Identify top exposures and mitigate
Portfolio Risk Identification and Mitigation Proposal
Context
You are evaluating a commercial/corporate lending portfolio. Assume you have loan-level data with: obligor ID, sector/industry, region, facility type, exposure at default (EAD), probability of default (PD, 12m), loss given default (LGD, downturn), maturity, collateral type and loan-to-value (LTV), rate type (fixed/floating), coupon/spread, covenant quality score, rating, and historical performance. You can run simple stress scenarios (e.g., GDP −2%, rates +300 bps, CRE prices −20%).
No raw dataset is provided; make reasonable, clearly stated assumptions. Use small numeric examples to justify your decisions.
Task
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Identify the top five risk exposures in the portfolio (e.g., single-name concentration, sector/geography, collateral/LTV risk, covenant risk, interest-rate sensitivity, refinancing walls, FX mismatch, etc.).
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For each exposure, propose specific mitigation actions (e.g., collateral adjustments, covenants, limits, hedging, pricing changes, sell-down/participations, risk transfer).
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Justify each recommendation with quantitative and qualitative evidence (e.g., EL/UL/EC contributions, stress impacts, concentration indices, industry outlook), including formulas or small numeric examples where helpful.
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State any assumptions and describe how you would validate the effect of your mitigations (monitoring, backtests, scenario checks).
Constraints & Assumptions
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Preserve the scope, facts, inputs, and requested outputs from the prompt above.
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If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
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Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.
Clarifying Questions to Ask
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Clarify the task, data shape, labels, constraints, and evaluation metric.
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State assumptions behind the math or modeling technique you choose.
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Connect theory to practical training, debugging, and deployment implications.
What a Strong Answer Covers
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Correct definitions and formulas where the prompt requires them.
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A practical explanation of how the method behaves on real data.
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Trade-offs, failure modes, diagnostics, and mitigation strategies.
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Evaluation choices that match the product or modeling objective.
Follow-up Questions
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How would noisy labels, class imbalance, or distribution shift affect the answer?
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What would you monitor after deployment?
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Which baseline would you compare against first?