It is a tough time to be in auto insurance . Historically , insurers have often struggled to turn a profit on auto insurance policies , but post-pandemic economic conditions have only further undermined combined operating ratios . Supply chain issues , sticky inflation and the rising number of claims all continue to put upward pressure on operating costs . And premiums are not keeping pace . While motorist may have to live with premium increases of 16 % or more this year , insurers are not daring to raise rates significantly due to the current cost-of-living crisis . To add to the trouble , the industry own very structure is changing . According to McKinsey projections , technological advancements are likely to significantly disrupt the auto insurance risk pool in the US by 2030 , leaving traditional insurers with a shrinking share of value . Already , new technologies , including AI-driven opportunities , are opening the door to competitors from outside the sector who are enticing customers with innovative new products . These include :

• Big Tech players with their native expertise in data and their relentless drive to offer seamless , integrated , and high-value services to customers

• Auto manufacturers and brands , which are already using sensor data and their network of dealerships ( for repairs and parts ) to build stronger lifetime relationships with drivers

• Neo-banks and other emerging FinTech companies , such as Revolut in Ireland , which is offering discounted policies to safe drivers based on data collected from telematic devices

Introducing this month ’ s question with insight into auto insurers , AI and data is Simon Axon , EMEA Financial Services Industry Consulting Director at Teradata .

but also in imagining a wide range of additional services tailored to customer segments of one . Last year , McKinsey outlined a hypothetical journey for a US auto insurance customer , envisioning real-time risk calculations and liability shifting among different insured parties based on autonomous and semiautonomous driving , with second-by-second policy pricing . While these services are not yet available , the technology to make them achievable already exists . There is no reason why similar data and techniques could not be applied to reshape auto insurance norms and create innovative products .

Pulling ahead with data in the driver ’ s seat

If traditional auto insurers want to compete with the innovative companies entering the market , they must quickly accelerate their use of data for automation , Machine Learning , and AI . By collecting and analysing customer , telemetry and mobility data and leveraging analytic insights from the broader ecosystem of partners , insurers can better understand customer behaviour to improve customer engagement and create more value . To survive and thrive in the AIdriven decade ahead , insurers must transition from the traditional annual renewal and customer contact cycle to ‘ Always-on ,’ real-time relationships , continuously offering new products and services that create digital bonds with individuals at their point of need . Achieving this requires cultivating a data culture and investing in an enterprise-wide cloud analytics and data platform for AI that offers the speed and scalability to analyse billions of data points in real time .

Opportunities under the hood

Modern vehicles are packed with sensors that not only provide continuous performance data but also offer insights into individual driver behaviour . But the ‘ black boxes ’ used by some insurers are becoming outdated in terms of the type and amount of data they can provide . There is already talk of vehicles self-reporting accidents direct to insurers . The opportunity – and the necessity – for insurers lies not only in using this data for personalised quotes and usage-based policies ,

Simon Axon , EMEA Financial Services Industry Consulting Director at Teradata

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