One recent report suggests that the number of UK drivers who switched their car insurance increased from 22% in 2016 to 27% in 2017. Alongside these issues, the industry has recently faced undue criticism over its pricing strategies and methodologies.
Our LexisNexis Risk Solutions consumer survey conducted in 2013 found that one in four motor insurance policyholders believed it to be acceptable to omit information at point of quote. Then two years later, in an identical 2015 study one in three respondents felt this way. So there’s no denying the ‘trust gap’, as we defined it in a previous blog article. The motor insurance sector is a tough market to operate in right now.
But what does the future look like? Sadly there is not one golden bullet that’s going to solve the sector’s challenges. But fundamentally, the more insurance providers can gain a deeper understanding of a customer’s risk at point of quote, and at speed, the more accurately they will be able to price that risk, reduce their claims exposure, and improve their quotability.
This in turn will enable them to deliver a fair and streamlined quote reflective of an individual customer’s risk.
The good news is that increasingly sophisticated analytics models combined with big data processing power means that the way we assess risk and gain a consolidated view of the customer is evolving significantly, with the emergence of risk scoring specific to the motor insurance industry.
Credit data versus insurance-specific data
Up to now, credit scores have been used by many if not all insurance providers as an indicator of insurance risk when assessing motor quotes. Whilst these scoring models have been valuable for the sector, they are based on payment history on loans and mortgages, and do not truly reflect that customer’s risk from an insurance perspective, nor do they help predict a consumer’s likelihood to claim.
They would not, for example reflect the fact that a customer had a past policy cancellation, a gap in cover, or the number of years they were eligible for a No Claims Discount. They would not reflect if they had named drivers on their policy, or held a commercial motor policy at any time.
Insurer’s own risk scores based on their own historical data have been effective in assessing risk, but they provide a limited view of a customer only for as long as that customer has been their client, and provide no insights on a new customer.
In the markets where we operate around the world, data enrichment by LexisNexis Risk Solutions leads to greater accuracy of pricing, and greater granularity of the underwriting risk, leading to lower premiums for the consumer. So this is a journey that is a win-win, for insurers and for consumers.
So as our understanding of risk and claims loss related to policy history has evolved, the need for an insurance information-based, market-wide score that incorporates these factors, utilising policy history data from across the market has become more urgent.
Market-wide scoring leading to loyalty and accurate pricing
This is an issue we recognised three years ago at LexisNexis Risk Solutions, when we started the journey to build the first market-wide insurance risk score based on policy data from the motor insurance sector. We envisioned a score that consolidated everything insurance providers know and ought to know about a particular customer, directly related to their motor insurance history, and worked to pull it all together into one easy-to-understand motor insurance score.
Most importantly, our goal was for the scoring model to be built specifically to help insurance providers predict loss and improve quotability.
The route towards that goal was to combine our proprietary motor insurance policy history data plus insights resulting from public records data such as CCJs, the edited electoral roll, and insolvencies data. Added to this mix was our proprietary scalable automated linking technology (SALT) and LexID allowing us to link different and disparate records for an individual customer to provide a singular view.
For example, insurance providers might want to know quickly whether a customer was also a home insurance customer; or whether they had named drivers on their policy for the same address, and what was the risk of those other drivers. There are around 200 different data attributes that go in to making up the total score. The result is a score that is a true reflection of an individual customer and a stronger indicator of loss than any other type of scoring model.
In practical terms what this means is that at point-of-quote, the insurance provider receives a score between 200 and 997 for that customer. Based on their risk appetite, they can underwrite that risk and offer a tailored premium for that customer, at sub-second speed.
Enrolling insurance providers to pool their policy history data has been a key step on the road to bringing such a motor insurance risk score to the market. With 69% of the market already participating, the predictive power of the model is already evident.
In our analysis, we have seen highly accurate correlations between the score and actual claims. Those with the highest risk according to our scoring had a 200% higher claims cost. Those with the lowest score had a 41% lower claims cost.
Some key insights on loss cost
Individuals with a CCJ in the last five years, have on average a 55% higher loss cost
People living in urban areas have an 18% greater loss cost compared to those living in rural areas
Individuals who have provided 15 or more entries for an address have a 38% lower loss cost relative to the overall book
The longer the period of time since an individual address appeared, the lower their loss cost relativity by up to 48%
The higher the crime rank is by postcode sector, the loss cost relativity increases by 40%
If an individual is on the edited electoral roll then, the loss cost relativity decreases by 28%.Source: LexisNexis Risk Insights
This crucial level of information enables insurance providers to decide if they want to underwrite the risk in the first place, or if they want to offer discounts to those with a lower propensity to claim.
This is one way to elevate customer engagement and loyalty within the sector.
No doubt, the ability to predict claims losses offers huge financial value to insurance providers, but there is also the insight it offers around cancellations. We know that people who cancel in the first 30 days are twice as likely to cancel another policy.
An insurance specific risk score is not just the next step in motor insurance pricing. It is a giant leap forward for the sector and its customers at a time when the delivery of loyalty, with fair and accurate pricing has never been more important
This article is care of www.lexisnexis.com
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