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Artificial Intelligence

Insurance Technology

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Artificial Intelligence Report, Insurance Technology Report

Artificial Intelligence in Insurance - Thematic Research

Insurtechs’ AI-led revolution.

As our world becomes progressively more digitalized, customers are adopting technology that is shifting their behavior and core insurance requirements. Fast and cost-effective product innovation offers a key differentiating competitive edge. Hence, Insurtechs are entering the market in droves to respond to these needs, bringing with them product innovations such as on-demand insurance, micro insurance, and usage-based insurance.

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  • What you need to know...
  • Insurance incumbents are struggling to keep pace with emerging customer needs while maintaining profitability.
  • Learning from the retail industry, insurers have begun to use machine-learning algorithms to develop complex models to optimize customer lifetime value and increase opportunities for cross-selling and product recommendation.
  • AI enables preventative insurance. Predictive and preventive technologies such as Fitbit and fitness bands are designed to improve individual health management.
  • The robo-advice sector is to grow with more investment by insurers, as offering digital advice is typically less expensive for middle-low wealth consumers, and is definitely affordable for less wealthy young generations than working with a financial advisor.
  • First adopters of robo-advisors include AIG and LV. AIG achieved an investment boost of $244bn in its retirement units as a result of investment in robo-advice to make the investment advice process easier. LV acquired a majority stake in the robo-advisor Wealth Wizards and in 2017 launched a robo-paraplanner tool that provides retirement advice in less than two hours. In 2017 LV= invested £10m in robo-advice.

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Personalization can increase insurance companies’ profits by 5–10%

AI offers the opportunity to drive more personalized services, advertising, and marketing campaigns. It helps insurers to integrate proprietary data and new sources of data, such as that from digital and social media and from sensors and connected devices. More tailored experiences can be created by identifying customer segments for personalization utilizing cluster analysis of feedback.

This report provides an industry analysis, classifying it in terms of the speed of adoption – high adopters, mid adopters, and low adopters.

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