Q&A: Applying data strategies from motor insurance to home – LexisNexis Risk Solutions

What are the key challenges you see in the home insurance space right now?

Prior to the pandemic, the focus was on stemming losses related to escape of water and weather events while also delivering fair and competitive pricing. Those challenges remain. The ‘Beast from the East II’ this February brought ice, snow and the type of sub-zero temperatures that give rise to burst pipes and escape of water claims.

From a pricing perspective, when you consider more than one in three consumers (35%) have auto-renew set up on their home insurance policy according to our consumer study from 2018, the FCA’s proposed remedies to address the need for insurance firms to provide fair value to their customers and greater pricing transparency to the FCA will also be a major focus.

The pandemic has, however, brought new challenges. It has certainly altered the volume and severity of claims the market is seeing. Looking at ABI [Association of British Insurers] data for household claims in 2020, up to the end of Q3, the overall trend is that claims volumes are down – even accidental damage claims are on par with 2019, despite home schooling, home working, home workouts for sustained periods during the year. It’s not rocket science to work out why – burglaries tend not to happen in occupied homes and if you are home you will spot a water leak or fire before it has time to do its worst.

On the face of it, this is good news for the market (one estimate suggests £368m will be saved by the market during 2021), but insurance providers will need to think carefully about how they price if some of the changes, for example in home working, become permanent. This could impact claims longer term.

The additional factor to consider is pressure on household finances and how that might alter how people feel about home insurance. Will they wonder about the benefit of cover from theft for example, if they are at home for the foreseeable future? Also, less disposable income and higher unemployment may lead to more focus on price and switching activity. Again, in our 2018 study looking at the buying behaviour of home insurance customers, 66% thought it was acceptable to manipulate information given to price comparison websites to get a lower home quote.

Leading on from this is the risk of ‘recession-induced’ claims fraud as already highlighted in Insurance Times. The ABI’s annual insurance fraud figures showed that there were 27,000 dishonest property claims, worth £124m in 2019, up 30% on the previous year and that was before the pandemic.

From a longer-term perspective, the market is very conscious of the changing expectations of customers and how insurance needs to evolve to keep pace or risk losing out to new market entrants. Is it enough still to simply sell and policy and cover a claim? We’re seeing insurance providers doing more to support customers throughout the lifetime of the policy, understanding that this approach can actually help mitigate risks. Data from the IoT will pay a big part in this evolution of the home insurance space.

How can data, analytics and technology help solve some of these challenges?

Many home insurance providers hold valuable customer data that they struggle to maximise because it might be held in different silos or is held in multiple formats. Linking and matching that data to create a single customer view is a great place to start. You can then create one ID for that customer reflecting all touchpoints they have had with your business – this has multiple benefits not least in helping to identify cross-sell and upsell opportunities and in helping to communicate the benefits of home cover at the right time.

At application, data can make the process as simple as possible for the customer, so for example, using property characteristics data to prefill some of the information that they might take a guess at. This benefits the insurance prover too of course as they can then quote based on pre-validated information along with further detail provided by the customer.

With regard to pricing, it’s all about having the most accurate view of the customer, the property and the location in which it sits, at the point of quote. That’s potentially a large number of data points to assimilate at speed so streamlined access to this broad range of data, from one access point is key. 59% of UK homeowners shop around at the time their policy is up for renewal to get the best value for money based on our study so with finances likely to be tight for a lot of individuals, it is up to the insurance provider to use data in order to provide a price that is accurate for the individual and will cover their needs.

Perils data is already widely used by the market but it’s only recently that it’s been possible to access this data at the point of quote alongside 42 further data enrichment sources to achieve that immediate view of risk through the LexisNexis Informed Quotes data hub. This has been a big step change in automating and speeding up the quotation process. For the claims professional this is also good news as this data could be utilised at point of claim to help inform claims treatment strategies.

Mapping tools and near-real-time data direct from the Environment Agency are putting the market in a much stronger position to price and help mitigate environmental risks. Geospatial data visualisation tools that drill right down to an individual address or give an instant view of a flood or storm’s potential impact across a whole portfolio offer crucial visibility over which policy holders will be impacted, helping insurance providers mitigate their losses by getting on the front foot to alert those set to be affected and plan resources.

Doing the right thing for customers, treating them fairly based on their individual risk – not the risk of their next door neighbour, or the general risk of their postcode - comes down to having the right data at your fingertips to create the most accurate view at point of quote, underwriting and claim.

Are there data strategies that have worked well in motor insurance that could apply in home?

In motor insurance, the No Claims Discount (NCD) database validates and automates what was once a lengthy process to confirm proof of NCD. In home, we could see a similar approach taken with Property Flood Resilience measures recorded for access by the market to help support the quote process.

Claims history data is part of the quote process in motor and policy history data gathered from across the market is bringing new insights to insurance providers such as cancellations and gaps in cover and soon, insights directly related to the first pandemic lockdown in the UK. 

Added to this, buying behaviour data gathered from across the market can reveal switching behaviour and identify potential cases of fraud.

Again, these types of data sharing initiatives could work equally well in the home market - the first priority being claims.

What’s the next big data innovation to support the home insurance market?

You guessed it - claims history data contributed by the market – early contributors to this database are already on board. Access to a market-wide pool of claims history data will bring a much greater level of understanding on the nature of prior claims – the circumstances, the settlement, the parties involved and much more. This will provide the opportunity to do a deeper dive into the data to understand more about claims losses in household and the predictive factors for those losses.

With access to more detailed claims data than ever before, the market has an opportunity to create more granular pricing segmentation. Going beyond merely validating whether the individual had a prior claim and assuming this deems them a higher risk, this data will allow the insurance provider to understand the type of claim, when it occurred and the settled amount. This could help individuals, even those with claims history, to benefit from fairer, more accurate pricing.

Home claims data that goes deeper and wider can also be used at point of claim, providing valuable context for claims management professionals. For example, it can help the claims team assess whether an accident or break-in aligns with what has happened in the past or appears to be a ‘one off’ incident, helping to reduce referrals to fraud teams and in-turn speeding up claims processing times – and subsequently delivering a great customer experience at a key point in the customer journey.

Of course, it’s not just data on accidental damage and theft claims that will be valuable, prior claims for the property also needs to be considered. For example, if you can see that a property has a history of claims for escape of water or damage caused by flooding the picture of risk builds further and even opens up the potential to develop new services and solutions to help policyholders mitigate those risks.

Claims data will be most powerful when used in combination with public records data, perils data and quote history data to create a holistic view of the person and the property, to deliver appropriate premiums reducing the risk of being under-insured and helping to reduce the market’s potential exposure to claims fraud. As home insurance providers and their claims teams strive to understand more about the risk at quote and more about the policyholder at claim, in a post Covid-19 world, market-wide claims data could be their ally. 

How much potential does data from the IoT hold for pricing, renewals, claims in home insurance and what are the hurdles that need to be overcome?

 We do see very positive trends in the application for some Internet of Things (IoT) devices. In the US we have partnered with several home IoT device manufacturers and to validate reductions in home insurance claims – both the frequency and severity due to the presence of the device.

Also, with more people at home during the pandemic, we’ve seen the severity of home insurance claims reduce. For example, if an IoT escape of water alarm goes off, being there in person to shut off the water can stop an insurance claim becoming very expensive.

 However, gathering enough performance or claims data to validate the value of any individual IoT device is an ongoing challenge. It takes time for a device to become widely distributed and the data centrally collected.

Once that happens, we do expect to see a need to standardise and normalise the data collected by the many different devices that are in the marketplace today. The good news is we have been in this business with telematics devices for many years and we have extensive experience creating device generated attributes and scores.