When predicting claims risk and calculating adequate pricing for motor insurance, actuaries generally focus on the vehicle, usage, past claims and the driver. To differentiate the risk associated with drivers, historical policy data, claims data and public records data sets (to name a few) are typically used. Using all this information in a pricing model provides more granularity, a greater understanding of the risk, and therefore better premium estimations.
However, this data tends to be for the policyholder only. When it comes to differentiating risk associated with named drivers, insurers have mainly relied on one thing, their age.
Considering that around half of motor insurance policies have at least one additional driver, who in theory could be driving the vehicle at least 50% of the time, a finer differentiation of risk associated specifically with named drivers should be hugely beneficial.
To determine if additional information about named drivers would truly help with differentiating the risk of a policy, LexisNexis Risk Solutions analysed policies with named drivers present. We appended information from proprietary data sources, covering policy history, public information and quote history.
We know previous policy history can be indicative of future risk. Analysis of the LexisNexis Risk Solutions Motor Policy Database indicates that up to half of named drivers have prior policy history as a policyholder. What is more, historical policy information linked to these named drivers, is correlated with the loss ratio on the ‘current’ policy. For example, named drivers who as previous policyholders, have been associated with their vehicles for over three years have a 25% lower loss ratio.
Public records data for named drivers can also help differentiate insurance risk. Having negative public records (such as CCJs or bankruptcy) is usually associated with a higher propensity to claim.
Our analysis indicated that policies where a named driver had a CCJ and the policyholder didn’t, had a 17% higher loss ratio.
In addition to assessing claims risk to price policies more accurately, another problem that actuaries aim to solve is to spot policies where insurance fraud may have occurred. ‘Fronting’ is the practice of someone insuring a car as a main policyholder and listing the de facto main driver of the car as a named driver, in a bid to reduce the premium. This illegal practice is often only discovered when there is a claim, leading to the policy being cancelled and punitive actions against the people involved. It may also result in a large burden of debt for the consumer and huge admin costs to the insurance provider.
It may not be surprising that compared to any other age group, 17-20 year olds are nearly twice as likely to manipulate the details they provide for an insurance quote. However, recent research on consumer attitudes to data manipulation emphasise the scale of the challenge: seven out of ten motorists think it is acceptable to manipulate the information they provide when obtaining a quote for motor insurance from a comparison site. In addition, parents with children that are driving, 41% said they would consider fronting to save their child money .
Analysis of online quoting history associated with policyholders and named drivers gathered from across the market is providing a solution.
By looking at when the named driver on a quote also appeared as the policyholder on other quotes for the same vehicle within a certain period, insurance providers now have a clear indicator of possible fronting during the quoting process. This also extends to other forms of data manipulation such as changes to dates of birth, changes to declared claims information and so on, for both the policyholder and the named driver.
Quote manipulation is an indicator of higher claims risk so knowing, at point of quote that the information provided for that quote has been changed and to what degree, enables insurance providers to make more informed decisions about the proposed risk. This helps to reduce their exposure to fraud and enable actuaries to model pricing based on a more holistic view.
Follow the link to the LexisNexis Risk Solutions website find out more about how we support UK insurance.
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