In recent years, the integration of artificial intelligence (AI) and machine learning (ML) into actuarial practice has been reshaping the landscape of risk assessment and decision-making. As these technologies become increasingly prevalent, it is important to understand the principles and methodologies involved in creating, testing and using AI models to ensure their reliability, accuracy and fairness. Actuaries play a critical role in this process, leveraging their expertise in statistical analysis, risk assessment and regulatory compliance. Through comprehensive testing of AI models, actuaries help mitigate model risk and enhance the overall integrity of the decision-making process. This paper aims to provide educational material regarding good practices for testing AI models within the context of actuarial work, emphasizing a principle-based approach that allows for flexibility in application. By presenting suggested approaches and tools for effective model testing, this paper aims to foster confidence in the use of AI technologies while promoting ethical and responsible practices.
The paper is structured to offer an overview of the key considerations and methodologies involved in testing AI models. The structure of the paper is set out below. Some points may appear in more than one section as they are relevant in the different contexts. Additionally, the paper addresses the importance of model governance and risk management, highlighting the actuary’s role in the testing and validation process.
