Connect with us

INSURANCE

Simplifying home insurance through property characteristics data

Simplifying home insurance through property characteristics data 44

Simplifying home insurance through property characteristics data 45By Neill Slane, senior vertical market manager, U.K. and Ireland at LexisNexis Risk Solution

From 1st January 2022, the U.K. home insurance market began operating under new general insurance pricing rules[i] to ensure that at renewal, existing customers are priced the same as a new customer. These new regulations have provided an important opportunity for home insurance providers to review how they can better serve and price customers, using data enrichment to build a fuller understanding of their risk at application and quote.

Bringing in more data to make the process of buying home insurance easier and the assessment of risk more accurate has multiple benefits. It may not only assist in delivering fair and accurate pricing at renewal and new business but could also help evolve how home insurance is packaged and sold.

One of the biggest data enrichment innovations in home insurance, is the ability to assess individual properties not general postcodes, really digging deep into the characteristics of the building; from floor area to rebuild cost, heating type to listed status and use this highly granular data to understand any possible changes in the property risk at renewal.

Has a new bathroom, kitchen or bedroom been added?  Has an extension been built?  Has the property been sold in the past year?  Has this customer invested in a heat source pump? When viewed alongside environmental data down to the address level such as flood, subsidence and local crime rates plus data on the individual, the insurance provider is able to build a much more holistic view of the risk at quote. This not only helps to ensure the policy is right for the customer’s needs, therefore reducing the risk of under-insurance – or indeed over-insurance – but that the price is right and fair.

Home insurance providers cannot take loyalty for granted. In a study LexisNexis Risk Solutions conducted, 57% of homeowners[ii] shop around using aggregators and comparison sites every time their policy is up for renewal. Detailed, verified property data can really help insurance providers differentiate their offerings to individual customer segments and offer a more personalised quote.

For example, knowing up front that an applicant’s property is a listed building or that it has recently sold, the insurance provider is empowered to deliver a swift quote based on that knowledge. This can help build trust that the insurance provider understands the homeowner’s needs and that if a claim arises, there are no misunderstandings over how the risk was presented at quote. It makes sense that the more insurance providers can impress householders with individual insights into their own wants and needs from a home insurance policy, the more likely they are to feel engaged with their brand.

Going a step further, if an insurer provider understands that a customer has a rental property plus a home in which they reside, the potential opens up to offer a packaged product. Data enrichment solutions can help home insurance providers understand more about each property risk, helping to make the most of potentially untapped markets.

The fact that 11% of homeowners[iii] in our study rarely shop around at renewal, simply renewing each year with the same provider, simply underlines that insurance customers want simplicity. Whatever the insurance sector can do to reverse consumer perception that the process of changing home insurance provider is complicated, has got to be a positive step forward.

Some home insurance questions are easy to answer, others can be much less so, leading to estimates and guesstimates, creating a level of uncertainty and discomfort for the customer. Property data enrichment solutions like LexisNexis® Property Insights can be used to prefill applications in addition to supporting quotes. By automatically pre-populating application questions from 27 different data attributes, data entry required by customers is minimised. Prefilling data not only simplifies, but also substantially speeds up the quote process and reduces referrals. What’s more, by pre-populating the data, like year of build and rebuild cost, a quote can be delivered in confidence that the data is accurate, and in turn, the customer is reassured that their home insurance is fit for purpose.

Using highly granular property characteristics for individual addresses could be a common success factor in helping to provide fair, speedier and more accurate quotes for consumers. In the future, it could also help in building a more human-centred approach to insurance provision when used in tandem with past claims data to build an even deeper understanding of the risk of the property. For example, if an insurance provider knows the precise detail on a past claim for a burglary and at the same time, understands the exact number of rooms, how they are used, the parking space at the front of the house etc. they are in a far more informed position to ask questions about security, offer relevant advice and insurance cover tailored for the risk.

Those leading the charge in using data driven solutions to differentiate themselves in a brand-fickle market may be more likely to diversify and grow. Having the information at their fingertips to provide the personalised approach consumers want will benefit the market in delivering accurate quotes which will, in turn, help improve loss ratios and support profitability in the highly competitive home insurance market.

[i] https://www.fca.org.uk/publications/policy-statements/ps21-11-general-insurance-pricing-practices-amendments

[ii] https://risk.lexisnexis.co.uk/insights-resources/white-paper/a-clearer-view-of-claims-to-help-fight-frau

[iii] https://risk.lexisnexis.co.uk/insights-resources/white-paper/a-clearer-view-of-claims-to-help-fight-frau

Continue Reading
Editorial & Advertiser disclosure

Recommended