Parametric insurance: basis risk and mitigation strategies

PwC’s Alexander Viergutz examines some of the steps that can be taken to address basis risk in parametric coverage.

With weather-related losses increasing and the protection gap widening to an estimated $172bn in 2023, according to Swiss Re, the insurance industry continues to find new ways to serve its purpose. One notable and exciting innovation has been the adoption of parametric (or index-based) insurance.

Unlike traditional indemnity-based insurance, parametric insurance pays out based on the occurrence of a predefined triggering event, such as a weather phenomenon, cyber attack, terrorism event or pandemic, irrespective of the actual loss incurred. An independent third party determines whether the parameter or index threshold is reached or exceeded to ascertain the claim's impact.

Parametric insurance offers several advantages over traditional indemnity-based covers, including expedited claims payments, greater claims certainty and transparency, the ability to insure hard-to-cover risks, and the flexibility to create bespoke covers tailored to specific needs. However, it also introduces some challenges, some of which are found in conventional covers. One of the most significant is basis risk.

Understanding basis risk

Basis risk arises when the parametric index does not perfectly correlate with the actual loss experienced by the insured. This can result in either a lower-than-expected payout (negative basis risk) or a higher-than-actual loss payout (positive basis risk). Several factors contribute to basis risk:

1. Index correlation: The index used to trigger payouts (e.g., rainfall levels, seismic activity, wind speed) may not perfectly align with the actual losses incurred.

2. Data quality: Inaccurate or insufficient historical data can lead to a mismatch between the index and actual losses. In regions with underdeveloped infrastructure, sparse data might lead to indices that poorly represent local conditions. Moreover, historical data may not reliably predict future conditions.

3. Model accuracy: If the models predicting losses based on the index are not accurate, the payouts may not reflect true damages – even small errors can lead to significant basis risk.

4. Geographical mismatch: 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 instance, localised events like hail or flooding might not be adequately represented by a broader parametric index.

5. Temporal misalignment: The timeframe used by the index may not align well with the period of loss, particularly in cases involving seasonal variations.

6. Non-physical triggers: Changes in underlying metrics, such as currency fluctuations or political instability, may not always correlate directly with policyholder losses, distorting the index's effectiveness.

Mitigation strategies for basis risk

While basis risk cannot be entirely eliminated, several strategies can mitigate it:

1. Enhanced data quality: Using reliable data sources, such as high-resolution satellite imagery and Internet of Things sensors, can improve data accuracy, refine indices and reduce basis risk.

2. Tailored triggers: Designing region-specific or location-specific triggers that consider unique characteristics and risks can more precisely reflect the risk event's intensity, ensuring accurate payouts.

3. Multiple or layered triggers: Employing multiple triggers increases the likelihood of payouts and offers a broader range of benefits. For example, crop-index insurance could use multiple parameters (e.g., yield, quality and commodity price) to minimise single-parameter effects. Similarly, drought insurance might consider not only rainfall levels, but also soil moisture and temperature readings to trigger payouts.

4. Hybrid models: Combining parametric insurance with traditional indemnity-based products can cover gaps and reduce overall basis risk.

5. Geographical precision: Using data sources geographically closer to the insured risk, such as local weather stations for agricultural insurance, can minimise geographical mismatches.

6. Temporal alignment: Ensuring the timing of triggers aligns as closely as possible with the period of risk, perhaps by analysing historical data to identify the most relevant timeframes for specific risks (e.g., to reflect seasonality).

7. Sophisticated modelling: Developing more advanced models that account for various factors influencing risk events can enhance accuracy. For example, earthquake insurance models might consider local building codes and ground composition alongside seismic activity.

The future of parametric insurance

Parametric insurance has demonstrated how data and technology can reshape the capabilities of the insurance sector to better meet gaps and address evolving needs.

Although basis risk presents a challenge, a multifaceted approach can significantly reduce it by improving model accuracy, refining index triggers, ensuring high-quality data, and designing policies than align as closely as possible with the insured’s true risk profile.

As data quality and modelling sophistication advance, parametric insurance holds promise for covering previously difficult-to-insure risks and even new, currently uninsurable risks.

In doing so, it can fulfil its wide-ranging potential and, alongside traditional insurance and other novel forms of risk transfer, play a key part in closing the protection gap and make society more resilient.