How harnessing external data can transform the profitability of your property book

Now, more than ever before, insurers are understanding the power of data when it comes to revolutionising the underwriting process—and this is especially true when it comes to property insurance. To be able to gauge risk in the most accurate way possible, and gain a competitive edge with pricing, insurers need to overlay their own data with the insights derived from new sources of property-related external data, such as weather, mobility, and location data.

But achieving these enhanced insights is no simple feat. As noted by McKinsey in a  recent article: “Although external data sources offer immense potential, they also present several practical challenges. To start, simply gaining a basic understanding of what’s available requires considerable effort, given that the external-data environment is fragmented and expanding quickly.”

It may sound simple, but in fact data enrichment is an extremely complex and ever-growing challenge for insurers. To deliver maximum context, data must be acquired, verified, processed into the right format, and linked to the appropriate internal data. And after initial use, that data must be continually updated to stay valuable to the business.

Data integrity specialist Precisely helps insurers to focus on harnessing the power of data to transform their underwriting processes, rather than on the challenges of collecting and processing that data—offering expertly curated business, location, and consumer datasets that can be quickly and easily linked to internal data to help improve risk assessment and pricing.

All this was the theme of  a panel discussion, hosted by Precisely and featuring panellists from AIG, Generali Global Corporate and Commercial, and Swiss Re. In the run up to the event, Intelligent Insurer caught up with Martin James, Senior Director–UK at Precisely, and Leona Deson, the firm’s Manager–UK Insurance, to learn more:

Why is it important for insurers to leverage new sources of external data?

Martin James: When insurers are building up their risk models, having data that is previously unavailable to enhance those models will enable them to maximise the potential for any underwriting they are doing, and to mitigate the risk of exposure. This enhanced level of context provides insurers with a competitive advantage by being able to better understand and predict their risk exposure.

What are the key external datasets that property insurers should be leveraging when assessing risk?

Leona Deson: We’re seeing a big increase in popularity for weather-related data. It’s about having the ability to look at historical weather patterns as well as the potential damage. This enables insurers to predict the likely impact of impending weather events with a high degree of accuracy, and also identify potentially fraudulent claims.

James: Exactly. There are two ways of looking at this: if I am to underwrite an insurance policy, using historical enrichment data to predict. If there is a claim, leveraging this data to prevent fraudulent claims.

Deson: The insurance industry is also leveraging the use of location intelligence, and this begins with: “where is this building or structure located?”. However, such a simple question can be difficult to answer accurately and consistently across the enterprise. The need for hyper-accurate geocoding and a persistent and unique identifier serves as an important first step in the process of identifying the exact location.

Then it is possible to layer enriched context to the location such as property attributes, points of interest, parcel boundaries, building information, dynamic weather, flood, and wildfire risk.

James: It’s about adjacencies: the proximity to a petrol station or a fire station are two opposite ends of the scale in terms of risk. Leveraging some of the enrichment data that Leona mentioned can have a great impact on the claim by providing a high level of accuracy as to where that property is located, eg, having emergency services nearby is a positive thing. We use mobility data too. If an area is busy from a traffic perspective, or not busy at all, when you are building up your models how will this negatively or positively impact your risk assessment?

You mentioned weather data there: how can insurers continue to write in loss-impacted areas, and work collaboratively with brokers and customers to support the insurability of climate change-related risks?

James: Using enrichment services, such as Precisely’s Dynamic Weather, insurers can identify what policies in force will or may be affected by a severe weather event and avoid untimely policy changes. The data permits them to proactively inform policyholders and brokers, when they know you were affected by the event, allowing insurers to provide high levels of customer experience while others are still in damage control mode.

The data can determine what policies will be impacted by a forecast event and have adjusters available for those that will be affected the most. Insurers can share highly detailed post-weather event data, which enables claims teams to remotely settle more claims with increased accuracy. They can also use the data to review and share recent past weather data within various territories.

Once insurers have identified new sources of external data, how do they ensure they are deriving joined-up insights?

James: Precisely streamlines the process of adding location context to business data by associating a unique and persistent identifier, called the PreciselyID, with every address. We all know how frequently postal addresses can change. New street names, alias street names, and postal/ZIP codes constantly evolve—introducing inaccuracies in your data. The PreciselyID is persistent and takes into account aliases and older addresses, mapping these addresses to the same, unique PreciselyID.

Using PreciselyID enables you to ensure that your data is reliable, updated, and consistent. It also gives insurers the ability to turn the data into insight, identify trends and to gain a more complete view of the asset risk—performing a quick and straightforward lookup of a PreciselyID returns a variety of data related to that specific geocoded point, including data on points of interest, property attributes, demographics, boundaries, and more.

What role can advanced data visualisation tools play in achieving more profitable underwriting?

James: Visualisation products are designed to assess the real-world exposure of critical infrastructure and property risk, drawing on a large variety of datasets to help paint a picture of exposure for insurers. To underwrite risks, you need the best answers to some key questions, including:

Visualisation tools can improve risk selection and enable high resolution modelling of risk frequency and severity and their relationship to each other, while giving a better understanding of portfolio level exposure.

For those that missed the recent panel discussion, what were the key takeaways that you’d like to share?

Deson: During the session we discussed how property teams can improve underwriting performance by better leveraging external data sources—helping to mitigate climate exposure, improve the accuracy of risk assessment and pricing, and how access to trusted data is fast becoming essential for high priority business initiatives such as ESG reporting.

James: Everyone is on a data journey, and if you’re not then you will be losing some of that competitive edge. It’s about making sure you’re able to source the right data, ensure it’s compliant, and connect it in the right way for the most valuable insights possible. We’re here to help insurers make more confident business decisions based on data they can trust.

Martin James took part in Intelligent Insurer’s panel discussion “Harness External Data to Transform the Profitability of your Property Book” and was joined by:

If you missed it  click here to view the session on demand.