As parametric insurance gains traction, insurers face specific challenges in capital modeling and regulatory capital navigation. I have a longer paper coming out on this, but if you’re looking for an intro, here are some of the interesting and different aspects compared to more traditional insurance.
1. Regulatory Uncertainty: The treatment of parametric insurance under frameworks like Solvency II and SAM remains ambiguous. Insurers must engage proactively with regulators to establish appropriate methodologies. Regulators have the challenge of how to shoe-horn parametric insurance into a regulatory framework that was not designed with this in mind. For example, in South Africa, a of 2024 at least, parametric non-life insurance is approved on case by case basis under a regulatory sandbox, but as “non insurance business”. This is because under current regulations, “non life insurance” must be on an indemnity basis.
2. Line of Business Allocation: Fitting parametric products into traditional lines of business is complex. Many parametric products resemble inwards non-proportional reinsurance more than direct insurance, with payouts triggered by specific events. Even then, there is no guarantee that the standard premium volatility factors are appropriate. Insurers may need to explore Undertaking/Insurer Specific Parameters (USP / ISP) or transition to partial internal models. For now, this “non insurance business” approved in South Africa has typically been allocated to the agriculture LoB for capital purposes. This may match the nature of the business (typically drought or rainfall related) but there is no reason to believe that the variability in claims will match that of other agricultural business. I wonder whether “inwards non proportional reinsurance” might be a better fit in some ways. The reserve risk parameters will hopefully be too conservative – since the a key idea behind parametric insurance is very quick and objective claim settlement without extended reporting or payment delays.
3. Portfolio Size and Trigger Remoteness: The risk profile changes significantly with smaller portfolio sizes and trigger remoteness. As triggers become more remote, the capital required relative to premium increases. At a certain point, the 99.5th VaR can fall well outside the 3-sigma range, challenging standard deviation-based approaches.
4. Diversification Effects: Understanding correlation between parametric triggers, and at different levels of triggers, means approaches like copula modeling might be necessary. Student t copulas are a likely candidate. As portfolios grow and become more diversified this may moderate. However, there will almost always be fewer sensors / indices than individual policyholders and risk exposures. Therefore I expect challenges on diversification to continue.
5. Attritional vs. Catastrophic Losses: The binary nature of parametric triggers blurs the line between attritional and catastrophic losses.
6. Time Series vs. One-Year Capital View: While sensor data forms a time series that could be modeled using techniques like SARIMAX or GARCH-X, the one-year capital view required by regulations doesn’t necessarily need to incorporate this time series structure. The complex physics-based models that are increasingly used for pricing and prediction will likely remain too unwieldy for capital purposes for an extended period.
7. Climate risk and trends: An advantage of parametric insurance is the typical clean time-series sensor records (necessary for pricing and risk management). However, the continued relevance of historical records is at risk given climate change for many key parametric coverages.
8. Demonstrating Appropriateness: The Head of Actuarial Function (HAF) faces the challenge of demonstrating that the chosen capital approach appropriately reflects the risk profile of parametric products. The approach needs to work within the regulatory framework, but the result must still be reasonable.

As the parametric insurance market evolves, so too must our approach to capital modeling. The challenges are significant, but so are the opportunities for innovation and more accurate risk assessment.
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