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Know the Score on Rating the ‘Person Risk’ for Motor Insurance

The rules over the use of credit data in motor insurance pricing are clear, but the future of motor insurance underwriting lies in customer policy history data.

It is standard practice for motor insurance providers to use public records data, such as the presence of current County Court Judgments, insolvencies and edited Electoral Roll data, to help assess risk for pricing and underwriting.  At the point of quote, this public records data alongside an increasing volume of data attributes helps the industry to understand the likelihood of claim and potential loss cost better. 

But what about private credit data?  The use of contributory credit data about how people pay their credit cards and other financial commitments is used to assess credit risk and affordability for premium installments but the use of this data in underwriting and pricing is prohibited.  It is deemed that when and how a customer pays their credit cards and loans should not help determine the kind of insurance risk they represent. 

The problem is that often the private and public credit data are presented as a credit score, so the lines become blurred in the use of credit data to determine insurance risk. The use of credit data in insurance pricing is also coming under increasing scrutiny over concerns around financial exclusion.

LexisNexis Risk insights built on insurance-specific data

The logical solution is to build a score based on a person’s insurance history, not just with one provider, but a score that represents their experience with the market as a whole. This is not only the guiding principle of ‘Treating Customers Fairly’ and the formal requirement for the industry, but it’s also far more accurate in predicting risk.

Until recently, an insurance provider working in silo has needed to rely on the customer data in their own databases to gain a view of their insurance history.  Customer data naturally varies from provider to provider, but evidently, those operating in the market the longest with the largest database have been at an advantage. 

However, given that even the largest insurance companies only have a market share of 10% to 15%, this leaves at least 85% of the market entirely unknown to them.

They also needed confidence that their data was providing the complete view of their experience with a customer over the years, despite possible address changes, name changes or typing errors.  Is the ‘John Smith’ insured on a motor policy in 2019 the same ‘John Smith’ insured with a motor policy in 2010?

However, now this challenge is resolved thanks to industry collaboration, data analytics and linking technology. 

To create the insights needed to understand risk accurately related to policy history has meant gathering data from across the market into a contributory database.  The good news is that so far in 2019, over 70% of the motor insurance market is contributing, with the figure expected to rise to 80% by the close of the year. 

The more data, the deeper the data insights and the better insurance providers can understand the risk and deliver quotes to customers reflective of their individual insurance risk.

Speed is essential when it comes to motor insurance quoting.  The key has been to build that policy history data into a score so that insurance providers gain an instant understanding of risk based on their underwriting criteria. This new insurance specific score factors in a whole range of policy history behaviours predictive of insurance loss that could never be reflected in a credit score that is designed to predict financial defaults.

For example, it can factor for the likelihood of that person cancelling their policy; whether the person has had a previous gap in cover; what their switching behaviour is; when they purchase cover and what their current policies and no claims discount entitlements are.  It can also factor for the risk of named drivers on a policy and claims history including their NCD entitlement.

Pulling together predictive data sets for motor insurance

In addition, the score factors for the aforementioned public data, the CCJs, edited electoral roll data, Council Tax bands, Insolvency and Land Registry data.  In all there are over 200 data attributes built into the score to ensure the insurance provider has the clearest view of the risk possible..

All of this valuable information is consolidated into one score ranging from 200-999 to give insurance providers an immediate indication of the propensity for insurance loss.

At its heart, this score is a true reflection of the individual concerned and their insurance risk, not someone with the same name at a similar address. This type of pinpointed accuracy is achieved using linking and matching technology to create one customer view.  In essence, this takes around 1.8 billion rows of data and crashes all of that down into a single reference number. We call this number LexID®, which is assigned to that individual’s record.

Fundamentally, by pulling all these powerful data sets, containing insurance specific data, trained on insurance outcomes, into one score, insurance providers can gain a far more accurate view of risk for customer segmentation, underwriting and pricing.  How do we know this?  Based on our analysis with one motor insurance provider, when we identified the worst 10% of their customer base using the score we found these customers had a 200% higher claims cost than average. At the other end of the scale, the customers who had the best score – again those in the top 10% – had a 41% lower claims cost than the average. 

Policy history data is a key component of this motor insurance score and is changing the nature of motor insurance underwriting, giving providers the confidence to compete, with a price that is right for the risk. 

Follow the link to the LexisNexis Risk Solutions website to find out more about how we support insurance providers.