Basis risk in parametric insurance

Because there is no need for traditional claims investigations, payouts can be made quickly, often within days of a triggering event. This can be critically important in situations where funds are needed urgently, such as after a natural disaster.

The policyholder has a clear understanding of the exact amount to be paid and under what conditions in advance, meaning that all ambiguity is removed. An independent source defines the mechanics of any payment, thus ensuring a truly unbiased method of assessing the policy. The promise of a seamless payout mechanism contrasts with the long, drawn-out claims adjudication process observed for traditional policies.

Because payouts are based on a pre-determined metric, such as wind speed or the magnitude of an earthquake, parametric insurance is straightforward and easy to understand.

Because payouts are based on a pre-determined metric, in certain situations parametric insurance can be more cost-effective than traditional insurance policies.

Parametric insurance pushes the boundaries of traditional insurance by using data to offer alternative means for underwriting difficult-to-insure losses – unanticipated expenses, lost wages, revenue shortfall.

In contrast to conventional insurance policies that tend to have standardised policy wordings, parametric policies are tailor-made, allowing the policy holder to determine the parameters, terms, and conditions that align with the particular risk exposures they are looking to cover. The term of a parametric cover is also flexible – a policy can be effective for any period from a few months to several years.

From an investor’s perspective, parametric insurance can effectively reduce the volatility of a company’s or a project’s financial results, making them more willing to lend or invest in a company/project.

The use of an index (such as rainfall levels, seismic activity or wind speed) to trigger insurance payouts means that if the index doesn’t perfectly correlate with actual losses, basis risk occurs.

Inaccurate or insufficient historical data can lead to a mismatch between the index and actual losses. For example, in regions with less developed infrastructure, data might be sparse, leading to indices that don’t accurately represent local conditions. Also, historical data might not be a reliable predictor of future conditions due to climate change or some other evolving factor.

If the models used to predict losses based on the index are not accurate, the payouts may not reflect the true damages. The complexity of natural phenomena makes modelling them inherently difficult. Small errors in models can lead to significant basis risk. Also, models can become outdated over time as new research and data become available.

The index may not capture localised losses if the geographical area it covers is too large or not well aligned with the actual area of loss. For example, losses can be highly localised, such as hail damage or flooding. This may not be captured by a parametric index covering a broader area. In another example, disparities in infrastructure and building codes within the covered area can also lead to a mismatch between index readings and actual damage.

If the timeframe used by the index does not align well with the period of loss, this can contribute to basis risk. For example, seasonal variations can impact the relationship between the index and actual losses.

Deductibles, limits and the specific structure of the parametric product can affect how closely payouts match losses. The choices of deductibles, caps and limits can lead to significant basis risk if they do not align well with the actual risk profile and financial resilience of the insured. The exact wording and definitions within the policy can also lead to disputes about coverage.

For non-physical triggers, changes in the underlying metrics may not always correlate directly with the policyholder’s losses. For example, currency fluctuations, political instability as well as other economic variables can distort the correlation between the index and actual losses.

Changes in regulations or legal interpretations can influence the execution and effectiveness of parametric insurance, introducing basis risk. New laws or interpretations of insurance contracts can change how policies are enforced, potentially leading to unexpected gaps in coverage. Cross-border insurance policies face additional complexity from differing legal systems, which can contribute to basis risk.

For parametric insurance linked to financial markets or commodities, market volatility can influence basis risk. For parametric insurance policies tied to commodities or other financial indicators, swings in these markets can lead to payouts that don’t align with the insured’s actual losses. External economic shocks can distort the relationship between the trigger index and the actual value of the loss.

One of the most effective ways of addressing basis risk in parametric insurance is by using reliable data sources to infer the probable risk of triggering a payout. The deployment of high-resolution satellite imagery and IoT sensors can also provide more accurate data, thereby refining the index and reducing basis risk. This helps to improve the accuracy of the payout and lowers the chances of basis risk occurrence. Employing sophisticated data analytics, including AI and machine learning algorithms, can also help in better understanding the correlation between parametric triggers and actual losses. These technologies can analyse large datasets to refine trigger thresholds and reduce the likelihood of a mismatch.

Designing triggers that are closely aligned with the insured’s specific risk exposure can significantly reduce basis risk. This involves understanding the unique aspects of the risk being insured and customising the parameters accordingly. 

Parametric insurance providers can also offer multiple or layered triggers to mitigate basis risk. By offering multiple indices, providers can increase the likelihood of payouts and offer a wider range of benefits to policyholders. For instance, in crop-index insurance policies insurers could utilise multiple crop parameters (e.g., yield, quality and price of the commodity) to minimise the effects of any single parameter. In another example, a drought insurance policy might consider not just rainfall levels but also soil moisture and temperature readings to trigger payouts.

Another effective approach towards addressing basis risk is by developing dynamic parametric insurance products. These products use live data, including satellite readings, to calculate payouts and adjust parameters continually. Adopting dynamic indices that can be updated with new data and risk modelling techniques to reflect the changing nature of risks can minimise basis risk. This approach ensures that payments reflect real-time conditions, reducing the likelihood of basis risk.

Using data sources that are geographically closer to the insured risk can reduce geographical mismatch. For example, utilising local weather stations for agricultural insurance rather than regional averages. Also, ensuring that the timing of the triggers aligns as closely as possible with the period of risk (e.g. to reflect seasonality) can mitigate basis risk. This might involve analysing historical data to understand the most relevant timeframes for specific risks.

Developing more sophisticated models that can account for a variety of factors influencing a risk event can mitigate basis risk. For example, in earthquake insurance this could include considering not just the seismic activity but also local building codes and ground composition. Also, regularly updating and refining models based on new data and past claim experiences is key in order to keep the triggers relevant and accurate.