INFORMATION EXCHANGE PREDICTING THE RISK OF CANCELLATION
Motor insurance policy cancellations in the personal lines market represent a huge cost to insurance providers. When a customer decides to cancel their policy – for whatever reason the insurance provider loses marketing and administration costs, as well as potential aggregator fees. Also, many insurance policies are paid for using direct debit- so if a customer cancels their instruction too early, the insurance provider is also faced with a bad debt that may be difficult to recover. This isn’t a small number of cases. In the last year alone, there were 1.3 million new business cancellations. At LexisNexis Risk Solutions we estimate this all mounts up to between £25 and £75 in cost per policy cancelled. Scaling this up to an insurance provider with 100,000 policies on their books, a cancellation rate of around 5% per year, for example, equates to a substantial loss of anything from £125,000 to £375,000 per annum to one business. A lost customer is also a loss in potential lifetime value (LTV) through future potential renewals as well as cross selling opportunities. Predicting the likelihood of cancellation prior to policy inception has been nigh on impossible for the sector up to now. Aside from the fact that a new customer is not required to state if they have decided to cancel a policy in the past during the application process, there has been no firm evidence to show history repeats itself and that people who cancel tend to do so again and again. But risk of cancellation isn’t the only risk to be considered – are people who choose to cancel more likely to claim or commit fraud? These questions can now be answered through a market wide contributory database of motor policy history data combined with the science of predictive data analytics. The LexisNexis® Motor Policy History database now comprises policy data from over 94% of the personal lines motor insurance market. Detailed retrospective analysis of a substantial proportion of this data set has confirmed that past cancellations do indeed correlate to future cancellation risk. If an individual has ended a policy prematurely in the past, they are more likely to do so in
future. In fact, the more policies they have cancelled, the more likely they are to repeat the behaviour. Looking at claims history in conjunction with policy history, these individuals present an average 70% higher claims cost than someone with no prior policy cancellation . Gaps in cover also indicate higher risk of cancellation – the more gaps between policies, the higher the risk of mid-term cancellation. Our analysis identified that between 10% and 30% of the customers currently on insurance providers’ books have had a cancellation in the past. Five million current policyholders have cancelled an insurance policy within the past five years; 1.4 million have cancelled two policies . The timing of the cancellation is also a valuable insight for the sector. When someone purchases a policy to start the same day, they are twice as likely to cancel early. This is more than double the average. This is hugely valuable insight for insurance providers, allowing them to price for the risk of cancellation based on policy commencement date. And whilst you might expect most cancellations to occur within the cooling-off period, only 15% were cancelled in the first two weeks, 37% within 16-100 days and a substantial 48% 101-364 days after purchasing the policy . Past cancellations can also indicate a higher risk of claims and fraud. Those with a current CCJ present are 64% more likely to cancel, and people who have attempted fronting at point of quote are twice as likely to cancel. Finally, we deduced that the less historical information there is available on the individual, the higher the risk of a future cancellation. Whilst this group may not represent a higher risk in terms of credit or claims, they do have an increased risk of cancellations. This perceptive new insight into cancellations is possible through data science and industry collaboration designed to gain a broader, more meaningful view of the customer. Insurance providers can now understand where the cancellation risk lies within their own customer base. They can then use this intelligence to help set new customer pricing strategies and offer payment terms based on the customer’s perceived cancellation risk.
• Source – analysis of a substantial proportion of the LexisNexis® Motor Policy History database which comprises data from 94% of the motor insurance market • Source – analysis of a substantial proportion of the LexisNexis® Motor Policy History database which comprises data from 94% of the motor insurance market • Source – analysis of a substantial proportion of the LexisNexis® Motor Policy History database which comprises data from 94% of the motor insurance market • Source – analysis of a substantial proportion of the LexisNexis® Motor Policy History database which comprises data from 94% of the motor insurance market • Source – analysis of a substantial proportion of the LexisNexis® Motor Policy History database which comprises data from 94% of the motor insurance market
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by Stuart Goldsmith, Product Manager at LexisNexis Risk Solutions, UK & Ireland