Reduce spatial basis risk to enable new insurance solutions

Our solutions reduce the spatial basis risk that occurs as a result of the discrepancies between the weather at the nearest weather station and at the asset’s location. These discrepancies cause unsuitable payments to be made, and user dissatisfaction. On the other hand, our historical data solutions aim to reduce the gap between the insurer’s and the client’s knowledge of season-specific environmental information, thus reducing adverse selection.

Benchmark Labs Insurance

We enable the development of better parametric insurance models by delivering historical asset-specific data and probabilities, and by providing real-time in-field observations that are used to determine payouts, when certain conditions are triggered.

Benchmark Labs Localized Insurance Forecast


What is unique about our innovation?

Unlike traditional weather forecasting approaches, we offer environmental forecasts for specific locations (latitude, longitude, elevation) rather than mean forecasts over an area (grid).


Understand weather history

No matter the location of your asset, our API provides access to location-specific historical weather data to enable better parametric insurance models and understanding of probabilities of occurrence.

By comparing current and historical conditions at the asset level, our users can contextualize today’s weather more effectively, thus allowing better management & pricing decisions.

Benchmark Labs Insurance Historical Data

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With the support of

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