It’s time to rethink the limits of parametric insurance.
The concept behind parametric insurance is not new. Insurance payouts automatically triggered by an event exceeding an index level have existed for decades. It’s traditionally been applied to events like natural catastrophes that are relatively straightforward to define and for which an index or scale can be substituted for the actual loss sustained. When an earthquake reaches a certain magnitude as measured by the Richter scale, for example, insurers pay the policyholder an amount agreed to ahead of time under the terms of the contract.
Scientists have used the Richter scale to measure the size of earthquakes for more than 80 years. Today, however, data scientists can produce a fairly accurate index for far more abstract events, like customer sentiment on Twitter, or a change in shopping habits following an infectious-disease outbreak. Parametric insurance has also been applied to operational risk for large financial institutions.
Big data and related technologies are driving insurers and customers to rethink what’s possible with a parametric insurance model.
But the rise of parametric…
