Description: This article discussed modeling the P&C reinsurance market using an Agent Based Model to provide insights into the market. The characteristics of this marketplace provide credibility to this type of modeling. Specifically, the P&C reinsurance market has few major players, very little product differentiation, and low barriers to entry. In addition established competitors are volume-constrained due to prior business and capacity decisions made based on estimated product costs and prices.

Description: COVID19 highlights the need for greater complexity expertise in risk management. Crisis like the current COVID19 pandemic and 2008 Financial meltdown and others help us to turn into focus inwards for introspection on how we can better manage our resources and provide a deeper understanding of what our objectives should be and how we can achieve them through applying complexity science.

Description: COVID19 highlights the need for greater complexity expertise in risk management. Crisis like the current COVID19 pandemic and 2008 Financial meltdown and others help us to turn into focus inwards for introspection on how we can better manage our resources and provide a deeper understanding of what our objectives should be and how we can achieve them through applying complexity science.

Description: The purpose of this paper is to define a predictive model, based on known system attributes, for assessing risk associated with information systems.

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.