As in a number of other sectors, 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. In its latest publication, the CRO Forum examines how to adapt existing governance to cover such tools, including machine learning algorithms, and what reasonable steps can be taken to ‘know what’s happening inside the black box’, ensure reliability and avoid unfair discrimination.

 

Big Data and AI present particular challenges due to complexity, self-calibration, autonomy and the potential for unexpected results and unforeseen impacts. The report explores 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.

 

The key principles emerging from the case study discussions include the following:

The report then explores the complex area of ethics and bias in data and how such bias, if not understood and managed, can cause significant issues for an organization. 

 

Building on the findings, the paper eventually gives an example of an overall model governance framework for Big Data tools, and provides a checklist that the reader can reference when needed. Key here is that model governance techniques and frameworks that exist today do not need to be fundamentally altered, but can be enhanced and adjusted to meet the evolving needs of complex tools and machine learning developments.

 

SCOR, as an active member of the CRO Forum, participated in this study.