Description: How do you make model output media worthy? This article explores considerations that may contribute to the mass production of model results for public consumption.

Description: The author delves into other important parts that should be in the scope of model validation besides the code.

Description: This article discusses the sensitivity of actuarial functions to interest rate changes.

Description: 1993 SOA Annual Meeting in New York. This session is about Dynamic Solvency Handbook developed by The Financial and Investment Management Practice Education Committee. The handbook is for actuaries who prepare solvency reports, and the targeted completion date was December 31, 1994.

Description: It is worth the effort to obtain a pricing surface. It helps make an informed decision and facilitate the pricing conversation within and outside a company.

Description: This article discusses challenges that short-term predictions have on actuaries' reputations, both as individuals and as a profession, and discusses strategies to improve our professionalism and better manage this risk.

Description: Presentation at the 41st Actuarial Research Conference held on August 10-12, 2006 in Montreal, QC. This paper considers the problem of extending regime-switching models for asset returns to the situation with multiple asset markets. The conditional tail expectation CTE is highly sensitive to the assumed dependence structure between the regime models for the different markets. A Monte Carlo simulation is used to illustrate the cost of misspecification. The authors find that the model most commonly used by practitioners is worse than all of the alternatives that they investigate.

Description: Summary of the SOA research project on PBR Simplified Methods. Describes the multi-risk scenario generator, central estimate assumptions, assumption objectivity measures, and regulatory demonstrations per VM-20.

Description: This article deals with enterprise risk management and how the techniques contained within the discipline can help prevent unforeseen large mistakes in strategy, product design, and product management

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.