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 followup on predictions about the future of enterprise risk management from 2003.

Description: This research report is on the incorporation of flood and other catastrophe model results into pricing and underwriting.

Description: This research report is on the incorporation of flood and other catastrophe model results into pricing and underwriting.

Description: Simultaneous modelling of operational risks occurring in different event type/business line cells poses the challenge for operational risk quantification. Invoking the new concept of Levy copulas for dependence modelling yields simple approximations of high quality for multivariate operational VAR. From 2007 Enterprise Risk Management Symposium, M-AS07-1.

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: Is Risk/Return a sound tool for decision-making? No. It is not consistent, not objective, distorts decision and degrades governance. Why? What shall we do then?

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: Discusses the successes and difficulties companies have had with the plan, methods most successful in securing bank cooperation, legal issues, persistency experience, etc.