The definition lands: exposure is not “what can go wrong,” it’s the type of loss tied to the business line. Controls and governance don’t create exposures; they contain them. That framing should make our risk registers cleaner and more MECE.
The MECE 6 categories make it easier to ensure all loss types are considered — from fraud to regulatory claims — without overlap or gaps. What stands out is how exposures are treated as the starting point for understanding risk, not the risk itself. It emphasizes that effective control and resilience depend on how completely exposures are identified and measured.
Question: How can organizations practically test whether their exposure identification process is truly “MECE” — complete, non-overlapping, and consistently applied across all business units?
I know that operational exposures are heavy-tailed. Capital and limits hinge on the tail. For each exposure class, are there any models to show how it is tailed and do any stability tests? Is there a way to predict the future exposures numerically?
The idea that control failures don’t create exposures but rather let them spiral is powerful. It suggests that exposures are inherent in the business model — controls only shape how much impact we can tolerate. This helps shift accountability from risk managers fixing issues to business leaders owning exposures. However, if exposures are outcome-based while control, resilience, and governance are cause-based, how do we ensure that the organization’s risk taxonomy doesn’t blur these boundaries over time?
I think the part about “under which scenarios” is super important. Exposure isn’t just a fixed number—it really depends on the context. Like, a derivative might have almost no exposure in calm markets, but in a stress scenario, the potential loss could spike, So it makes me wonder: should companies define a standard set of scenarios when they measure exposure? Otherwise, different teams might be using totally different assumptions, and that could mess up how risks are compared or aggregated. Having consistent scenarios could help everyone stay aligned.
The definition lands: exposure is not “what can go wrong,” it’s the type of loss tied to the business line. Controls and governance don’t create exposures; they contain them. That framing should make our risk registers cleaner and more MECE.
The MECE 6 categories make it easier to ensure all loss types are considered — from fraud to regulatory claims — without overlap or gaps. What stands out is how exposures are treated as the starting point for understanding risk, not the risk itself. It emphasizes that effective control and resilience depend on how completely exposures are identified and measured.
Question: How can organizations practically test whether their exposure identification process is truly “MECE” — complete, non-overlapping, and consistently applied across all business units?
I know that operational exposures are heavy-tailed. Capital and limits hinge on the tail. For each exposure class, are there any models to show how it is tailed and do any stability tests? Is there a way to predict the future exposures numerically?
The idea that control failures don’t create exposures but rather let them spiral is powerful. It suggests that exposures are inherent in the business model — controls only shape how much impact we can tolerate. This helps shift accountability from risk managers fixing issues to business leaders owning exposures. However, if exposures are outcome-based while control, resilience, and governance are cause-based, how do we ensure that the organization’s risk taxonomy doesn’t blur these boundaries over time?
I think the part about “under which scenarios” is super important. Exposure isn’t just a fixed number—it really depends on the context. Like, a derivative might have almost no exposure in calm markets, but in a stress scenario, the potential loss could spike, So it makes me wonder: should companies define a standard set of scenarios when they measure exposure? Otherwise, different teams might be using totally different assumptions, and that could mess up how risks are compared or aggregated. Having consistent scenarios could help everyone stay aligned.