Case Studies

Data resolution and quality is critical to every aspect of the insurance industry, from risk assessment through to portfolio management. Yet while the ability to model and analyse information is improving, data granularity and accuracy remain woefully insufficient.

To explore the issue of the lack of high-resolution data currently available to the insurance industry, Insurdata has teamed up with market-leading re/insurers to conduct a series of data studies based on live property portfolio data.

In each study, Insurdata carried out a detailed analysis of the data each company provided and augmented the geocoded information via the Exposure Engine. The reports compare original exposure data to the enhanced exposure data, including the impact from key risk metrics, such as annual average loss figures and exceedance probability curves, to key performance indicators, such as loss ratios, P&L and balance-sheet reserving.

Case Studies

“Insurdata are using the latest technology to tackle the market issue of poor exposure data quality, and have developed a data structure that can grow as Vave continues to expand its product offering, allowing us to feed our pricing algorithms with the most accurate data at the point of sale.”
Marek Shafer, Managing Director at Vave (part of Canopius Group)

Flood risk with SCOR

SCOR and Insurdata conducted a study to assess how high-resolution exposure data available at the original point-of-underwriting informed the underwriting process

Breaking down the flood data

Canopius and Insurdata explored the extent to which augmented geocoding, perimeter and building attribute data affected exposure levels and annual average loss forecasts
Talk to an Expert
“We saw the expected losses to flood as a result of the repositioning change by an order of magnitude of as much as 80% in some cases, and that is material”
Paul Nunn — Head of Catastrophe Modelling at SCOR