Tackling a challenging risk landscape: is a new modelling approach required?

Moody’s RMS’ Chloe Garrish on why high definition represents the future of modelling.

Making sense of rising global risks based on outdated catastrophe model approaches without the benefit of scientific and technological advancements is like using a dial-up modem to surf the web in an age of fibre optics.

Compared to 20 or even 10 years ago, the computer power needed to accurately model high-hazard gradient perils such as floods, severe convective storms or wildfires, which all require a high level of granularity, is much more readily available. But leveraging new technology and computing power is just one part of the story.

To take full advantage of all the recent technological and scientific advances requires a paradigm shift in modelling that goes beyond what has gone before. You need to combine powerful, native cloud-based computing together with, for instance, an advanced temporal simulation modelling framework that can accommodate tens of thousands of years of simulated events, to evolve probabilistic risk modelling and deliver a more realistic representation of loss.

Offering granular, accurate model insights all delivered many times faster than traditional modelling approaches, Moody’s RMS’ suite of high-definition models opens a world of risk management possibilities. All HD models are available on the cloud-native Moody’s RMS Intelligent Risk Platform via applications including Risk Modeler and UnderwriteIQ.

Getting a clearer, more realistic picture of risk can generate deep insights into uncertainty, with the potential to revolutionise risk management practices and business decision-making.

Better informed, more accurate and confident decision-making, using the most current and sophisticated tools that guide insurers, also helps build an increased level of trust within the industry when making crucial choices about capital needs, pricing, and reinsurance coverage.

But how does HD modelling represent the evolution of risk modelling? We have identified three key reasons:

Improved spatial and temporal coverage

Using an HD model framework backed by a robust simulation engine offers a nuanced time-based evolution of each event over authentic multiannual perils. This is important when capturing seasonality, antecedent conditions and windstorm clustering with precision.

Simultaneously, introducing significantly larger event sets heightens spatial coverage, especially for high-frequency events like hail, tornado and severe convective storms.

Together, these enhancements allow for a more accurate expression of risk across pricing, accumulation and reinsurance, grounded in highly realistic loss distributions.

Higher granularity of hazard and damage calculations

Using a uniform resolution grid instead of a variable resolution grid gives HD models the promise of unmatched spatial fidelity, ensuring a consistent, high-resolution view of hazards, eliminating the need for aggregating hazard layers, and allowing for detailed exposure disaggregation.

By leveraging ground-up simulations, the damage is assessed at the coverage level for each specific location, making use of the enhanced high-resolution hazard data.

An advanced financial model applies policy conditions at every level – from location to treaty – facilitating straightforward aggregation to any desired financial perspective, and combined, these advancements help improve the accuracy of risk assessments and strategic decision-making.

Enhanced model transparency

A clear understanding of how financial contracts are applied and how losses flow through them from all loss perspectives allows HD model users to set their own benchmarks for determining when losses have adequately converged.

Previously unseen in traditional catastrophe models, this grants a level of control and confidence in the reliability and stability of their output. Stakeholders are not merely dealing with data, but with insights that are both clear and adaptable to individual needs.

New risk modelling approaches are required to understand a more challenging risk landscape and the business environment. HD models are built to help clients capitalise on the potential of science and technology, and deliver the new required risk insights.

Chloe Garrish, senior product marketing manager, Moody’s RMS