Machine learning in pricing models raises questions for insurers
Excerpts from speech by David Rule, executive director of Insurance Supervision Association of British Insurers Prudential Regulation Seminar, 14 May 2019
For many pricing models, new technology is bringing rapid change. Processing power continues to increase, including from use of the cloud. This is enabling development of models that employ artificial intelligence techniques, such as machine learning, to analyze so-called big data – larger, more complex information from new sources, some of it structured but a lot of it unstructured.
One example is real time information about customer behavior taken from computing devices embedded in everyday objects, such as mobile phones, home appliances and cars – the so-called internet of things. Together with digital distribution of insurance, these changes bring huge opportunities. For example, better information may bring improved pricing, wider availability of insurance, more tailored and relevant products, enhanced fraud detection and better customer service.
At the same time, they are raising new questions and risks. Many are ethical and conduct related. For example, when designing a pricing algorithm what characteristics and behaviors of people is it acceptable to include? How transparent do insurers need to be to their customers about these algorithms? Where is the boundary between collecting data on behavior to improve pricing and unacceptable interference in privacy? Is it acceptable to charge a customer more because their behaviour suggests they might be willing to pay more? All these questions highlight the growing importance of managing model risk, including these ethical issues.
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