10031668Acuity: Transforming insurance management and understanding for consumersClosedCollaborative R&DInnovate UKThe insurance sector has the lowest levels of consumer trust among financial services, with 30% of consumers reporting mistrust (FCA, 2020). Mistrust is driven primarily by misleading terms and conditions (53%) and unclear content (34%; FCA, 2020). Indeed, a UK study found that consumers must be educated to at least A-level, but in the majority of cases graduate or even postgraduate level, in order to meaningfully understand their insurance policies (BrowneJacobson, 2018). Consequently, when choosing either motor or home insurance, most consumers (76%+) select a policy based on price rather than features (FCA, 2020). Feature differences between policies are difficult to identify and assess, yet vital to ensuring the right cover is in place. For example, if a user misses a flight home, does their travel insurance cover accommodation until the next flight? It is also commonplace for consumers to forget their policy renewal date and even their insurance provider. Founded in Cambridge in 2019, Rnwl's mission is to make it easier for consumers to manage their insurance policies, find the best possible coverage for the best price, and avoid spending hours renegotiating and renewing every year. Starting with motor insurance, we have built a consumer app (available to download on Apple and Android). Our app keeps track of the renewal dates of a user's car insurance, MOT, tax, and breakdown cover, as well as recording vital policy details such as provider, policy number, and contact details, which could be needed urgently in the event of an accident. When it comes to insurance renewal, we provide the user with quotes at the optimum time to purchase a new policy. With support from Innovate UK, we have developed an analytical engine that uses natural language processing (NLP) and machine learning (ML) algorithms to extract key policy features at scale, using this data to help consumers manage their motor insurance policies. Here, we will extend our cutting-edge NLP and ML algorithms to improve user readability and comprehension. Our technology will effectively reduce the required insurance policy reading age from up to postgraduate level to that of a 12-13 year old i.e. from the level of a scientific journal to the level of the BBC website (BrowneJacobson, 2018). By developing a solution that works for consumers at scale, spanning unstandardised insurance policies from multiple insurance providers, we will disrupt the insurance industry, and ensure maximum value and peace of mind for UK consumers.