This article was written jointly with Kjersti Aas (Norwegian Computing Center & Norwegian University of Science and Technology), Fei Huang (University of New South Wales) and Ronald Richman (Old Mutual Insure & University of the Witwatersrand), for the introduction of a special issue of the Annals of Actuarial Science.

.The expanding application of advanced analytics in insurance has generated numerous opportunities, such as more accurate predictive modelling powered by Machine Learning and Artificial Intelligence (AI) methods, the utilization of novel and unstructured datasets, and the automation of key operations. Significant advances in these areas are being made through novel applications and adaptations of predictive modelling techniques for insurance purposes, while, concurrently, rapid advances in machine learning methods are being made outside of the insurance sector. However, , these innovations also bring substantial challenges, particularly around the transparency, explanation, and fairness of complex algorithmic models and the economic and societal impacts of their adoption in decision-making. As insurance is a highly regulated industry, models may be required by regulators to be explainable, in order to enable analysis of the basis for decision making. Due to the societal importance of insurance, significant attention is being paid to ensuring that insurance models do not discriminate unfairly. In this special issue, we feature papers that explore key issues in insurance analytics, focusing on prediction, explainability, and fairness.

Continue reading Insurance analytics: prediction, explainability and fairness →