The emergence of big data analytics and artificial intelligence has triggered a deep transformation of the insurance industry. Established insurers invest in the digitisation of their processes and products, while an increasing number of InsurTech companies are entering the market as insurers, distributors of insurance solutions, and at other points along the industry’s value chain. Both incumbents and newcomers are developing insurance products that use large amounts of data to assess, select, price, predict and prevent risks that in some cases were previously considered uninsurable. Going forward, access to data and the ability to derive new risk-related insights from it will be a key factor for competitiveness in the insurance industry.
However, the use of big data in insurance raises complex issues and trade-offs with respect to customer privacy, individualisation of products and competition. Assessing these trade-offs requires complex value judgements, and the way they are addressed leads to different scenarios for the future development of the sector. In this context, policy choices can have far-reaching consequences for the future face of the industry, its socio-economic relevance and the value it creates for its customers.
Our new report aims to contribute to an informed and fact-based debate by identifying and discussing key trade-offs involved with the application of big data in insurance. The paper discusses the implications of a wide range of uses of data and develops potential future scenarios to highlight likely consequences of different policy choices.
