UPLIFT: Ensuring Fairness Within Insurance and Technologies

UPLIFT: Ensuring Fairness Within Insurance and Technologies

Article written by Sam Chapman, Chief Innovation Officer

The Floow has long been the leading example in predictive risk understanding for motor insurers and their policyholders.

Our risk estimation is based upon actual driving correlated to claims rather than proxies which provides clear and demonstrable benefits to both insurers and their policyholders. For insurers, it can lower the requirement for some proxies, while fundamentally enhancing overall predictive risk insight to make policies smarter and more profitable. The solutions we provide to insurers offer clearer pricing which is linked to behaviour, allowing drivers to be recognised and rewarded for exhibiting safer driving behaviours behind the wheel.

However, with any new technology or approach, alongside the clear benefits that they bring, it is also vital to explore any potential unexpected impacts. Therefore, it is essential to ensure that the technologies and approaches not only perform core risk prediction well, but that they also ensure that they do not unintentionally add bias, or decrease fairness in any manner. The Floow has always worked extremely hard to ensure that we continually focus on these aspects throughout all of our innovation work and our day-to-day activities.

All of our teams bring a vast amount of experience gathered from years of industry knowledge, leadership in innovation, and a novel understanding of mobility data, which ensure that we deliver the unique ability to understand risk in a highly detailed manner.

The UPLIFT Project

UPLIFT (Utilising Processing to explore Insurance Fairness using Telematics) was a project funded by the UK’s Industrial Strategy Challenge Fund which aimed to strengthen advanced data solutions for the insurance sector.

The project sought to discover possible areas where bias could theoretically be present in advanced motor insurance, and develop new methods to address and improve any areas of bias which were identified, as required.

However, it is important to stress that telematics, despite its recent growth, already delivers a much fairer experience compared to proxy-based policies, as drivers are rated based on actual driving, not proxies. Despite this, it doesn’t mean that telematics approaches cannot be further improved. It is clear that in order to achieve unprecedented levels of fairness, both old and new biases need to be removed, to provide optimised and fair risk understanding.

Bias and technology

The UPLIFT project drew inspiration from the idea that predictive systems, machine learning and data-led approaches have the potential to: encode old biases from prior data and understanding, or add new undesired changes to otherwise accurate systems. These potential impacts are more commonly discussed in relation to technologies including facial recognition, where despite their accuracy, they can be open to bias (if they are not developed, configured and tested carefully) which can impact on fair decision making.

Some of these biases can be extreme but it is also possible to have a range of small hidden biases included within any system. To address this issue, it requires a mature and investigative understanding to be developed before employing carefully tested technical mitigations.

Looking beyond traditional biases

The UPLIFT project focused specifically on telematics motor insurance, and it sought to explore and investigate any traditional biases relating to personal data, such as gender and ethnicity, but it also went further than this. It looked at potential areas where bias could emerge including - the device(s) used to capture data, the operating systems and chipsets within them, plus the geographical location bias, usage bias, and importantly real world data quality issues and the differentiated impacts for individuals.

All of these aspects go beyond the minimum required for predictive accuracy, and extend beyond the normal quality assurance approaches. This is because these investigations focused on policyholder needs, such as individualised ethical fairness for all. Simply put, systems that serve all users equally, rather than focusing purely on overall predictive accuracy, are fairer and have less bias

To investigate this, it requires the identification of demonstrable edge case scenarios, where risk understanding may deliver slightly differentiated results for some users in varying conditions or circumstances. Despite this analysis being guided by the identification of edge cases, the work undertaken, as part of the UPLIFT project, found zero connection with any traditional protected data - gender, ethnicity etc.

Instead, the areas for further investigation included: the device used to gather mobility data, the device’s operating system, rare specific geography (rare locational issues), ionospheric radiation, etc.

How UPLIFT has influenced our telematics solutions

The areas highlighted on the previous page also identified further points of study aimed at determining the potential of bias, its impacts, and how to address and mitigate any observed bias.

These areas have helped to enhance many of The Floow’s core capabilities including:

• Passenger vs. driver detection in smartphone-monitored solutions where accuracy enhancements can ensure fairer processing for multi-driver policies.

• Severe crash detection on nomadic devices to enable better reactions to major incidents, which will help drivers and claims handling.

• Risk estimation differences as a result of routing choices.

• Driver education feedback with differences highlighted between journeys which originate from varying devices.

• GPS data quality issues from different devices to ensure a fair methodology for end-users, regardless of originating data quality, and;

• Further areas where both large and small gains can be made with new or improved methodologies.

The improvements which we have made to our core capabilities, as highlighted above, allow us to remain the leading example in risk understanding, and ensure that our solutions continue to offer benefits to insurers and their policyholders.

The most recent examples of how the work completed as part of the UPLIFT project has been utilised within our solutions can be seen with the launch of FloowClaims - our claims module, and our Data Quality Assessment Service which was introduced in our latest release, release 9.1. Both of these solutions aim to enhance our already powerful algorithms and create fully connected insurance solutions which provide insurers with in-depth insights and provide policyholders with the robust and fair capabilities which fulfil the requirements they have for their insurance policy.

A change in focus, delivering successful results

Although we initially set out to understand and enhance fairness, we instead found solutions to issues that have produced products which are better balanced, and which enable accuracy improvements.

By addressing edge cases within the UPLIFT project, we were able to remove previously unknown outliers and demonstrate a predictive uplift in scoring - ensuring that the use of telematics within insurance remains fair for all.

To find out more about the work which we have carried out as part of the UPLIFT project, visit our webpage and check out our project update blog from August 2020.