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: In this paper we propose a model that can be used to analyze correlated claims data. In the process we introduce a class of multivariate generalized Poisson distributions. Then we present posterior distributions of its parameters. These distributions are difficult to manipulate, so we employ Markov chain Monte Carlo methods to draw random numbers from posteriors and predictive distributions. We present a collection of EXCEL VBA functions and subroutines to perform the simulations.

Description: Abstract of Research Project on Interest Rate Risk

Description: Reviewing a text covering multiple statistical modeling topics: e.g., generalized linear models, decision trees, clustering, using the statistical language R.

Description: Excerpts from a conversation with Alan Mills regarding his work in complexity science and agent-based modeling.

Description: In 2003, the American Academy of Actuaries Life Capital Adequacy Subcommittee LCAS published a set of 10,000 stochastic scenarios that was intended to be used to support the publication, “Recommended Approach for Setting Regulatory Risk-Based Capital Requirements for Variable Products with Guarantees Excluding Index Guarantees.” The primary purpose of this article is to illustrate statistical measures that can be used to evaluate stochastic simulations.

Description: This article discusses the pending elimination of Section 7 exemption for small companies.

Description: The purpose of this paper is to explore pension plan dynamics— – the behavior of a defined- benefit pension plan over time in an uncertain environment. We proceed by simulating the behavior of a model plan in a stochastically varying economic environment. While both the model and its environment are simplified, the model displays interesting behavior suggesting policy considerations that should be included in the design and funding of these plans.

Description: Empirical evidence of reported losses suggests that insurance firms are interconnected in a nontrivial way, making them prone to systemic risk. Systemic risk can be viewed as the result of a cascading process, as it unravels throughout a network structure. In response, this work presents a simple analytical model that can simulate this process. The model is subsequently tested upon an empirical dataset via the means of numerical simulations. This work makes three main contributions. First, it provides a novel methodology for quantitatively and objectively assessing the systemic role of individual firms within the insurance domain. Second, it exemplifies the inability of traditional, firm-based information in serving as proxies for mapping these systemic effects. Third, it provides a practical example where network-based information (e.g., Criticality IDX, Sensitivity IDX) can outperform firm-based information (e.g., admissible assets, excess c apital) resulting in increased efficiency in the decision-making process. These findings strengthen the need to account for the interconnected nature of the domain while showcasing some of the potential benefits that can be harvested by doing so.