital charges if the analytics are accepted by supervisors—or raising them if risks prove greater than current assumptions.
How Insurers and Asset Managers Can Act on the New Intelligence
- Underwriting and pricing: Insurers can integrate the model’s absolute burn probabilities directly into technical pricing tools, especially for commercial property portfolios with assets outside the U.S. and Australia.
- Reinsurance purchasing: Reinsurance buyers can use the global coverage to identify and manage wildfire accumulation risk in regions previously modelled with inconsistent or index-based approaches.
- Regulatory submissions: Asset managers and insurers should evaluate whether the forward-looking scenarios to 2100 align with upcoming climate stress-test requirements in jurisdictions such as the EU and UK.
- Investment reviews: Since the model translates hazard into potential financial loss, investment teams can use it to screen real asset and infrastructure holdings for wildfire exposure, particularly in portfolios where property-level data has been thin.
Risk & Opportunity Assessment
| Commercial Risk | Medium | Insurers that fail to adopt high-resolution wildfire models risk underpricing policies in emerging exposure zones, potentially leading to significant losses if the $40bn loss trend persists. |
| Competitive Risk | Medium | Climate X faces competition from established modellers; the platform must prove its accuracy in live fire seasons to gain market share against incumbents with long track records. |
| Regulatory Risk | Medium | Regulators may make forward-looking climate risk quantification mandatory; firms using less granular approaches could face higher capital requirements or compliance pushback. |
| Reputation Risk | Medium | If the model's absolute probabilities diverge markedly from actual loss experience, Climate X and early-adopting clients could face credibility challenges with investors and rating agencies. |
| Technology Disruption | High | The combination of satellite-data-trained machine learning and cellular-automata fire-spread modelling represents a meaningful shift from index-based or historical methods, potentially disrupting how wildfire risk is priced and managed. |
| Commercial Opportunity | High | The model’s global scope and asset-level granularity open a large addressable market among insurers, reinsurers, and asset managers seeking a single, consistent wildfire analytics framework for multi-region portfolios. |
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