Description: This article explores the possibility of using machine learning to improve, or potentially rewrite, actuarial code within actuarial software.

Description: In reaction to a spate of model conversion activity and rising acceptance of model risk management techniques, many companies have, or are in the process of centralizing their modeling function to accommodate both governance and efficiency demands. These operating model changes have produced a wide range of questions and issues, such as division of labor, roles and responsibilities, selection of tasks to keep decentralized, among others. This session will frame a series of key considerations around modeling function consideration, and share lessons learned with the audience.