Physical Climate Risk Analytics

This technology delivers physical climate risk analytics for any asset or portfolio. It combines climate hazard with consequence models, offering richer insights than typical climate risk screening tools. Outputs detail financial repercussions from damages, projected downtime, portfolio risk correlation, increased climate-induced risks, and various other actionable risk metrics. The technology has global coverage, uses high-resolution input data (30x30m), validated computations, and proper uncertainty quantification. Models integrate climate dynamics, providing these same risk metrics for future climate. Stochastic event simulations underpin all the models, which uniquely enables the computation of climate risk correlation across portfolios.

Outputs:

• Climate risk analysis results for any asset or portfolio of assets.
• API or dashboard for users to access the physical climate risk model.

Technical features:

• Multi-hazard climate risk analytics (coastal and riverine floods, hurricanes, heat, tornadoes and more).
• Global coverage at high-resolution.
• Projections for current and future climate scenarios.
• Compliant with data-standards used by the insurance sector.
• Based on millions of stochastics event simulations, which enables proper calculation of correlated portfolio losses, full probabilistic analysis, or extraction of specific simulations for stress-testing.
• Output in quantified and actionable metrics: expected losses for all recurrence levels, average annual loss, projected downtime, portfolio risk correlation, increased climate-induced risks and more.
• Underpinned by validated methods, high-resolution datasets, efficient computation, and leading scientific research.
• Proper uncertainty quantification and auditable results.

Ideal collaboration partners include consulting firms, ESG SaaS enterprises, auditing firms, financial data aggregators and insurance firms.

The product can be used for three main areas of application:

1. Structuring risk financing products: can be used to develop new insurance products, insurance products in new geographies, set triggers for parametric insurance and develop other risk transfer products.
2. Physical climate risk disclosures: support quantification and disclosure of climate risk, meet increasing regulatory requirements, promote ESG strategy, increase investor confidence in demonstrated climate-resilient entities, etc.
3. Climate risk management: support climate-informed investments, strategies for protecting at-risk assets, acquisitions of new assets, develop climate-resilient loans, test and improve supply-chain resilience, screening for physical or nature-based protection potential, and much more.

It is estimated that the global market for the use of this technology is approximately US$2.5 billion and growing, including customers in the Insurance, Investment & Risk Management, and Financial Disclosure industries. A recent acquisition of RMS – the leading global provider of climate and natural hazard risk modelling and analytics – by Moody’s Corporation for approximately US$2 billion is a clear signal of the growing interest and need for such services in the market.

The technology offers high quality and high-resolution climate risk information for the entire world, quantified in financial and operational metrics. A key value proposition is the ability to compute proper portfolio risk across the globe. This is possible due to the unique computational architecture: generating millions of stochastic extreme events, each of which propagates to complex hazard intensity footprints, which propagate further to impacts on physical exposures. These simulations can capture impacts to multiple assets by the same events, as well as risk correlation and clustering across portfolios. Tests have shown that alternative methods overestimate portfolio risks by up to 400%. Other solutions which utilize event sets also have limited geographic coverage (eg: USA).

This product also considers the impact of climate change. No other known product provides the comprehensive coverage which includes risk from future climate change scenarios.

The technology is underpinned by machine-learning enhanced datasets. The digital-elevation model (DEM) used is high-resolution (30x30m) and is the lowest average and standard error among known global DEMs.

The risk outputs from the models are precisely attributable. Unlike black-box models in the market, this product can pin-point the specific sources of risk and uncertainties. For instance, it can determine whether a building's flood risk stems mainly from flood intensity or the building's inherent vulnerability. The sources of uncertainty can be queried and areas where data-refinement can reduce the uncertainty can be identified. This is useful for purposes of auditing results, or for better targeting strategies for risk reduction.