Over the past two years, FMG has been exploring the potential use of parametric insurance—a non-traditional model that pays out automatically when specific, pre-agreed events like heavy rainfall or earthquakes occur. We wanted to validate whether this approach could be suitable for FMG to offer for farmers and growers to help manage their risks in the face of extreme weather events.
The primary difference between traditional insurance (indemnity based) and parametric insurance (index-based) is the payment mechanism. Where traditional insurance requires damage assessment and claims processes to align payout with actual losses incurred, parametric insurance uses trusted third-party data (such as weather reports or satellite readings) to trigger a pre-agreed payment.
Partnering with industry to explore the possibilities
We collaborated with industry partners to explore a prototype heavy rainfall insurance product for New Zealand’s rural sector. Our work aimed to calibrate satellite data with on-farm measurements to confirm we could pinpoint accurate and fair triggers for payouts.
However, we encountered data challenges: the satellite grids available were too broad (minimum 10km x 10km), and the best trigger accuracy we could achieve was only 67%. In some cases, we were only achieving 18% trigger accuracy – a figure that doesn’t instil much confidence.
Through our explorative work we also identified the significant cost of purchasing high-quality third-party data was prohibitive for both FMG and our clients.
Pausing our work – this is not the end
Following these investigations, we made the decision to pause our work in this space. We identified these data gaps mean a heavy rainfall policy product won’t accurately trigger when it should, at a New Zealand wide level.
This doesn’t mean the end of parametric insurance for FMG. While we won’t continue to pursue a New Zealand wide heavy rainfall parametric insurance product, we will take our learnings and apply them against other perils and other data sets, and potentially target defined areas where it can trigger more accurately.
