Articles - Predict cancellations and claims using policy history


The use of industry contributed motor policy history data in insurance risk assessment is not a new concept. The market has been accessing No Claims Discount data at the point of quote for the past six years – vastly improving the validation process for insurance providers and their customers and identifying any misstatements in entitlement.

 By Martyn Mathews, Snr Director, Personal Lines, LexisNexis Risk Solutions
 
 However, it is only recently that the breadth and depth of policy history data shared by the market has reached the critical mass to enable a much wider understanding of risk related to an individual’s prior car insurance history.

 Today, over 80% of the motor insurance market – insurers, brokers, MGAs - are sharing policy history data, creating a comprehensive record going back looking at years of key events in the lifetime of a policy. The value of this data goes beyond operational savings and is now proven to help address application fraud, understand claims risk and the possibility of future cancellations to assist understanding and pricing at point of quote.

 In analysing the data held today, there are immediate opportunities to refine pricing, retention and underwriting strategies based on an individual’s previous cancellations, NCD entitlement, vehicle cover and gaps in cover. For example when we ran a piece of analysis we found:
 • Past cancellations can equate to 70% higher loss cost, a person with two prior cancellations are more than twice as likely to cancel again.
 • An individual with more than one NCD entitlement at any one time has a 33% higher loss cost and those who have had a NCD downgraded in the past are 60% more likely to cancel.
 • The more often people switch vehicles the more likely they are to cancel.
 • There is a 50% higher loss cost when a customer has previously had a gap in cover and one gap in cover in the last 5 years means they are 55% more likely to cancel.

 Loyalty rules, cancellations cost millions
 Policy history data is a true measure of cancellation relativity across the market. Based on our analysis of this data, 15% of new business is cancelled on average across the motor sector and most of these policies - 13% - are cancelled after the cooling off period, incurring significant cost for the market in fees, potential bad debt and loss future custom. In cancellations alone, we have calculated the cost to an insurer or broker is over £1.2m per annum.

 Understanding the cancellation risk in relation to other factors is crucial so that a decision can be made about the risk rather than a straight accept or decline. For example, lower risk customers may be offered a discount to encourage loyalty whereas up-front payment may be needed from higher risk applicants.

 Risks related to switching behaviour
 Building on these insights, we can start to dig deeper into the data and bring in quoting behaviour to understand risk.

 Motor insurance is the most ‘switched’ of all insurance products so the next step is to look at switching behaviour - how often and at what stage in the renewal cycle the customer switches and how that correlates to claims and cancellations.

 Our early analysis shows that 31% of motorists intend to switch at renewal. However what’s really powerful for insurance providers is the fact that when a quote is obtained for the same day cover there is a predicted 32% higher loss cost. Furthermore, there is a 91% higher chance of early cancellation.

 Named Drivers
 There is also a considerable opportunity to use policy history data to improve the understanding of named driver risk. From our analysis we can see 42% of policies in our database have a named driver present, and there are 4,500 policies with 5 or more named drivers . Our data science team has already uncovered that named drivers on cover that had two prior cancellations lead to 40% higher loss cost and are also twice as likely to cancel in the future.

 By applying policy history to the named driver as well as the proposer at point of quote, it will be possible to identify gaps in cover and cancellations that may impact the risk of the policy. There is also the opportunity to use the data to help identify a genuine connection with the main proposer based on a shared prior policy. This could be particularly valuable at MTA when a named driver is added half way through a policy.

 360 Degree View
 Ultimately the best value from policy history data will be gained when it used alongside other data including quoting behaviour and public data to create a 360 view of the risk. This way the market can be in a stronger position to deliver fairer pricing to customers.

 Brokers may feel more empowered to pass on the right risks to their insurer partners, and the costs incurred through cancellations and claims losses can be reduced, while loyalty can be rewarded and promoted.
  

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