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Tackling the blind spot in ADAS data for greater pricing precision

Technical Article

Publication date:

06 January 2022

Last updated:

09 March 2022



Adaptive Cruise Control; Lane Departure Warning; Blind Spot Mitigation - these are just some of the Advanced Driver Assistance Systems (ADAS) now available in cars.

The penetration of ADAS equipped cars in the U.K. is growing with more and more new cars[i] being born with ADAS and offered for sale with the option of additional safety features[ii]. In fact the U.K. is already leading the adoption of automated vehicle safety systems across Europe[iii] with 8 safety features per vehicle on average in the car parc, based on LexisNexis Risk Solutions analysis.

With predictions that some level of ADAS will be present in 85%[iv] of all vehicles produced globally by 2025, understanding the presence and performance of ADAS to inform insurance pricing has become increasingly important for the highly competitive, price-driven, U.K. motor insurance market.

Historically, identifying ADAS on a specific vehicle and assessing its impact on reducing claims frequency has been difficult at any level, let alone at the Vehicle Identification Number (VIN) level the insurance market needs, given ADAS technologies are often chosen as optional extras. ADAS features are often brand-named by manufacturers with an eye on their marketing messages, making it difficult to compare apples with apples. As a result, and quite understandably, there has been hesitation amongst insurance providers to accept self-declared safety features.

As well as needing a clear taxonomy of ADAS features – created through an insurance lens –insurance providers have also faced the challenge of analysing the real-world effect that individual safety features might have on claims frequency. After all, specific vehicle tech features or feature groups might lead to fewer accidents and lower insurance losses, but insurance providers also need to factor for repair and recalibration costs associated with increasingly advanced ADAS features.

To add to the complexity of using ADAS data in motor insurance pricing, some ADAS features work superbly as solo safety technologies but others complement each other and reduce risk as an interconnected, interrelated protective package.

Fundamentally, it’s been a major challenge for the motor insurance market to know exactly how a car is equipped with ADAS and how those features work in isolation or in combination to reduce claims frequency, at the point of quote.

Making a potential quagmire of risk considerations into a smooth road ahead, started with a new classification of ADAS for the insurance market. This classification system has been created using machine learning to scan millions of lines of car manufacturer vehicle data to logically sequence and classify vehicle safety features and the component’s intended operation or purpose.

The next step has been using the classification system to build intelligence around how ADAS fitments relate to real-world insurance claims. Close to 3 million cars and associated claims have been analysed to date, working with insurance providers and car manufacturers across the UK and Europe.

This work has found that there are a set of 12 core ADAS features that deliver a reduction in claims frequency. In fact, 69% of cars analysed in Europe by LexisNexis Risk Solutions were equipped with a core safety feature and are therefore less likely to have an insurance claim.

To make this intelligence workable in the insurance environment, insurance providers can now call on the data at quote as part of the overall risk assessment process, either as granular data confirming which forms of ADAS are present on the customer’s car and the performance of those features, or they can simply use a value 0-5 that has been calculated based on all the features on a specific vehicle, in terms of reducing claims frequency.

By uncovering the presence, purpose and performance of ADAS at the VIN level and the effectiveness in reducing claims, insurance providers can add a further layer of granularity to the understanding of risk. There is little question that knowing more about the vehicle build can help insurance providers optimise their pricing for new business and differentiate themselves in a crowded market. At renewal, ADAS data gives insurance providers the ability to reassess the risk with a much clearer understanding of the vehicle and potentially reward customers for the investment they have made in their car’s safety features.

Finally, adding ADAS data to risk assessment can help insurance providers demonstrate to the Financial Conduct Authority (FCA)[v] the steps they are taking to deliver the right motor insurance cover at the right price.

Already eight in ten new cars are currently available with driver assistance systems[vi]. Consumer demand for these safety features can only grow and in response, motor manufacturers will continue to provide increasingly sophisticated ADAS solutions. By developing an understanding now of the insurance risk associated with ADAS, insurance providers will be more prepared for a world where levels of vehicle safety technologies will continue to increase[vii].

Carla McDonald, Director Product Management, LexisNexis Risk Solutions, U. K. and Ireland


[i] SMMT Motor Industry Facts July 2021


[iii] LexisNexis Risk Solutions analysis

[iv] L1 and above, in the Society of Automotive Engineers or SAE classification

[v] https://www. fca. org. uk/publications/policy-statements/ps21-11-general-insurance-pricing-practices-amendments

[vi] SMMT Motor Facts 2020 – 8 in 10 cars available with driver assistance systems



This document is believed to be accurate but is not intended as a basis of knowledge upon which advice can be given. Neither the author (personal or corporate), Society of Claims Professionals or Chartered Insurance Institute, or any of the officers or employees of those organisations accept any responsibility for any loss occasioned to any person acting or refraining from action as a result of the data or opinions included in this material. Opinions expressed are those of the author or authors and not necessarily those of the Society or Chartered Insurance Institute.


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