Description: Hear from the editor’s on the August 2018 issue of Risk Management published by the JRMS.

Description: Business intelligence;

Description: Hear from the editor’s on the December 2018 issue of Risk Management published by the JRMS.

Description: The editors provide highlights of the articles included in the August 2015 issue of Risk Management.

Description: Read the March 2017 issue of Risk Management published by the Joint Risk Management Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Hear from the editor’s on the March 2017 issue of Risk Management published by the JRMS.

Description: The article discusses modeling of variable annuity behavior risk, with a particular focus on how interaction between different behavioral assumptions complicates modeling. The article gives a brief overview of variable annuity behavior risk, discusses the challenges that behavioral interactions pose for VA modeling, and discusses three potential solutions for dealing with this complexity.

Description: Read the March 2017 issue of Risk Management published by the Joint Risk Management Section. Newsletters provide practical information for the working actuary, articles on trends that impact sections and news about section activities.

Description: Global financial crises like the one recently experienced, affected both large and small institutions.Today, when there is heightened need for enhanced risk management tools, some entities are unable to employ sophisticated mechanisms due to limited data availability. Moreover, from the Basel Committee on Banking Supervision’s point of view via Basel II and Basel III, the internal ratings- based (IRB) approach requires that institutions have some reliable estimates of default probabilities of default for each rating grade. Taking the work of previous researches a step further, this paper intends to propose a new dynamic mechanism to for the risk management industry for to calculateing probabilities of default (PD). Through these estimates for the core model, we generate implied probability of default (PD) through actuarial estimation tools and different probability distributions. This mechanism is specialized to work best for low- defa ult portfolios (LDPs). Furthermore, scenario testing is adopted to validate the model against any model- specific bias

Description: The editors provide highlights of the articles included in the April 2016 issue of Risk Management.