Guidewire market strategy director, data and analytics, Dawn Mortimer on why underwriters need help to make sense of Big Data

There has never been so much data available, including from new sources like Internet of Things sensors in homes and businesses, to underwrite policies with a greater degree of accuracy. But the plethora of data resides in disparate systems, websites and across various internal and external data sources. 

The underwriting process can naturally be quite complex for property risks, especially writing commercial policies. For nearly 300 years, the same four property risk characteristics have been used by underwriters when evaluating an application for property insurance:

  • Construction – what material is used, how old is the property and how big is it?
  • Occupancy – how is the building being used? 
  • Protection – what is the distance to the nearest fire station or fire hydrant? 
  • Exposure – what risks of loss is posed by neighbouring property or the surrounding area? 

The underwriter has one of the most difficult roles in insurance. They need to make sure they are putting the appropriate business on the books and pricing for it properly in the event of future losses and market valuation, though they face many challenges.

Underwriters evaluating commercial properties need to review the risk and address issues such as:

  • Incomplete data from brokers/producers –
  • information coming from brokers differs significantly
  • Risk selection based on the underwriter’s experience is subjective
  • Lack of tools for understanding the risk exposure: no tools allow for geospatial location data, as well as other characteristics to understand the full exposure
  • Pricing issues for accuracy: underwriters are unable to conduct advanced analysis because they have to trawl through so many data sources. 

Underwriters need to supplement their expertise with the right analytics, as well as internal and external data, so that they can make informed decisions. There is a strong need for conformance to company underwriting guidelines, which are becoming more complex with all the new data sources and are changing more often than ever. The underwriter needs a clear view on where they can apply judgement and where they can’t.

By enabling their policy administration system to work in conjunction with the appropriate analytic tools, the insurer is able to put all the required data in a single workspace and offer the underwriter the context needed to understand location risk and make better risk acceptance. 

Capabilities like map-based interactive visualisation allows results to be understood in a more transparent way to enhance the decision-making process. 

By providing a variety of analytic tools that pull the data into one platform, underwriters will have location precision, underwriting consistency, and decision transparency to make informed decisions in the moment that drives profitable growth.