Description: Increasing uncertainties and volatility in financial as well as real goods markets increasingly reveal that controlling systems based on deterministic and pseudo-exact values are insufficiently eligible to manage the business activities. This raises the question how management systems can be modified to generate the adequate management impulses in volatile periods. Risk management deals from identifying, evaluating and aggregating chances and risks, which again determine the potential fluctuation range of relevant performance indicators. Everyday corporate life shows that risk management is considered important; but the corresponding processes to identify, evaluate and report chances and risks are seldom part of established decision criteria. This “paradox” lets assume that risks are managed in various differing and unsystematic ways. The combination of traditional controlling methods and risk management is not primarily a methodological challenge. The behavior of the whole organization, practiced over a long-year period, has to be transferred into a new corporate culture of transparent dealing with chances and risks. This paper describes the necessary change management process, which has to come along with necessary methodological modifications.

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: The actuary’s key role in the ORSA process should be to quantify the uncertainty surrounding the future financial results of the firm. By providing relevant standardized reports that clarify the uncertainty in terms easily understood by all stakeholders, the actuary will provide an essential element in the ORSA process. The dictum, "You can't manage what you can't measure." demonstrates the need to quantify risk, not just describe it qualitatively. While the ability to provide firm estimates of aggregate risk for complex organizations may not exist currently, doing so should be a target for the profession. The end result will be to enhance the financial security of the insurance industry at a time when all financial institutions are facing stakeholders skeptical about their financial condition. Part of the essay collection "Risk Metrics for Decision Making and ORSA"

Description: This brief article details my observations as a senior consultant for a leading actuarial consultancy across the Middle East and South Asia regarding ORSA. It highlights key challenges, potential solutions and describes the dichotomy of management and consultant paradigm.

Description: The National Association of Insurance Commissioners (NAIC) is moving forward to implement a new regulatory requirement that requires U.S. insurers to perform an Own Risk and Solvency Assessment (ORSA). Before developing a response to the ORSA requirement, insurers will want to understand its genesis and the underlying rationale for it, as well as its implications. This essay provides an overview of the evolution and rationale for ORSA, as well as practical implications for insurers as they begin to design an ORSA process. Part of the essay collection "Risk Metrics for Decision Making and ORSA"