• chrissly1

The Power of Contributory Data in Pricing and Underwriting

Carla McDonald, director of product management, claims, LexisNexis Risk Solutions U.K. and Ireland


A crystal ball would have its obvious benefits but for underwriters and pricing professionals charged with the responsibility of assessing the risk of a new policy at the point of quote, industry contributed data on policies, quotes and claims could be the next best thing.


LexisNexis Risk Solutions data scientists have been crunching the statistics of a shared motor policy history database since 2014. Initially created to validate No Claims Discount (NCD) in a quick, digitised way, this database now contains over 27.4 million active motor policies - that’s over 97% of motor policies in the UK – and 86% of the motor insurance market is sharing their data and receiving insights in return. The more data that is shared, the more predictive power it can offer participating insurance providers.


Policy history data now offers the ability to identify at the point of quote, who is more likely to cancel their policy or make a claim as well as how much that claim is likely to cost. For example, based on analysis of motor policy history data against claims experience, it was found that people with a gap in cover in the previous year had up to a 50% higher loss cost than those that didn’t[i]. We have also found that past cancellations can equate to a 70%[ii] higher loss cost relativity and an individual with multiple NCDs at any one time has a third higher loss cost relativity.


As well as understanding risk, policy history data can provide valuable context for an individual’s policy behaviour. Perhaps the best example is how it can help to identify those cancellations and gaps in cover that occurred during the national COVID-19 lockdowns, versus those outside of that time. This helps ensure that those individuals who chose to cancel their motor insurance or allowed their insurance to lapse during a COVID-19 lockdown period are priced more fairly when they next shop for insurance, renew their policy or make a mid-term adjustment.


Quote behaviour also offers a fresh perspective on risk. A database that connects and compares thousands of motor insurance quotes from the motor market, gathered over the last six years can now help insurance providers understand named driver risks and in particular whether the named driver is potentially the main user of a vehicle, which could indicate that the proposer could be ‘fronting’ the policy for the named driver.


The next evolution in contributory data will focus on claims data. In the same way data on policy history, quote history and No Claims Discount history is shared by the motor market, highly granular claims data gathered from across the market will soon allow home and motor insurance providers to validate claims history at point of application and quote and build a very detailed picture of risk for new business and renewal pricing.


Fraud remains a significant challenge for the insurance market – both at application and claim. In motor, the value of application fraud more than doubled in a year in 2021 and in home the suspected value of claims fraud in 2020 exceeded any other year since 2014[iii].


It’s little surprise that shared claims data has become a high priority for the market. It can help to improve pricing accuracy, smooth the claims process and tackle the perennial issue of claims not being declared at application[iv] – whether by intent or in error – as well as help identify incidences of organised and opportunistic fraud.


At point of claim, granular data on prior claims may also offer that all important context. It may tell an insurance provider if a new claim for a customer onboarded within the past twelve months is the first in the past seven years or the latest in a series of claims made with different providers that create a pattern.


Data sharing to improve financial decisions and reduce risk is by no means a new concept. What is new is the insights being derived from the years of insurance specific data that has now been accumulated and the potential this offers to help ensure individuals are offered products that are appropriate for their needs at a fair price.


In home insurance, the 5 top largest insurance providers hold 65% market share while in motor insurance, the top 5 largest providers hold 54% market share[v] – this means even the biggest insurance providers may only have a market share of 10% or perhaps 15%. This leaves at least 85% of the market unknown to them. By contributing data and accessing market wide data by return, even the largest providers have much to gain, particularly in the absence of that crystal ball.


Ends

[i] This article contains results of analysis carried out by LexisNexis Risk Solutions UK Limited on available data within the LexisNexis® Motor Policy History database. The analysis was completed within a fixed period and does not purport to represent the results of any identifiable customers. The statistical analysis reported is provided “as is”, nothing arising from the data should be taken to constitute the advice or recommendation of LexisNexis Risk Solutions. [ii] This article contains results of analysis carried out by LexisNexis Risk Solutions UK Limited on available data within the LexisNexis® Motor Policy History database. The analysis was completed within a fixed period and does not purport to represent the results of any identifiable customers. The statistical analysis reported is provided “as is”, nothing arising from the data should be taken to constitute the advice or recommendation of LexisNexis Risk Solutions. [iii] Association of British insurers, abi.org.uk/Insurance-and-savings/Industry-data [iv] https://www.abi.org.uk/news/news-articles/2020/09/detected-insurance-fraud/ [v] Association of British insurers, abi.org.uk/Insurance-and-savings/Industry-data

38 views0 comments