Description: In December 1997, the Society of Actuaries held a conference whose focus was actuarial and financial modeling. During the course of that conference, one of the speakers mentioned a number of technologies his firm was using for modeling, which were either unfamiliar or only vaguely familiar to some of the audience. The purpose of this paper is to define each of the technologies mentioned during that presentation and to briefly describe their distinguishing characteristics. The technologies discussed in this paper are statistical pattern recognition [SPR], neural networks [NNs], genetic algorithms [GAs], simulated annealing, fractal analysis, chaos theory, rule induction, data fusion algorithms [DFAs], data compression, fuzzy logic, case-based reasoning, and expert systems.

Description: Read the ARCH 2013.1 Proceedings editor's comments.

Description: This abstract describes a paper that presents examples of Risk Assessment application of Fuzzy Logic in the areas of operational risk and hazard risk.

Description: This article comments on the editorial policies of the Actuarial Research Clearing House publication. ARCH 2009.1

Description: This paper presents an overview of credibility theory in a fuzzy environment and discusses its implications.

Description: Read the ARCH 2013.2 editor's comments.

Description: This paper gives an overview of fuzzy optimization, presents applications of fuzzy optimization and investigates ways to extend the methodology to risk assessment.

Description: What is the 'best' post-retirement financial strategy for a particular participant of a defined contribution plan? The answer to this question must accommodate a number of risks, the most obvious of which are related to longevity, inflation, investment, and health. Moreover, what makes this question especially intriguing is that its solution must be molded to the wealth, features and agenda of a particular individual and/or family. Many of the constraints and goals are qualitative, and standard market and population metrics may have limited significance. This is the abstract of a paper that deals with implementation issues, qualitative goals and constraints, and tailor-made solutions.

Description: This abstract describes a paper that explores the answers to the questions of: 1. How is each view of FRVs conceptualized? 2. What are the differences and similarities between the metrics for each of these views? 3. How are the metrics for these three views implemented?

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.