Description: The direct and indirect effects of the pandemic have impacted many actuaries’ work since the spring of 2020. This article addresses three considerations actuaries should account for when using risk evaluation models which are focused on projecting remote, highly unlikely losses: model effectiveness, asset risk, and insurance (mortality and mortality) risk.

Description: This paper proposes a model for the incurred but not reported - IBNR- claims of an insurance company. This model will be fit to third party automobile bodily injury claims for which the months and years of occurrence of the accident are available. From the Actuarial Research Clearing House 1990 VOL. 1

Description: In this article the author cautions against the temptation to extend stochastic modeling into areas of unpredictable parameters and probabilities. He challenges actuaries to rely on their own judgment.

Description: This panel discussion considers some of the approaches, techniques and problems in the development of corporate models and assumptions. The pros and cons of buying versus developing models was discussed and the use of deterministic versus stochastic models was addressed as well.

Description: Feature article discussing numerical short cuts, rules of thumb, estimators, modeling tips, etc. The featured problem is fitting the intermediate points for a real-world stochastic model of interest rates.

Description: This article is the first installment of a three-part series that aims to provide an overview of ESGs and will focus on the general motivation for stochastic modeling, its advantages and its limitations. Future articles will further break down the key factors of ESGs and the application of these factors to better understand the Academy Interest Rate Generator (AIRG).

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: The article warns against treating the results of a stochastic model with more deference than other actuarial analysis, and describes some hidden risks involved with choice of model and calibration.