In today’s very competitive P&C insurance market, many insurers are struggling to underwrite policies with a high degree of assurance that they are pricing accurately. Whether you are writing policies for autos, personal or commercial property, insurers will find that using high accuracy geocoding and current geospatial data will improve risk models.
By utilising precision geospatial data, P&C insurers can increase the level of modelling granularity and accuracy, beyond traditional techniques, which have often used large postal or demographic areas alone. There are, however, myriad data sets available for property locations and attributes such as building footprints, age of the building, next generation high-definition flood risk and terrain data in raster format, traffic profiles, and others.
However, leveraging these multiple sources of data, usually implies working with terabytes of geospatial vector and raster data, which require specialized data science, data engineering or analytical tools that can provide scalability. Turning to cloud-native or cloud big data technologies will be required, but data scientists may still struggle with geospatial data integration and analytics.