Anecdotally it’s well known by home insurance providers. It can be in the form of a window unintentionally left open, accidental damage to an expensive carpet, failing to insulate water pipes for winter, or failing to keep up with reasonable property maintenance: an individual’s behaviour contributes to the property risk, over and above the usual perils of wind, rain, water or other damage.

Whether they know it or not, lying in a home insurance application can be costly for the consumer, and to an increasing degree insurers have the tools to know more about the policyholder – and other individuals living in the property – to determine various factors for certain types of risk.

So interestingly, when we surveyed UK insurers recently on how they perceive the behavioural risk from individuals in the home, there is a wide polarisation between those insurers who already use this type of additional data on property occupants other than the policyholder (and who see value in it) and those who do not (and who perceive only a little value).

Our recent research* amongst 77 leading home insurance providers found that almost six in ten of them (57%) perceive that an individual’s behaviour is a factor, to some extent,  in driving home claims. Only 14% of insurers believe that it’s not a factor.

Most insurers perceive that there is opportunity to better understand behavioural risk, and that majority sees the value in doing so.

In another recent blog article, we called it the ‘four Ps’ of property insurance: people, property, place and prior claims. Home insurance providers are used to ingesting rich data sources on some of these risks, such as property and place, but there are clear information gaps when looking at the other elements of people and prior claims.

It appears that what we could call industry inertia and lack of awareness, lack of access to data on individuals beyond the policyholder – and technology limitations – are barriers to ingesting additional data on more people such as credit, custom insurance risk scores, and past bankruptcies.

Wide range of views on ‘people risk’

When we asked insurers about whether they see value in applying this type of risk data to property occupants outside of the policyholder, specifically to assess and price risk at point-of-quote, it uncovered a wide range of views.

  • 20% of home insurers who already use credit say credit data on other home occupants is extremely valuable (this figure is nil for home insurers who don’t use credit)
  • 49% of home insurers who already use credit say credit data on other home occupants is very valuable (this figure is 6% for home insurers who don’t use credit)
  • 22% of home insurers who already use credit say credit data on other home occupants is somewhat valuable (this figure is 59% for home insurers who don’t use credit)
  • 9% of home insurers who already use credit say credit data on other home occupants is not very or not all valuable (this figure is 35% for home insurers who don’t use credit)

Overall, public records was perceived as having slightly more business value than credit data.

  • 31% of home insurers who already use public records say public records data on other home occupants is extremely valuable (this figure is 2% for home insurers who don’t use public records)
  • 34% of home insurers who already use public records say public records data on other home occupants is very valuable (this figure is 18% for home insurers who don’t use public records)
  • 35% of home insurers who already use public records say public records data on other home occupants is somewhat valuable (this figure is 51% for home insurers who don’t use public records)
  • No home insurers who already use public records say public records data on other home occupants is not very or not at all valuable (this figure is 29% for home insurers who don’t use public records).

In parallel to this, publicity related to GDPR has created more awareness amongst consumers, and especially younger consumers, that by sharing their information they can receive better, and more personalised, services in return.

survey from You Gov conducted just prior to ‘GDPR day’ found that 72% of people hadn’t heard about GDPR specifically, although 40% were aware of media reports about “changes in the data rights of individuals.”

Most important data sources for ‘people risk’ according to home insurers

Credit, custom insurance risk scores and past bankruptcies are considered to be the most valuable types of information about an individual/homeowner for pricing at point-of-quote.

  • Credit: 62%
  • Custom insurance risk score 60%
  • Past bankruptcies 60%
  • CCJs 42%
  • Edited electoral roll data 29%

Note: Percentage shows the segment of home insurers rating these datasets as extremely or very valuable.
Source: LexisNexis Risk Solutions

70% of home insurers want a better understanding of behavioural risk

  • 36% of home insurers say a better understanding of an individual’s behavioural risk would be extremely valuable
  • 34% of home insurers say a better understanding of an individual’s behavioural risk would be somewhat valuable
  • 26% of home insurers say a better understanding of an individual’s behavioural risk would be not very valuable
  • 4% of home insurers say a better understanding of an individual’s behavioural risk would be not at all valuable.

Appetite for change

An industry-wide view of all the property occupants related to single properties, or across the entire property portfolio for an insurer, would bring a new dimension to the understanding of risk exposure.

It’s interesting to note from this research that the perceived value of the data on other home occupants is being driven by those insurers who already have experience with using this data source. Knowing the risk is a great place to start. Beyond that we can look to the future and see factors like multiple occupancy and configuration of the household being used as some interesting modelling tools and techniques.

For more insights from these research results download the LexisNexis Risk Solutions white paper ‘Application Acceleration: Prefill and Automation’.

*LexisNexis Risk Solutions carried out an anonymous survey, the UK Home Insurers Study, 14 November – 13 December 2017. Data collection: A mixed mode of web and telephone survey was used to collect data. To qualify, respondents were screened to meet the following criteria: Currently employed in the insurance industry (direct insurers, indirect insurers, brokers, and MGAs) with responsibilities related to underwriting or pricing. The sample was 77 home insurers who spend at least 30% of their time working in the personal home insurance sector.

Follow the link to the LexisNexis Risk Solutions website to find out more about how we support insurers or for information about LexisNexis Home Prefill.

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