What I took away from Class 2 exercise is that MECE 6 emphasizes the outcome rather than the initial trigger or root cause. It helps filter a wide range of issues or brainstorming ideas by focusing on the actual impact on the bank, rather than on what originally caused them.
This seems useful for translating a messy set of potential risks into a structured classification based on their consequences.
My question is: in practice, is this outcome focused approach interchangeable with input or trigger focused classification, or is it chosen mainly for convenience? For example, as seen in cybersecurity CIA Exposures, it sometimes seems more convenient to classify exposures based on the system failures that create the threat. In which…
When apply ECRG framework to this case I keep coming up with one question. Many of the controls that should exist appear to be implicit rather than explicit. How do we distinguish between a weak control and a missing control when the case doesn’t give enough detail? For example, if a control might exist but isn’t documented or visible, should we classify it as a control failure or a governance failure?
This case is a great reminder that risk management isn’t about listing every scary-sounding failure mode—it’s about structuring exposure. The CRO’s frustration shows why: without a coherent MECE-style inventory of loss types, teams end up mixing risks, controls, failures, and symptoms into an unusable pile. What stands out is how easily organizations default to predefined taxonomies (Basel 7, vendor lists, industry rankings) instead of thinking from first principles about this specific product’s exposure. None of these frameworks are wrong, but none automatically guarantee completeness or relevance. The real skill lies in stepping back, reframing the problem, and building a structured, product-specific exposure map that aligns with how the CRO—and ultimately the bank—makes decisions. This case captures that pivot perfectly.
This case made it very clear why operational risk discussions often become unproductive. In Case 2.1, the CRO’s frustration was understandable: the team produced a long laundry list of issues, mixing risks, exposures, control failures, and general concerns. A list like that cannot support any real assessment of loss ranges or help decide whether the new digital lending product should launch.
What stood out to me in this class was the shift from reacting to individual problems to building a structured exposure inventory. The exercise showed that frameworks such as Basel 7, the Grok Top 10, or industry lists can be useful references, but none of them automatically meet the CRO’s needs. They categorize events, but they do not necessarily…
In real risk event reviews, when the Basel 7 categories are not fully MECE, which priority should a team optimize for first—mutual exclusivity or completeness? And is there a practical workflow you’ve seen that helps ensure rigorous classification even under time pressure?
What I took away from Class 2 exercise is that MECE 6 emphasizes the outcome rather than the initial trigger or root cause. It helps filter a wide range of issues or brainstorming ideas by focusing on the actual impact on the bank, rather than on what originally caused them.
This seems useful for translating a messy set of potential risks into a structured classification based on their consequences.
My question is: in practice, is this outcome focused approach interchangeable with input or trigger focused classification, or is it chosen mainly for convenience? For example, as seen in cybersecurity CIA Exposures, it sometimes seems more convenient to classify exposures based on the system failures that create the threat. In which…
When apply ECRG framework to this case I keep coming up with one question. Many of the controls that should exist appear to be implicit rather than explicit. How do we distinguish between a weak control and a missing control when the case doesn’t give enough detail? For example, if a control might exist but isn’t documented or visible, should we classify it as a control failure or a governance failure?
This case is a great reminder that risk management isn’t about listing every scary-sounding failure mode—it’s about structuring exposure. The CRO’s frustration shows why: without a coherent MECE-style inventory of loss types, teams end up mixing risks, controls, failures, and symptoms into an unusable pile. What stands out is how easily organizations default to predefined taxonomies (Basel 7, vendor lists, industry rankings) instead of thinking from first principles about this specific product’s exposure. None of these frameworks are wrong, but none automatically guarantee completeness or relevance. The real skill lies in stepping back, reframing the problem, and building a structured, product-specific exposure map that aligns with how the CRO—and ultimately the bank—makes decisions. This case captures that pivot perfectly.
This case made it very clear why operational risk discussions often become unproductive. In Case 2.1, the CRO’s frustration was understandable: the team produced a long laundry list of issues, mixing risks, exposures, control failures, and general concerns. A list like that cannot support any real assessment of loss ranges or help decide whether the new digital lending product should launch.
What stood out to me in this class was the shift from reacting to individual problems to building a structured exposure inventory. The exercise showed that frameworks such as Basel 7, the Grok Top 10, or industry lists can be useful references, but none of them automatically meet the CRO’s needs. They categorize events, but they do not necessarily…
In real risk event reviews, when the Basel 7 categories are not fully MECE, which priority should a team optimize for first—mutual exclusivity or completeness? And is there a practical workflow you’ve seen that helps ensure rigorous classification even under time pressure?