Climate-Related Physical Risks Exposed at All-You-Can-Game Buffet: Potential Dangers Assessed

Climate-related risks (CRRs) are a growing concern for the insurance industry, as they are path-dependent, highly uncertain, and can pose detrimental fat tail risks to human life. These risks are intensifying across all geographical locations, making them increasingly systemic and difficult to diversify geographically.

One of the main challenges in managing CRRs is the short-term biases of property & casualty (P&C) insurers' business models. The time horizon of traditional catastrophe models often focuses on short- to medium-term event probabilities, whereas climate risks evolve over longer horizons with increasing severity and frequency. This time horizon mismatch can lead to under- or over-estimation of risks.

Another challenge is the risk-profile, time-horizon, and scenario base mismatches between CRRs and P&C insurers' catastrophe models. Insurers have heterogeneous portfolios with different hazard exposures, and models calibrated for one geography or exposure type may misrepresent risk for others. Additionally, applying historic or static scenarios in a changing climate context reduces model relevancy, as models that do not incorporate plausible climate change scenarios or mitigation efforts fail to capture future realities, reducing prediction accuracy.

Vendor-specific biases can also be a source of uncertainty in assessing the property & casualty insurers' catastrophe models. Each vendor’s model incorporates proprietary assumptions and data sources, leading to biases that can affect loss projections, hazard representation, and risk transfer decisions. Overreliance on a single vendor model can lead to blind spots, as some risks or mitigation effects may be under- or over-estimated, influencing premiums and insurer solvency.

To address these challenges, data science professionals can employ multi-model approaches, incorporate updated climate science and mitigation data, stress-test and sensitivity analyze models, improve data quality, and transparently document and communicate model assumptions, limitations, and uncertainties.

Regulatory oversight and model validation can also play a crucial role in ensuring models remain fit for purpose. Regulators can require periodic model reviews, validation against observed losses, and adjustment for climate trends. Encouraging multi-model and multi-disciplinary collaboration, incentivizing mitigation and adaptation, developing national or regional risk platforms, and promoting transparency and consumer engagement are additional strategies to mitigate risks associated with applying P&C catastrophe models to CRRs.

In conclusion, managing CRRs effectively within property & casualty insurance requires a complex interplay of data, assumptions, evolving climate science, and stakeholder collaboration. By addressing potential sources of uncertainties, improving models, and fostering collaboration, the insurance industry can better prepare for the challenges posed by CRRs and help protect human life from detrimental fat tail risks.

References

Mills, M. J., et al. (2021). Climate change and insurance: A review of the state of the art. Nature Reviews Earth & Environment, 2, 339–352.
Genest, C., & Sproat, R. (2021). Modeling climate change risks in insurance. Nature, 591, 503–506.
IPCC (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change.
Task Force on Climate-related Financial Disclosures (TCFD) (2020). Final Report 2020.
National Institute of Standards and Technology (NIST) (2021). Framework for Catastrophe Risk Modeling. Special Publication 1200.
The complex nature of managing climate-related risks (CRRs) in the insurance industry necessitates the employment of data science professionals to improve model accuracy and transparency.
Climate risks, which evolve over longer horizons, pose a challenge due to short-term biases in traditional catastrophe models, potentially leading to under- or over-estimation of risks.
The insurance industry must address vendor-specific biases in catastrophe models, as each vendor's model incorporates proprietary assumptions and data sources, leading to potential inaccuracies in loss projections, hazard representation, and risk transfer decisions.
To effectively manage CRRs, regulatory oversight and model validation are crucial, with measures such as periodic model reviews, validation against observed losses, and climate trend adjustments being essential to ensure models remain fit for purpose.