Description: This article poses the question, why doesn't the teaching of life contingencies start with the premise that the rate of investment income is a random process in a similar manner to the random nature of the time until death? The article suggests some methods for doing so.

Description: This presentation illustrates a calibration model against 60 years of historical data using a pragmatic mixture of filtering, maximum likelihood and method of moments techniques, and compare the resulting model with interest rates in contemporary economic scenario generators.

Description: This session from the 2005 SOA New Orleans Meeting discusses issues and concerns related to asset-liability ALM modeling. From the Record of the Society of Actuaries, Vol. 31, No. 1.

Description: An overview of bias and fairness in machine learning models, as they apply to use cases in life insurance.

Description: This paper focuses on business risks rather than market, credit and operational risks. The author proposes a bottom-up approach for modelling and measuring business risk where the dynamic of the underlying earnings is described in a continuous-time model.

Description: 1987 Valuation Actuary Symposium. This session discussed the implementation of a solvency testing process, associated company considerations, and the ways in which the process might be viewed by other parties.

Description: A presentation on the principle of equivalent utility from the 2001 ARCH. Contains both the static and dynamic model as well as examples with exponential utility.

Description: This paper discusses the rectangulatization of the survivorship curve and some related puzzling deviations between expectations and observations. From Actuarial Research Clearing House 1991, VOL. 2.

Description: Genetic algorithms [GAs] and neural networks [NNs] are currently being used in actuarial and financial modeling. Nonetheless, while there is a general awareness of the nature of these techniques, there is often only vague familiarity with the details of how they are implemented. This paper is intended to help alleviate this situation. Its purpose is to present an overview of GAs and NNs, which includes an explanation of what they are, how they work, and an example of how they are implemented.

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).