Jay Borkakoti, Director of home insurance, UK and Ireland, at LexisNexis Risk Solutions
As the use of perils data has become ‘the norm’, risk analysis in home needs to move on to the next level to help insurance providers differentiate their offerings and improve performance. Data attributes based on property characteristics, and more granular details of past claims could provide the answer.
To date, home insurance providers have struggled to verify the characteristics of a property, relying instead on estimates and assumptions a homeowner may provide. It’s fair to say that most homeowners are not property experts so don’t necessarily know the answer to some questions posed in the insurance application process. Others may not even be living in the property if they are in the midst of the purchase. Furthermore, facts about a property just aren’t commonly verified such as the parking or floor space which might help the insurance provider improve its risk assessment.
Proper property perspectives
Another problem that can significantly affect the home insurance underwriting and quotation process is the individual applicant’s view of the property. For example, a couple obtains a quote for a new policy- however, one may count the kitchen in the number of reception rooms, one may include the downstairs WC in the number of bathrooms and also count the smallest bedroom as a study. The result is very different-looking perceptions of the property.
Data Prefill is already enhancing online applications and delivering a smoother customer experience, whilst also improving accuracy of pricing. However, point-of-quote enrichment, using property attribute data, allows an insurance provider to rate and price without having to ask the questions homeowners may struggle to answer, or at the very least validate that the answers provided by the customer are correct.
The good news is that much of the data required for this pricing has been collected and is available today. As it’s been gathered by various organisations for vastly differing purposes, the challenge lies in amalgamating, cleansing and normalising the data. Keeping the data up-to-date is also a consideration – properties change, are updated and extended, so the dataset is constantly evolving. However, this work is underway and insurers now have access to property characteristics such as number and type of rooms, heating type, listed status and square footage at point of quote, completing gaps in knowledge that they may not have considered previously.
Proximity data and mapping claims
Some of these elements are more valuable than others but combining more than one attribute can provide invaluable insights.
Another element to be considered is data from surrounding properties. Whilst one home may present a very low risk, looking at claims from the neighbouring properties can change an insurance provider’s view. Real-time proximity data can show more about where the property sits, the context and the nuances around the property. Based on the address, the insurance provider can gain an overview of the risk based on perils data, location of trees, crime data, and distance to emergency services for example. Adding previous claims data sourced from a market wide contributory database related to both the property and the policyholder can build a fuller picture of risk.
This wider view helps insurance providers to determine their underwriting strategies based on specific geographies and better monitor exposure to risk.
Claims data
Ultimately, home insurance providers need access to deeper data derived insights on the home to be insured, and on the homeowner to deliver quotes appropriate to both the personal and the property risk with minimal reliance on the customer to provide the information. New data around property characteristics is the first element in building this capability. The next stage is integrating granular past claims data again on both the property and the person, contributed and accessed by the entire market through the creation of a contributory claims database for home.
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