Description: The costly, time-consuming and complicated process of enterprise risk management (ERM) can be improved in many companies and made less tedious for managers by using reasonable data and templates obtained from peer group entities. The models used to calculate economic capital (EC) often underestimate its value because they do not consider decision-maker perception about risk. We assume here that managers, as the decision-makers, have appropriate business understanding and they can provide substantial information about risk characteristics regarding all business processes. Therefore we are focused, in this paper, on collecting data from managers across the different businesses to derive the appropriate knowledge about risky events, the importance of particular types of risk, the relationship between the risk outcomes and the level of risk control in a particular industry. We conclude that the collected data has a high potential for use as a benc hmarking reference and analysis for improving ERM models for individual businesses.

Description: This paper illustrates a current approach to capital allocation, referred to herein as Tail Contribution Analysis TCA, using it to evaluate the impact of changes in an insurer’s operations by allocating capital to individual lines of business. It then highlights a common problem in the implementation of the procedure arising from reserve volatility and briefly notes the failure of the methodology to account for a firm’s changing levels of risk under various strategies. Finally, it introduces an alternative method for analyzing business segments and strategies called Economic Profit Analysis EPA. We will discuss EPA’s key feature, “volatility replication,” an intuitively appealing process of building investor expectations and capital market information into the risk-and-return measurements used to evaluate strategic decisions. Finally, we will illustrate the benefit of using EPA in overcoming the problems of TCA and thereby providing concise reliable information for business decision making.

Description: Global financial crises like the one recently experienced, affected both large and small institutions.Today, when there is heightened need for enhanced risk management tools, some entities are unable to employ sophisticated mechanisms due to limited data availability. Moreover, from the Basel Committee on Banking Supervision’s point of view via Basel II and Basel III, the internal ratings- based (IRB) approach requires that institutions have some reliable estimates of default probabilities of default for each rating grade. Taking the work of previous researches a step further, this paper intends to propose a new dynamic mechanism to for the risk management industry for to calculateing probabilities of default (PD). Through these estimates for the core model, we generate implied probability of default (PD) through actuarial estimation tools and different probability distributions. This mechanism is specialized to work best for low- defa ult portfolios (LDPs). Furthermore, scenario testing is adopted to validate the model against any model- specific bias