Quality of data will be a big theme at FIDES
The availability and quality of data will be one of the biggest talking points at the 2013 FIDES conference in Guatemala, according to Gino Smith, regional chief underwriter of XL’s reinsurance operations in Latin America.
Smith said that XL’s three key messages at FIDES all relate to data quality. They are specifically: the importance of tracking surety exposures by bondee and by beneficiary; improving the data quality and resolution of catastrophe modelling data; and tracking and accurately pricing non-modelled exposures such as flood particularly in light of recent flooding in Mexico following Hurricanes Ingrid and Manuel.
“The majority of our clients track Surety exposures by bondee and not by beneficiary. We are working with them to get the information provided in this format and also encouraging them to move from reporting aggregate information annually to semi-annually,” Smith said.
“We have seen two major industry losses throughout the region where tracking by beneficiary could have helped insurers and reinsurers alike to better understand their exposure concentrations and mitigate the extent of the losses.”
On the cat modelling front, he said despite significant advances in the quality of data available in the region, there is still a long way to go.
“Much of the data used for property catastrophe risk modeling is still being provided on an aggregate basis,” he said. “Additionally, non-modeled perils such as flood, hail and severe convective storm continue to be of concern as there is no consistent effort to track or price them across the region which could result in unexpected losses for the market.”
He said that XL is working with its clients and brokers to progress from receiving aggregate level data (ALM) to receiving detailed policy level (DLM) data that includes GPS location, construction type, and occupancy type, amongst other key details.
“We are also working with clients, brokers and insurance associations across the region to improve the quality of data for non-modeled perils so that we can better understand, track and price them,” he said.
“We see data quality as a major competitive advantage for our clients as it allows us to provide customised solutions to them. It also allows us to see who does a better job of managing and diversifying their exposures as well as avoiding concentrations in high risk areas – which has a direct impact on the price we charge.”
