Articles - If walls could talk unlocking the value of home claims data


It has a been a busy period for the property market and in turn, the home insurance sector. The different dynamics impacting insurance providers, from the changes in pricing rules to the constant threat of extreme weather have underlined how vital it has become for the home insurance market to fully assess risk at the point of quote. While the data available to assess risk has grown significantly in recent years, there is a still a big data gap to be filled. That gap is past claims data.

 By Carla McDonald, Product Director, Claims, LexisNexis Risk Solutions

 The volume of property transactions in 2021 peaked in March, June and September as the relaxation in stamp duty came to an end in England and Northern Ireland and the UK average house price reached a record level of £271,000 in September 2021 with the demand for property outstripping supply.

 But while home-moving tends to trigger shopping and switching activity in home insurance, the volume of policies underwritten has not seen growth so the whole of the market continues to fish in the same pool. Against this backdrop, ABI data shows insurance providers are facing rising claims costs in home. The number of home insurance claims from weather losses increased 89% in 2020 vs 2019 and 2020 saw £266m in gross flood claims, the highest figure since 2015. This means that while property premiums have remained relatively static in recent years, they are now at their highest since 2013 .

 The risk factors used to assess home insurance risk have traditionally focused on the person, the property and the peril such as flood, fire, crime or subsidence. Over time, the level of detail available to the market for each of these three ‘p’s’ has expanded to vastly improve the assessment of risk, right down to the individual address for more accurate pricing and to support the claims process. However, a key element in the total picture that has remained elusive to the home insurance market has been detailed information on any historical claims related to the property and/or the person.

 Insurance providers have had no way of knowing if a property new to their book has suffered a series of burglary claims prior to the policyholder’s tenure for example or suffered damage due to a flood or storm. If a consumer has only just purchased a property, they can’t really be expected to know any past claims for damage to their new home. Even if they had a property survey before purchase which revealed a past problem, they are unlikely to be able to establish if this resulted in a claim.

 It has also been difficult for insurance providers to verify claims history for new applicants, stated as part of the application process, leaving the market exposed to fraud and consumers at risk of underinsurance.

 We have previously reported on our research of consumer attitudes to home insurance which found two in three consumers think it is acceptable to manipulate the information they provide when using price-comparison websites in order to get a lower quote for home insurance. Typically, application fraud will involve mis-stating prior insurance claims . The obvious risk is that a claim is repudiated, and the policy made void when it becomes evident that a previous claim was not declared at application. In some cases, this will be a genuine mistake on the part of the customer, only becoming apparent at claim – the worst possible time. It's been a conundrum with the need for a solution made more urgent by the new pricing rules . We also know that between 2018 and 2019, suspected volume of fraud in home insurance rose 826% and in 2020 the suspected value of home claims fraud was £45,426. While this was lower than 2019, it exceeded any other year since 2014 .

 The solution lies in contributory data. In the same way the market shares data on policy history, quote history and No Claims Discount history, highly granular claims data gathered from across the market for the past seven years will soon allow insurance providers to build a very detailed picture of risk for new business and renewal pricing. This insight may also to help ensure products are suited to the customers’ needs. Granular claims data is set to be available in three main categories: person claims history, property claims history, and person and property claims combined, for use at point of quote, renewal and claim.

 At launch, the market will benefit from a point-of-quote rating functionality on prior claims history by validating customer declarations. As market coverage of the claims database grows, it will provide almost real-time information on claims specifically at the proposed property, regardless of the policyholder or claimant. Access to market-wide claims data will also allow insurance providers to remove the claims declaration within the insurance application, further speeding the process for customers and improving data accuracy.

 Insurance providers may often think ‘if only walls could talk’ when assessing the risk of a domestic property. Through market-wide contributory claims data, that notion gets closer to reality.

 
  

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