Parametric Insurance Hidden Realities: Beyond the Algorithm

Beyond the Algorithm: The Hidden Realities of Parametric Insurance

Intelligence often drives parametric insurance as a cutting-edge innovation. Machine learning technologies help predict the weather. Network analysis processes satellite images. Finally, smart algorithms activate payment triggers. However, beneath this technical efficiency, the above-described reality conceals another layer, one that makes the difference between the success or failure of parametric insurance. Exploring the parametric insurance hidden realities is essential, and three factors play a crucial role behind the scene.

Parametric Insurance Hidden Realities: The Origin of Truth Is Political, Not Intelligent

In order for a parametric instrument to work, there needs to be an independent data source a weather station, a seismometer, a water gauge that verifies that a certain triggering event has indeed taken place. The more subtle problem, which many overlook, is that all such instruments are liable to being false, controversial, or manipulative. Consider the farmer who won’t believe that his insurance was triggered if the official rain gauge registered just below 50mm when the threshold was actually 50mm.

Here again, AI can’t help. The real challenge here is creating tamper-proof networks of sensors, redundant streams of data, and dispute resolution processes. Some parametric companies use blockchain technology to record their data irrevocably. Others rely on third parties to verify the data they collect. This isn’t an issue where AI comes into play.

Parametric Insurance Hidden Realities: Basis Risk Is a Design Issue, Not a Prediction Issue

Basis risk the difference between the trigger event and the loss experienced is the Achilles heel of parametric insurance. In the case of hurricane parametric insurance, the policy is triggered by winds above 100 knots. Yet, the policy holder could lose their home to destruction at 95 knots; conversely, the building could withstand winds at 105 knots, depending on its construction.

The touted use of AI is to improve modelling to minimize the mismatch between loss and trigger event. The more hidden truth, however, is that in essence, basis risk is simply the trade-off in the design of contracts. By simplifying the event to enable automatic payment, you incur basis risk. It is built into the segmentation phase of product development: parametric insurance is offered to complement, not replace, traditional insurance payments.

The Adoption Barrier of Parametric Insurance:

It makes total sense from a spreadsheet point of view. Still, there is very little adoption happening in non-catastrophe bond and crop insurance scenarios. What could be a less obvious cause of this phenomenon? Humans are reluctant to accept an automatic claim they do not understand. The person that receives 50,000 following an earthquake but actually suffered damages worth 200,000 feels like a fool. By contrast, the company that does not receive any money because the water gauge level was only 10 cm lower than needed feels cheated.

The solution for this issue that lies in the field of digital technologies is not artificial intelligence. The most innovative companies work hard on building better interfaces and means of communication with their clients. Interactive visualization of the triggering condition, real-time monitoring of weather stations and other instruments – all this helps to gain people’s trust.

Conclusion:

AI will drive improvements in the modeling of parametric triggers and risk selection going forward. However, it is the relatively unexplored realm of data integrity, basis risk, and transparent human communication that will make or break whether parametric insurance can become a more mainstream form of insurance. No amount of machine intelligence can replace a good rain gauge and open discussion of insurance limits.