Description: Easy access computing power is harnessed to approximate ruin probabilities. The dynamics of the traditional insurance model are stated in the form of period to period constraints. This allows the development of a life-table with the survival probabilities for a given set of parameters. Given the spreadsheet approach of the model, sensitivity analysis to study the impact of different parameter values and loss variable distributions can be easily implemented.

Description: We find the necessary and sufficient conditions for fitting five given moments by a triatomic distribution. We consider three examples drawn from fire large spread, individual life medium spread and group life small spread insurance data, fit them with triatomics, and compute the ruin probabilities using well known formulas for discrete and for combination of exponentials claim amounts. We then compare our approximations with the exact values that appeared in the literature.

Description: 2003 Valuation Actuary Symposium, San Diego, CA. This session addressed the emerging practice where management is developing new ways to examine financial risks holistically in an integrated model.

Description: This is the disclaimer for the Economic Scenario Generators tool available from the SOA / AAA on the SOA Website.

Description: This panel discussion session focused on the practical applications of risk theory.

Description: 2011 SOA Enterprise Risk Management Symposium, Chicago. In this paper, a multivariate quasi-negative binomial distribution is proposed to model frequency dependence among different risk types. The operational risk diversification effect is illustrated through frequency dependency modeled by the bivariate quasi-negative binomial distribution under a framework of Monte Carlo simulation.

Description: This paper evaluates the current practice of operational risk management in the insurance sector. Operational risk is nothing new in insurance, but, because of regulatory requirements, companies have initiated computation of risk capital for their operational losses. The current effort to manage operational risk is not a naturally evolving phenomenon, and operational risk, in the Basel Committee on Banking Supervision’s Basel II definition, is unlikely to be a significant cause of insurers’ failure. In addition, the current Basel II definition of operational risk is not suitable for the insurance sector. Consequently, the invention of models and tools based on the definition is incomplete and illusionary. My findings are based on the analysis of dozens of interviews with insurance industry professionals. I demonstrate the way operational risk is quantified in practice, show that the result obtained from this computation is of littl e use in managerial decision making and propose a set of policy recommendations illustrating the characteristics of operational risk in insurance. This study can be used as a platform for launching dialogues to initiate fresh thinking about operational risk in insurance beyond the current artificial and narrow boundaries.

Description: This article provides an overview of the commonly used structural credit risk modeling approach that is less familiar to the actuarial community.