Description: Demonstrating that incentive maximization could lead to failure using game theory. This is illustrated by the optimum strategy for the Prisoner Dilemma single period, multi period and infinite period that could be mutually destructive or beneficial.

Description: A response to the comments on the paper.

Description: Presented at May 1985 Spring Meeting. This session discussed: Hurdle rates, Internal rate of return, Payback, Net present value, Lease versus buy decisions, Build versus buy decisions, Application to computer hardware and software decisions, Project budgeting, Post-implementation audit.

Description: Discusses a method for division of workload in a consulting firm in a way which will minimize cost.

Description: This paper demonstrates the potential synergies from applying actuarial techniques to operational risk analysis. It also demonstrates practical techniques, grounded in actuarial science, to solve operational risk management problems such as risk cost allocation and risk mitigation cost-benefit analysis. From 2006 Enterprise Risk Management Symposium, M-AS06-1.

Description: In response to the risks of embezzlement and branch mis-behavior, this paper describes an operational risk management framework for local Chinese securities companies and a set of tools to manage their branches. From 2007 Enterprise Risk Management Symposium, M-AS07-1.

Description: This article is about a session held at the October 1996 annual meeting, which focused on how actuaries can move beyond traditional employers.

Description: Overview of the model vetting practices of the last 20 years and challenges for the future.

Description: A discussion of the hidden costs of administering complex Products including operational inefficiency, product development constraints, increased time to market, inability to monitor experience, and lost cross selling opportunities among other things

Description: Recently researchers, practitioners, and regulators had intense debates about how to treat the data collection threshold in operational risk modeling. There are several approaches under consideration—the empirical approach, the “naive” approach, the shifted approach, and the truncated approach—for fitting the loss severity distribution. Since each approach is based on a different set of assumptions, different probability models emerge. Thus, model uncertainty arises. When possible we investigate such model uncertainty analytically, otherwise we use Monte Carlo simulations. For specific parametric examples we consider exponential and Lomax distributions which are special cases of the generalized Pareto family. Our primary goal is to quantify the effect of model uncertainty on risk measurements. This is accomplished by evaluating the probability of each approach producing conservative capital allocations based on the value-at-risk measure. These explorations are further illustrated using a real data set for legal losses in a business unit (Cruz, 2002).