C.L.U.E. Auto® enhancements can help you assess risk, segment the market and maintain price to risk
Imagine you could uncover 12% more accidents tied to an individual you’re quoting. Imagine you could create additional opportunities for segmentation. Imagine you could identify unlisted drivers on previous claims and price more appropriately to the level of the risk.
What would those capabilities mean for your ability to provide the most fair and accurate price to the market?
Not every event is reported to an insurer
When it comes to loss history, LexisNexis® C.L.U.E.® Auto is the gold standard of claims information, with over 99% of the market contributing more than 270 million claims. And it finds more claims than any other source on the market.
However, certain dynamics of car accidents mean not every event is reported to an insurer. For example:
- Damage is below the insured’s deductible
- Insured has insufficient coverage
- Insured doesn’t report the accident or damage due to concerns about premium increases
- Insured wishes to avoid the hassle and cost of filing a claim
- Incident involved a single vehicle
If you’re not aware of these unreported events, you could be taking on hidden risks.
Find the hidden risks
To achieve a more comprehensive view of risk, you need to know about every accident, whether it is reported or not. You also need to be certain that the accident data is attributed to the correct driver and vehicle. Supplemental, non-contributory sources of historical accident information provide this 360-degree view.
Police records provide incremental lift of 12% over claims
Notably, our customers have told us that police record insights are of particular interest because they indicate whether the driver was at fault in the accident. However, using police records can be challenging because one incident may appear on a police record and a claim, leaving insurers at risk of double counting.
To determine the incremental lift provided by police records, we studied 2.2 million policies. After de-duplicating the data set, we were able to uncover 12% more accidents tied to the drivers.
We also discovered additional opportunities for segmentation.
- Police records have a similar effect as claims. The effect on frequency of having one subject police record is almost the same as having one C.L.U.E. subject claim.
- Adding damage events creates additional segmentation opportunities. Adding subject-level police record events to C.L.U.E. subject searches can help you price more appropriately to the level of risk.
- It’s easier to identify confident clears. 70% of policies fell into a group with zero C.L.U.E. subject claims and zero subject police records. These groups may be eligible for a discount on their rates. Read this blog post to learn why confident clears matter.
To learn more about our study, including our finding that using LexisNexis Vehicle History can help uncover 25% more incidents than just using C.L.U.E. vehicle searches alone, download the white paper here.
Unlisted drivers on claims are predictive of higher loss costs
In addition to accident and damage events not reported on the claim, insurers tell us that claims with unlisted drivers are becoming a bigger issue. For example, the policy holder might lend their car to a relative or a neighbor who isn’t on the policy, and that unlisted driver might get into an accident.
That’s a problem, because our internal research found that compared to other claims, unlisted driver claims are predictive of higher claims frequencies and loss costs.
For reference, having a single surchargeable claim results in a 1.4x surcharge in property damage (PD) loss cost relativity. However:
- Having an unlisted driver results in a 1.6x surcharge, or an increase of 13% PD loss cost relativity
- Having an unlisted driver not living in the household and not carrying insurance at the time of the claim results in a 1.8x surcharge, or an increase of 31% PD loss cost relativity.
To help identify these unlisted drivers, we’ve added four flags to the claim record in C.L.U.E. When the vehicle operator doesn’t match the policy holder, you’ll learn:
- That the vehicle operator isn’t a listed driver.
- Whether the vehicle operator was living in the household.
- Whether the vehicle operator was carrying insurance at the time of the claim.
- How many days since the vehicle operator last carried insurance.
This incremental data could help you identify opportunities to establish factors for tiering and better assess and price risk.
C.L.U.E. Auto Damage 360 and unlisted driver flags in action
These data enhancements are available separately, allowing you the flexibility to incorporate either one or both into your workflows. Each flag can assist you in refining your tiering system.
A 360-degree view is critical
A more comprehensive view of accident and damage information associated with the drivers and vehicles being quoted can help uncover significant segmentation opportunities. LexisNexis® C.L.U.E. Auto Damage 360 can help you discover these additional events. With these additional insights, you can rate more precisely, maintain price to risk and sustain profitability.
¹ LexisNexis® Risk Solutions internal study, 2024