The Mack model and the Bootstrap Over-Dispersed Poisson model have long been the primary modeling tools used by actuaries and insurers to forecast losses. With the emergence of faster computational technology, new and novel methods to calculate and simulate data are more applicable than ever before. This paper explores the use of various Bayesian Monte Carlo Markov Chain models recommended by Glenn Meyers and compares the results to the simulated data from the Mack model and the Bootstrap Over-Dispersed Poisson model. Although the Mack model and the Bootstrap Over-Dispersed Poisson model are accurate to a certain degree, newer models could be developed that may yield better results. However, a general concern is that no singular model is able to reflect underlying information that only an individual who has intimate knowledge of the data would know. Thus, the purpose of this paper is not to distinguish one model that works for all applicable data, but to propose various models that have pros and cons and suggest ways that they can be improved upon.

Of the many retirement savings options available, defined benefit pension plans were once a retirement income staple. Due to the highs and lows of the economic cycle, defined benefit pension plans have become severely underfunded. A series of inadequate contributions, enabled by weak funding and risk management policies, poses uncertainty for the retirement of many. The cost of paying pension benefits rises as defined benefit pension plans become increasingly underfunded, burdening the employers who continue to pay them. However, without increasing these already unaffordable pension benefits alongside inflation, they become less valuable to retirees. As pension benefits lose their value and the costs of retirement, such as healthcare and assisted living, increase, defined benefit pension plans may not provide the retirement security that was once promised.