The culmination of several years’ development and with support from motor insurance providers across the UK, at LexisNexis Risk Solutions we recently launched Attract™ for Motor, the sector’s first market-wide, policy data-based risk score built to more accurately predict insurance claims losses.
As pricing in the motor insurance sector continues to rise driven by a cocktail of escalating claims costs, government tax measures and fraud, the need to gain a consolidated understanding of individual risk, based on shared intelligence on policyholders has become a matter of urgency for insurance providers.
Attract™ for Motor will give insurers an immediate understanding of risk by utilising proprietary motor insurance data. This means Attract™ for Motor will be a stronger indicator of loss than an insurer’s own custom score model, or general financial scores, that are based on data non-specific to the insurance market.
First market-wide, policy data-based risk score
Attract™ for Motor is built specifically to predict insurance loss unlike other scores. When looking at Attract™ for Motor as a predictor of claims cost, we found that the worst 10% as segmented by Attract™ had a 200% higher claims cost than average.
Conversely, those in the top decile, or the top 10%, had a 41% lower claims cost than the average.
The beauty of Attract™ for Motor is that we can deliver the scoring direct into insurer and broker systems with sub-second response times at point-of-quote, ensuring the customer quoting process is streamlined. This speed is possible because we use proprietary data sources and highly advanced analytics technology.
A powerful tool at point of quote, Attract™ for Motor enables motor insurance providers to enhance the accuracy of their underwriting decisions, knowing that the score is a true reflection of the individual customer’s risk. This also results in fairer pricing for customers, tailored to their individual risk.
Follow the link to the LexisNexis Risk Solutions website to find out more about how we support insurers.