Description: Quantitative operational risk assessment is essentially based on stochastic scenario modeling of operational loss sequences. Given the lack of reliable historical data in most cases, mathematical methods should meet even stronger requirements in terms of results received and include specific features, namely: rare event analysis, uncertainty analysis and human factor analysis, etc. In this paper, the analytical-statistical simulation approach ASSA is considered as the most flexible approach to stochastic scenario loss sequence modeling and compared with Markov chain, fault/event tree and Monte Carlo, in terms of scenario loss sequence model adequacy and calculation cost. Probabilistic risk analysis software PRAISE and some applications are given. Presented at 2010 Enterprise Risk Management Symposium, Society of Actuaries, April 12-15, 2010.

Description: A discussion of the possible effort required and impacts resulting from the implementation of C3 Phase II for establishing risk-based capital for variable annuities. This article will describe practical considerations gleaned from actual valuation calculations recently performed by a consulting firm.

Description: Discussion of the movement toward principle-based reserves for LTC insurance.

Description: An Overview of Stochastic Simulation modeling techniques and usages

Description: Given the lower incidence rate, use of decision tree techniques like Classification and Regression Tree CART in understanding credit or operational risk becomes quite challenging. A commonly adopted solution is biased sampling approach, where more weights are attached to bad customers to artificially hike the incidence or bad rate. While adopting this type of biased sampling approach, question of the best weight arises. This paper adopts an iterative approach in identifying the best weight. The best weight for bankruptcy BKO profiling problem in hand occurred when the incidence rate was around 50 percent where entropy reaches its maximum.

Description: This article deals with enterprise risk management and how the techniques contained within the discipline can help prevent unforeseen large mistakes in strategy, product design, and product management

Description: In the copula literature there are many bivariate distribution families but very few higher dimensional ones. Moreover, most of these are difficult to work with. Some of the bivariate families can be extended to more dimensions but in general the construction of distribution functions with more than two variables is a difficult problem. This paper introduces a construction method that is straightforward to implement and can produce multivariate distribution functions of any dimension.