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.

Description: Results of the 2020 survey on the effects of the Pandemic

Description: Defines and discusses operational risk as it exists in every industry, especially as it exists in the financial services industry.

Description: This article provides an overview of the evolution and rationale for Own Risk Solvency Assessment (ORSA), as well as practical implications for insurers as they begin to design an ORSA process.

Description: This article discusses pointing out flaws in Incentive Compensation program using short term financial metrics like Total Shareholder Return may miss non-short term financial risk and non-financial risks.

Description: Introduction to essays advocating for incentive compensation linked to the level of risk in an organization.

Description: This research project examines operational risk management practices and assess the viability of ORM as a formal discipline. A key goal of this initiative has been to determine whether the management of operational risk is feasible, and, if so, what issues need to be addressed in order to effectively implement ORM within a broader ERM context.

Description: A risk map is an effective and dynamic tool in helping companies develop a broader view of their risks.