Description: A response to the comments on the paper.

Description: Insurance companies are not immune to liquidity risks.

Description: This article is the result of the Leadership & Development book club on Dare Lead. The article talks about major themes in the book and relates them to other podcasts and authors thought on those themes.

Description: This paper demonstrates the potential synergies from applying actuarial techniques to operational risk analysis. It also demonstrates practical techniques, grounded in actuarial science, to solve operational risk management problems such as risk cost allocation and risk mitigation cost-benefit analysis. From 2006 Enterprise Risk Management Symposium, M-AS06-1.

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: Overview of the model vetting practices of the last 20 years and challenges for the future.

Description: A discussion of the hidden costs of administering complex Products including operational inefficiency, product development constraints, increased time to market, inability to monitor experience, and lost cross selling opportunities among other things

Description: Recently researchers, practitioners, and regulators had intense debates about how to treat the data collection threshold in operational risk modeling. There are several approaches under consideration—the empirical approach, the “naive” approach, the shifted approach, and the truncated approach—for fitting the loss severity distribution. Since each approach is based on a different set of assumptions, different probability models emerge. Thus, model uncertainty arises. When possible we investigate such model uncertainty analytically, otherwise we use Monte Carlo simulations. For specific parametric examples we consider exponential and Lomax distributions which are special cases of the generalized Pareto family. Our primary goal is to quantify the effect of model uncertainty on risk measurements. This is accomplished by evaluating the probability of each approach producing conservative capital allocations based on the value-at-risk measure. These explorations are further illustrated using a real data set for legal losses in a business unit (Cruz, 2002).

Description: Many top-performing businesses aren’t just risk aware, but rather risk intelligent. Such a posture helps prepare companies for a major event and can demonstrate to a regulator or auditor that an appropriately strong control framework is in place. Better still, companies gain real strategic value from a “risk adjusted” understanding not only of their threats and vulnerabilities, but also the upside opportunities associated with various risks. It starts with having both qualitative and quantitative information to identify, assess and manage risks. Strategic risk management, at its core, is about creating value by increasing transparency and accountability, and quality of information. More than a mere compliance exercise, effective risk management enables more informed strategic decision making, increased accountability and transparency, a greater likelihood of organizational success, and – ultimately – competitive advantage, as borne out by many studies that correlate risk management and financial results.

Description: As tariff policies are rapidly changing throughout 2025, the U.S. markets have been reacting in complex ways. In addressing the difference in short and long-term impacts of these policy changes, the importance of resilient and holistic risk management is highlighted in order for companies and investors to find opportunities and mitigate sudden losses.