The elevated level of natural catastrophes that the world has seen over the past few years should mean a re-evaluation of the nature of secondary perils.

In the opening speech of the third day of the Singapore International Reinsurance Conference, Gianfranco Lot, chief underwriting officer, P&C Reinsurance, Swiss Re, warned that the perils previously labelled as “secondary” had been underestimated.

He pointed out that wildfires, convective storms, hail storms, floods and landslides reported around the world over the last few years are among a number of perils that have been underestimated and were therefore not appropriately modelled.

Lot said that these so-called secondary perils were now some of the main drivers that have been pushing up the average losses per year.

“Investors are quite concerned that the reinsurance and insurance companies are not earning their cost of capital and not delivering and charging enough for these types of risks,” Lot said.

“They also ask more fundamental questions: do you have the accumulation under control? Is your modelling appropriate? Do you make use of all the data that you have?”

Lot stressed that data management is the key to understanding the correct level of risk in the market. He pointed out that having access to satellite images gives re/insurers better data, and better visualisation of where the risk is. This can for example allow wildfires to be tracked, and the saturation of soil, which is relevant for agricultural insurance, to be monitored.

Lot added that partnerships can help. “We need to update the models, to make use of different data and collaborations and partnerships that make us smarter and provide us more insights into this space,” he said. “In insurance, the use of technology and data is fascinating and will transform the way we do business.”

With better management and better data, re/insurers can reach a better underwriting resilience, he said, adding that the Asia-Pacific region could lead the way in this area. Lot described the region as an incubator for this type of data innovation which makes collaboration important.