Description: Report on the EuSpRIG Annual Meeting last July in Paris, which focused on modeling.

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

Description: Explores the open vs. closed systems debate with regards to actuarial modeling and exposes the over-simplification. Valuable for anyone who uses models.

Description: Feature article discussing what American actuaries can learn from their European counterparts about actuarial modeling.

Description: This paper applies GPUs and deep learning to achieve orders of magnitude performance improvement in the valuation of large portfolios of variable annuities.

Description: Producing a crystal-clear balance roll-forward on time, obtaining a fresh model output the first time or providing model results for your business partners with a tight turnaround all depend on one thing: how fast the model engine runs. Authors explored various reasons behind significant computational power consumption, and discussed different ways to speed the computational process such as in-force population compression, model use case consolidation, code simplification and streamlining, and infrastructure automation, etc.

Description: Summary of methods and standards to use to achieve an efficient, maintainable model