A common language for uncommon risks: Choosing your physical risk analytics path

Key Takeaways:

• A new report by the UK’s Financial Conduct Authority’s Climate Financial Risk Forum (CFRF) provides an expansive overview of the physical risk analytics market and divergence in insights produced from the solutions available today. 
• Variability of results is often a result of modeling choices and quality. 
• Providers choices on granularity (i.e., resolution to model flood) and validation (i.e., calibration against real world data) specifically impact model outputs. 
• The insurance industry has established a formalized process for evaluating modeling credibility and validity that can be used as a valuable precedent for the broader financial sector. 
• If the financial sector invests in a common language of risk, as defined by rigorous physical risk analytics, new opportunities for financial resilience can emerge in protecting bottom lines and capitalizing on top line potential. 

As the physical risk analytics market rapidly evolves, banking, investment, and corporate risk management leaders face a complex and potentially confusing marketplace. New research from the Climate Financial Risk Forum (CFRF) and the breadth of analytics available suggest we have reached a pivotal moment in market development, which demands a structured approach to evaluating data, models, and methodologies.

“Although we know that the overall threat level is rising, it is not straightforward to translate this into how physical risks might impact individual firms, including those in the financial sector, or even individual assets. For this, a granular spatial resolution of analysis is needed. The risk profiles for a bank, insurance company, asset owner or asset manager differ — as discussed in many CFRF documents over the years — but all firms share the common challenge of how to assess the physical impacts that they are exposed to, be that in their own operations or in the firms that they lend to, underwrite, insure or invest in.” - CFRF

The state of physical risk analytics

Moody’s recognizes the complexity in modeling physical risks as stakeholders navigate inconsistency in definitions, data availability, resolution in modeling, and scope of analytics as highlighted in the CFRF study. There are, however, known dimensions of modeling design that impact the quality of outputs and resulting insights. The insurance industry utilized third- party models of physical risks for more than two decades and the industry best practices in evaluating solution quality can offer the rest of the financial sector a framework for assessing the forward-looking physical risk models available today, as highlighted in the CFRF report to invest with confidence.

Not all models available today are created equal. Differences in the underlying analytics fundamentally shape results and relevance for decision-making. More specifically, hazard modeling assumptions, vulnerability data, spatial resolution, and data sources lead to significant variability in outcomes. This means that the variability can often be traced back to questions of quality. For financial institutions, this means that model validation standards must be front and center when choosing analytics to ensure reliability.

Understand today’s risks, better estimate tomorrow’s

The need for long-term estimates of financial impact, this time horizon aspect of the modeling used to uncover opportunities for resilience strategy and adaptation investments, presents inherent uncertainty in model outcomes. However, the experience of the insurance industry in developing an approach to estimating current day physical risk provides a solid and reliable foundation to support the development of forward-looking analytics.

We are not starting from zero. Lessons from the insurance industry ground physical risk modeling in quality.

Modeling long-term physical risk hazards and financial impacts will result in a range of potential outcomes due to uncertainty in both the science and measures adopted to mitigate a changing climate. However, risk executives can have more confidence in the outlook when the estimate of today’s risk is based on validated modeling approaches. A more robust estimate of risk today provides more justifiable band of uncertainty and associated estimates of financial impact as projections extend to long-term horizons.

For over three decades, the insurance industry has established expectations and use cases for current day physical risk models. These catastrophe (cat) models were initially developed to support underwriting rare, but costly events notably following the financial impacts of the devastating Hurricane Andrew that struck Florida and Louisiana in 1992. The industry needed new tools to quantify and predict risks and guide proactive strategies and investments to mitigate their exposure.

Cat modeling has evolved to become integral to insurance industry business strategy and accepted as a critical input in serving customers in these regulated markets. Within this regulated environment, insurers demanded that providers establish a robust model validation process to justify the incorporation of the third-party cat models and resulting insights in their rate making. The result is a robust framework for modeling current day risk, which establishes a critical baseline for expansion into modeling forward-looking physical risk.

The approach creates more rigorous results by incorporating detailed hazard assumptions and vulnerability data refined with comparison to real-world event impacts, relevant spatial resolution, and robust data sources. How a physical risk analytics solution incorporates these elements results in significant variability in output metrics.

For the insurance industry, cat models have helped insurers better price policies to reflect risk, determine the appropriate reserves necessary for sustainable business within the context of their exposed risk, and execute risk transfer strategies to secure their viability into the long term. Today, the broader financial sector can learn from this industry experience to assess the robustness of physical risk analytics and their suitability for long-term strategy.

The lessons of these modeling principles are demonstrated in the relative accuracy of current day risk assessment, setting the right foundation for forward-looking analytics. No model can perfectly predict future risk, but the rigor of the cat modeling approach provides transparency in process and has been refined in the insurance regulatory process to help justify the distinction against other top-down options.

How to evaluate quality? A framework for comparison

Moody’s suggests three key benefits in investing in physical risk solutions built on the lessons from the insurance industry that transfer throughout the climate risk journey. From preliminary screening for refined goal setting to unlocking opportunity for business transformation that mitigates climate risk, you may need different context, but the foundational analytics should remain consistent. A solution that delivers applications designed for distinct use cases should all:

• deliver specific and granular insight into risk at any location based on best available science, 
• provide confidence that models that have been validated and incorporate process for continuous improvement, 
• and demonstrate consistency in language that enables new conversations in boardrooms and across industries uniquely stressed by the threats of physical risk. 

1. Location-level assessment and best available science

Scale is critical when evaluating physical risk. The variability of risk and impact reflects details at the location level across four dimensions, which should be incorporated in the modeling approach:

• Hazard : What wind speed can I expect?
• Vulnerability: How badly would the building be damaged at that wind speed?
• Exposure: What characteristics does the building at that location have? Is it built out of wood or cement? 
• Financial loss : What damage to the building structure, its content and ensuing business interruption does result? 

Moody’s has tackled this complexity by incorporating granular, real-world data from millions of dollars of insurance claims and on-the-ground post-event engineering studies.

These inputs shape foundational cat models and inform the climate-conditioned forward-looking physical risk analytics. The result is a combination of output metrics that help customers assess risk at the scale of a single site, portfolio of real assets or within a broader geographic boundary. This flexibility in analysis helps inform key business decisions at different stages of risk management strategy and for different use cases.

As examples, site specific risk can help lenders understand potential changes in credit worthiness in regions likely to face increasing physical risks. Portfolio level insight can help commercial real estate (CRE) investors better establish diversification strategy across a local market. Aggregation at the corporate level can support reporting and disclosure needs. Spatial areas can estimate regional financial impact for benchmarking relative physical risk across geographies for informing potential changes in municipal debt.

2. Transparency and regulatory validation

Risk executives are facing an inflection point in how they address physical risk. Stakeholder pressures are driving a shift from aspirational long-term sustainability or net zero goal setting to more physical risk assessments and planning that showcase specific business benefits.

As mentioned above, the framework for model validation from the insurance market offers lessons for the broader financial sector, and transparency is a critical dimension for risk leaders evaluating third-party models. It should be clear what data inputs inform the estimates of hazard, assumptions on vulnerability, and spatial resolution for each peril modeled. Furthermore, executives should have visibility into how model outputs are continuously reassessed, refined, and updated based on real-world events today and the assumptions on how the results may change in the long term. These are well established standards in the insurance industry that can be built on by decision makers in other industries.

3. A common language of risk to move from aspiration to action

While the pressures on risk management executives can vary across sectors and geographies there are benefits to investing in a physical risk solution that offers a consistent approach and output metrics across use cases. When comparing the business-as-usual risk management approaches in banking and insurance, as examples, these industries strategize and act within different time horizons and are informed by different decision metrics.

However, today the financial impacts of physical risks are impacting both critical actors in the market and driving new demand for understanding of these event threats to their bottom lines and how they may uncover new top line opportunities. Consider that according to the results of recent analysis on a potential Cat 5 hurricane hitting Miami, our estimates suggest the economic impact from this single event would be worse that of a normal recession.

While banks may define expected loss to reflect economic value of a property and profit impacts, related to their counterparty credit risk. Insurers look at the financial costs of damage and interruption to an insured property, but devaluation of the land, for example, may not be a concern. These two financial industry market-influencers decisions are informed by complementary, yet distinct risk metrics to their financial resilience in terms of risk from customer financial stability – shaped in different terms such as credit worthiness or amplification of claim frequency.

Ultimately, these two critical players can benefit from a consistent framework to discuss risk when considering opportunity to drive mitigation and adaptation investments that can reduce risks in both sectors. That, in turn, necessitates models whose quality and validation credentials can service both industries. If these parties have a common language on the financial impacts of physical risks, new runways can emerge for business model innovation, risk transfer, and incentivizing adaptation that can foster long-term financial resilience.

Where does all of that leave us – on model choice and opportunities going forward?

Physical risk analytics that deliver consistent financial impact metrics that can serve as inputs into segment-specific workflow analytics enable consistency – consistency in speaking about the same risks in the same terms. As we face increasingly intense and frequent physical risk events, there is more opportunity for innovation in driving infrastructure hardening, site-specific mitigation measures, and adaptation investments that will shift the likely damage, destruction, and economic impacts – a common language of risk will enable new ways of incentivizing investment at the intersection of underwriting mortgages, insurance, and policymaking.

Despite the complexity of a rapidly evolving emerging analytics marketplace, one thing is clear: climate risk is business risk. As the market for physical risk analytics grows, thoughtful evaluation and adoption of robust, validated solutions will be the key to resilience and competitive advantage. The insurance industry has been modeling physical risks based on trusted methods, data and validation approaches and learnings for model validation and quality requirements can be transferred.

Moody’s is committed to providing the data, models, and expertise needed to navigate this complex landscape. Learn more:

• How detailed physical risk modeling empowers executive decision-making 
• Empowering smarter risk decisions: How Spatial Areas data transforms physical risk management 
• Physical and Transition Risk – Moody’s 
• The Eye of the Storm