Insurers are developing and deploying increasingly complex Big Data tools, first for post-sales purposes such as claims handling and customer service, but also potentially for sales, pricing and underwriting. These Big Data and AI tools present particular challenges due to their complexity, self-calibration, autonomy and the potential for unexpected results and unforeseen impacts. In this paper, we explore these issues with the use of case studies and a group comprising data scientists, model risk experts and governance experts. The paper focuses solely on the (re)insurance industry and is meant to provide a practical overview for risk managers.

Key insights presented in this paper deal with the importance of clearly defining the problem the tool is expected to solve, keeping humans in the loop, and the training needs that arise when using such tools. Furthermore, the paper focuses on ethics and bias in data and how such bias, if not understood and managed, can cause significant issues for organizations. Lastly, the paper gives an example of an overall model governance framework for Big Data tools, and provides a checklist that the reader can reference when needed.