How data is transforming home insurance for a Covid-19 world

Data enrichment and sourcing new datasets is going to be of vital importance to home insurers and brokers as customer behaviour – and therefore claims – changes to reflect the fluctuating role of the home against a Covid-19 backdrop.

Due to the national lockdown, imposed in March by prime minister Boris Johnson to mitigate the spread of coronavirus, policyholders’ homes have morphed to also act as workplaces, schools and gyms to name a few.

This has had a knock-on effect on home insurance claims, said Neill Slane, senior vertical market manager, UK and Ireland at LexisNexis Risk Solutions. He continued: “Some of the behaviour patterns that we see traditionally in home insurance have been extenuated by Covid.” This includes, for example, more accidental damage claims where children home from school have been left unsupervised for longer periods of time where parents are attempting to work from home. Equally, theft and crime-based claims have decreased as more properties have been occupied since the introduction of lockdown and shielding.

In turn, this change in policyholder behaviour and the activities conducted at home presents a pricing challenge for insurers operating in this sector. Slane explained: “The difficulty those insurers are going to have now is they’ve priced on the basis potentially six months ago, nine months ago and that dynamic [has] changed.

“Because the behaviour and interaction with the property’s changed, is the insurance that they’re providing and the cover they provide, is it still appropriate? If they knew when they wrote the policy what they knew now, would they change that? And that’s something we believe our clients are going to be looking at. They’re already looking at it.”

Data solution

For Slane, the key in tackling this knowledge gap and potential pricing mismatch is the use of data and, in particular, data enrichment, which he said “is starting to become a much more industry standard” in the UK insurance market.

“Insurers now really want to try and get around these challenges by trying to identify data sources which can give them a 360-degree view of the risk of an individual,” he added.

“What the broker or insurer needs is a range of information, so we need pieces of information on the individual’s financial history, whether that individual might be on any fraud registers and then we also need to look at the actual property to understand what the risk is there – how many bedrooms? How many people are living at it? Is it an old one, new one, etc?

“There’s also the information you need to garner from the consumer themselves, so what are the big-ticket items you want to have covered by your contents? Things like a particularly expensive PC, or a TV, or jewellery, etc. So, there’s lots of information that has to be gathered and garnered to be able to provide that 360-degree view. I include the claims piece in that as well.”

LexisNexis Risk Solutions uses four main prongs for its data collection, said Slane. This includes public credit data to identify an individual’s financial risk, analysing the property’s attributes to validate policyholders’ details (for example, whether the property is a house or a flat), checking the prior claims history of the policyholder and the property, as well as studying the applicable perils, such as flood, subsidence, fire risks, distance from a fire station or police station, crime and arson rates. Geospatial data forms part of this analysis too.

Richard Tomlinson, managing director at data enrichment firm Percayso Inform, agreed with this data-led approach. He added: “The current pandemic has led to major and dramatic changes to many consumers’ financial circumstances as a result of the furlough scheme, reduction of income and even loss of employment. It is therefore crucial that insurance providers are able to access accurate and up to date consumer financial information.

“The ability to access both public and private credit bureaux data in addition to using open banking data to inform insurance pricing enables a clear view of shifts in a customer’s lifestyle, spending patterns and financial status. With this ability an insurer may identify major changes in circumstances and also opportunities to cross-sell or upsell to make sure the customer has the right level of cover and, of course, price a policy more accurately.

“Reviewing these records could show that a customer has bought more gadgets, such as home office equipment, over the past few months or whether they’re spending on home improvements. If their current policy has certain limits, they may need to top it up. Are they spending on dog food all of a sudden? There could be an opportunity to sell pet cover.”

Open banking further presents an opportunity for real-time data collection, Tomlinson said, presenting a different view from insurance’s typical “historic and likely outdated picture”.

This foundation of pulling together different data sources is defined as data science or data analysis, Slane continued – this forms the basis of data enrichment, which “is where all that work comes to fruition and in a sub-second speed, the pricing model goes ahead, comes up with the premium for the individual and they get that price shown on the screen”.

The use of claims history data here could be of particular benefit to brokers. Slane explained: “The brokers want to give the best experience to their customer, so their business model is very much a case of giving the right policy to their customer and potentially making subsequent sales off that positive experience they’re giving.

“The claims data can be particularly useful there because the broker business model relies on that relationship. That relationship can benefit from knowing what’s happening with your customer. So, if you’re customer’s going through a particularly stressful time, it enables the broker to manage the relationship with the customers a bit better; it certainly builds up that relationship, that goodwill and that’s where the broker market really excels at what they do.”

Fraud mitigation

As the government’s furlough scheme reaches its autumnal conclusion, Slane further believes that financial hardship-related fraud will become more prevalent, whether this is in the form of fraudulent or exaggerated claims, or via application fraud – for example, potential policyholders may tweak their details on aggregator websites in order to get a cheaper home insurance premium.

“We carried out some market research in the last year and 66% of [home insurance] buyers think it’s acceptable to manipulate [their] information to get a lower home quote,” he added.

Tomlinson agreed that financial stress is a dominant driver of fraud in today’s economic climate. He said: “The increased financial pressure that Covid has already placed on some households could lead to an increase in exaggerated or fraudulent claims given the clear correlation between financial stress, claim risk and fraud.

“With economists predicting a recession with a significant number of job losses on the horizon, getting an up to date and accurate picture of customers’ financial circumstances is crucial to understanding risk and setting premiums at the right level.”

With this in mind, past claims data can prove useful in demonstrating whether a policyholder is amending key fields in their quotes or if they have changed the frequency of their quotes – this can pinpoint potential fraudulent behaviours.

However, Slane added that insurers should use data to explore the affordability of their cover in light of Covid-19’s financial implications. He said: “With financial hardship coming to the fore, we should be looking at the affordability assessment on that individual, so there’s data sources which look at the affordability check, which check that that individual can afford their monthly repayments and that is available at point of quote.”