Next-Gen Property Risk Data and Analytics

Most property risk models rely heavily on ZIP code. Yet, technology and data exist today to evaluate more than 1,000 risk data points for every single property in the U.S.

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The future landscape of the P&C insurance market is being largely determined by the technology decisions companies make today. Is your business keeping pace with the rapid advances in property risk data and analytics?

The digitization of core processes, the increasing migration to the cloud and the explosion of insurtech companies has set a foundation for a new generation of property risk data and analytics. The accessibility and quality of property data has exploded – and simultaneously become drastically more cost-effective. The ability to store or access such data via the cloud and the ready availability of APIs to connect to sources of data has translated into an explosion of data that can be instantly delivered and leveraged by underwriters. 

Still, many insurers are operating in the past. To illustrate, consider that 30% of legacy systems do not have data on the location of a property’s nearest fire station or fire hydrant. Yet, that data is readily available and has a significant impact on the estimated extent of potential fire damage to a property. Or consider that lightning risk is often not considered in most property risk evaluations, yet the data for that $1 billion claims category is also readily available. 

Most current property risk models rely heavily on data and evaluations based on a property’s ZIP code, a practice that dates back to the 1980s. Yet, technology and data exist today to evaluate more than 1,000 risk data points for every single property in the U.S. And these geospatial hazard ratings are much more comprehensive and precise than the current zip code and census block-based practices.

In a recent study, the global consulting firm McKinsey reported that best-in-class insurers are “putting distance between themselves and competitors” by applying advanced data and analytics in underwriting. They cite these insurers reducing loss ratios by three to five points, increasing new business premiums by 10% to 15%, and improving retention by 5% to 10%.

See also: Biggest Operational Risks of 2022

As the McKinsey report states, “external data is the fuel that can ignite the value of analytics.” By leveraging next-generation data and analytics, insurers can gain deeper insight into risks across the insurance lifecycle, from risk selection to pricing: 

Risk Selection - Through access to internal data and integrating the right mix of external data, and then integrating that data seamlessly into the risk-selection process, insurers can better screen applicants. Insurers can classify applicants using risk models based on their underwriting principles to determine whether to cover or renew a client – and determine the amount of premium they should offer to that client. With next-generation data and analytics an insurer can select good risks and avoid the risk they do not want to underwrite. Of course, the idea is not to eliminate losses completely but to eliminate highly identifiable and highly probable losses. 

Prefill - Another point of the customer journey that can be made significantly more efficient and effective using the right next-generation data is in the interview and screening process. Using traditional data systems, the screening and interview process can be cumbersome, but with next-generation data integration, you can match and prefill data for prospects and customers quickly and inexpensively. Minimizing the number of questions, you need to ask a potential customer or client can dramatically help speed and smooth the screening and sales process.

Pricing - With quick access to and integration of the right internal data, and cross-analysis with a broad array of external data, insurers can more effectively make their case to regulators on pricing – and can more accurately and appropriately price policies to reflect the actual inherent risk. With most current systems, a property owner in a ZIP code with an F wildfire rating, perhaps a home in an urban area of that ZIP code, likely pays the same premium as a home that is actually in peril of a wildfire due to its proximity to forests/wild areas with dry brush. If a customer lives in a significantly more fire-prone property, they should be paying more than those in a low-risk property.

Targeted Marketing - Marketing is really one of the almost untouched or greenfield areas where insurers have yet to apply advanced data and analytics. Being a leader in the application of next-generation data in marketing can really make a competitive difference, particularly for small and medium-sized insurers. Marketing should really be seen as the starting point of risk selection. If you are smarter and more targeted about whom you market to, you are going to be able to produce stronger, less risky and more profitable leads.

There is tremendous value in closely integrating greater property risk data and analytics into the underwriting process to drive greater insights. And the time for updating your property risk data and analytics is now. Being ahead of the curve can be a real competitive advantage.

John Siegman

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John Siegman

John Siegman is the co-founder of Hazard Hub, a property risk data company that was acquired by Guidewire in mid-2021. He is now a senior executive at Guidewire helping to lead the direction of the HazardHub solution and guiding P&C insurance clients in innovating their data integration into critical processes.


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