Can parametric insurance models be fueled by synthetic data?

As the world around us changes – and the world of insurance races to adapt – parametric insurance stands out as a revolutionary approach that promises efficiency, transparency and tailored risk management.

What is parametric insurance?

Unlike traditional indemnity-based insurance, parametric insurance provides coverage based on predefined parameters or triggers. This innovative model is particularly beneficial in regions prone to natural disasters or other unpredictable events where conventional insurance methods may fall short.

Imagine a scenario where a hurricane devastates a coastal town. Traditional insurance would require a lengthy claims process, involving assessments and paperwork, before any payout is made. In contrast, parametric insurance simplifies this process. If the hurricane’s wind speed exceeds a certain threshold, the policyholder receives an immediate payout.

This speed and efficiency can be transformative for those affected by such disasters. Not to mention the degree to which it could reduce the time and administrative burdens associated with claims processing.

Transparency is another hallmark of parametric insurance. Policyholders know exactly what events will trigger a payout and the amount they will receive. This predictability fosters trust and confidence because there are no surprises or ambiguities in the coverage.

When and where to use parametric insurance

Parametric insurance makes the most sense in markets where traditional insurance methods are more challenging due to the nature of the risks involved. These include:

• Regions prone to natural disasters. This includes areas susceptible to hurricanes, earthquakes, floods and other natural disasters. Since predefined triggers for parametric insurance ensure quick payouts, affected communities can recover faster.
• Farmers facing risks like droughts, floods and pest infestations can use parametric insurance to protect their livelihoods. With fast payouts, they can manage losses and continue their operations with little interruption.
• The travel industry and areas with high tourism activity. These areas can use parametric insurance to cover things like flight cancellations, extreme weather events and other disruptions. This gives peace of mind to travelers and businesses alike.
• The energy sector. Companies that deal with equipment failures, supply chain disruptions and natural disasters can use parametric insurance to manage these types of risks more efficiently. Another benefit is that the parameters underlying this type of insurance coverage may prompt organizations to take precautionary measures that prevent damage in the first place.

However, the success of parametric insurance hinges on accurate and comprehensive data to set the appropriate parameters and triggers. This is where data scarcity can pose a significant challenge, especially in areas where historical data is limited or nonexistent.

Synthetic data plays a crucial role in developing sophisticated models for new products like cyber or parametric insurance and rare events like natural disasters.

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Synthetic data for filling gaps, improving reliability

Synthetic data is artificially generated data that mimics real-world data. It is created using algorithms and statistical models to replicate the characteristics and patterns of real-world data. Synthetic data can be invaluable in situations where historical data is scarce or unavailable.

For parametric insurance, synthetic data can fill the gaps in historical records, ensuring that the parameters and triggers are based on robust and reliable data sets. By simulating various scenarios and generating synthetic data, insurers can create a comprehensive data set that accurately reflects a variety of potential risks and events.

Tackling data scarcity with advanced analytics

With our proven capabilities in data and AI – including advanced analytics, machine learning and data management – SAS can help you generate synthetic data to overcome data scarcity challenges.

• Advanced analytics used with existing data can identify patterns and trends – insights that can be used to generate synthetic data (a product of generative AI). This helps create a robust data set for parametric insurance.
• SAS machine learning algorithms can simulate various scenarios and generate synthetic data that mimics real-world events. This synthetic data can fill gaps when historical data is lacking, so your parametric insurance model will be built on comprehensive and reliable data.
• Data management capabilities integrate data from multiple sources, including satellite imagery, weather data and other relevant data sets, to create a more complete picture of risks and real-world scenarios. This integrated approach provides you with the most accurate and relevant synthetic data possible.
• SAS tools can monitor and update synthetic data as new information becomes available, leading to continuous improvement that keeps your parametric insurance model up to date and effective at managing risks.

A bright parametric market future

In a report exploring four potential futures for insurance, Economist Impact noted: “The industry can aggressively develop forward-looking climate risk models to better manage their portfolios and move into new areas. This will create new products and services (e.g., parametric solutions) that de-risk physical assets and incentivise decarbonisation projects.”

Notably, according to a report by Allied Market Research, the parametric insurance market’s projected value is expected to reach $34.4 billion by 2033, with a compound annual growth rate (CAGR) of 6.6% between 2024 and 2033. This growth is driven by the increasing demand for efficient and transparent insurance solutions in regions prone to natural disasters and other unpredictable events.

Ready to learn more about the potential of parametric insurance for insurers and customers?

Read the results of a survey by SAS and the Open and Embedded Insurance Observatory