Risk Management interview preparation

Questions

100

Traced to a firm

37

Firms

12

Updated

September 2026

Asked at

All firmsUBS14MSCI7BLBlackRock5FTFranklin Templeton3Oaktree Capital Management2Scotiabank2Jane Street1Moody's1Neuberger Berman1PIMCO1SSState Street1TSTruist Securities1

Topic

All topicsMarket risk and VaR14Tail risk and stress testing5Greeks and sensitivities5Credit risk11Counterparty risk and CVA6Operational risk5Model risk and validation6Regulatory capital7Liquidity risk and ALM6Statistics and quant foundations7Indian regulation7Risk governance and appetite4Markets and macro9Fit and career8

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Type

AnyTechnicalCaseBrainteaserMarket viewFit

1. 027How would you approach building a delinquency model?Credit riskHardtechnicalNeuberger BermanRisk · Chicago · 2024

   Say this

   Define the target first, then build backwards. Delinquency is not default, so I'd fix the bad definition, say 90 days past due within twelve months, set an observation and performance window, and only then worry about features and model form.

   Then walk it

   1. Target definition is the decision that determines everything else. 30, 60 or 90 days past due, and over what horizon. Roll-rate analysis tells you where delinquency becomes effectively irreversible, and that's where you draw the line.
   2. Sampling: pick an observation point, take the borrower's state as at that date, then observe outcomes over the following twelve months. Strict separation, or you leak future information into features and get a model that looks brilliant in development and fails in production.
   3. Features in three families. Behavioural: utilisation trend, minimum-payment behaviour, recent missed payments, bounced mandates. Bureau: enquiry velocity, existing delinquency elsewhere, thin-file flags. Loan and demographic: loan-to-value, instalment-to-income, vintage, product, channel of origination.
   4. Model form: start with logistic regression on coarse-classified, weight-of-evidence binned variables. It's monotonic, explainable and passes validation. Then run a gradient boosting challenger to see how much signal the simple model leaves on the table. If the gap is small, ship the simple one.
   5. Validation: out-of-time as well as out-of-sample, because credit models degrade through the cycle not through the sample. Report Gini or AUC for ranking, and a calibration curve for whether the predicted rates match observed. A model can rank perfectly and be badly calibrated.
   6. Two traps specific to credit. Survivorship and selection bias: you only observe outcomes for people you approved, so the model is blind to the rejected population, and you need reject inference. And macro sensitivity: a model built on 2021 data has never seen a rate cycle, so the absolute PD level will be wrong even if the ranking holds.
   7. Then monitoring. Population stability index on the score distribution, drift on each feature, and a monthly actual-versus-expected. Most delinquency models fail from population shift rather than bad maths.

   Where candidates lose it

   Going straight to algorithms. In credit, the target definition, the observation window and the reject-inference problem are worth more than model choice, and interviewers who build these for a living are listening for exactly those. Also say the word calibration; ranking power alone doesn't let you price or provision.

   Expect next

   • How would you handle reject inference?
   • How would you know the model had degraded?
   • Would you use gradient boosting in production for this?

   Reported by candidates at Neuberger Berman (Risk, Chicago, 2024). Source: Wall Street Oasis.

2. 028How do you build a credit scorecard, and how do you prove it works?Credit riskHardtechnicalBank credit riskGlobal capability centres

   Say this

   Bin every variable, convert to weight of evidence, fit a logistic regression, then scale the log odds into points. You prove it works on three axes: discrimination, calibration and stability, tested out of time, not just out of sample.

   Then walk it

   1. Coarse classification first. Bin each variable so the bad rate is monotonic across bins and each bin has enough volume, usually at least 5 percent of the population. Then replace the bin with its weight of evidence, the log of the good-to-bad odds ratio.
   2. Information value tells you which variables to keep. Below about 0.02 is useless, 0.1 to 0.3 is useful, above 0.5 and I'd check for leakage rather than celebrate.
   3. Fit logistic regression on the WOE variables, then scale: points equal offset plus factor times log odds, calibrated so a chosen score doubles the odds every 20 points. That scaling is cosmetic but it's how credit officers read the output.
   4. Discrimination: Gini, or equivalently AUC, where Gini equals two times AUC minus one. A retail behavioural scorecard should hit 0.55 to 0.70 Gini; an application scorecard on a thin-file population might only get 0.35, and that can still be commercially valuable. Kolmogorov-Smirnov is the other standard, the maximum gap between the cumulative good and bad distributions.
   5. Calibration: plot predicted against observed bad rate by score band, and run a Hosmer-Lemeshow style test. Discrimination decides who you approve; calibration decides what you charge and what you provision. You need both.
   6. Stability: population stability index between development and current, per variable and on the score. Above 0.25 and the population has shifted enough that the model needs rebuilding, not just recalibration.
   7. And the governance point: build on a development sample, validate on a holdout, then validate again on a later time period the model never saw. An out-of-sample test on a random split proves almost nothing for a credit model, because the whole failure mode is time.

   Where candidates lose it

   Quoting a Gini target as if it were universal. A good Gini depends entirely on the population and the product, and someone who has built these knows that. The other failure is testing only discrimination. A model with 0.7 Gini and broken calibration will approve the right people and price them all wrong.

   Expect next

   • What Gini would you expect on a prime mortgage book?
   • The Gini is stable but the bad rate has doubled. What happened?
   • When do you recalibrate versus rebuild?
3. 031How does IFRS 9 expected credit loss differ from Basel regulatory expected loss?Credit riskHardsuperdayBank credit riskRegulatory reporting

   Say this

   Different purposes, so different parameters. IFRS 9 is accounting: point-in-time, forward-looking, neutral, and lifetime for Stage 2 and 3. Basel is prudential: through-the-cycle PD, downturn LGD, twelve-month horizon, and deliberately conservative.

   Then walk it

   1. Horizon. Basel EL is always twelve months. IFRS 9 is twelve months in Stage 1 and lifetime in Stages 2 and 3.
   2. PD. Basel wants a long-run average, through-the-cycle PD. IFRS 9 wants a point-in-time PD conditioned on a macro forecast.
   3. LGD. Basel requires downturn LGD, a stressed recovery assumption. IFRS 9 wants a neutral, expected LGD with no prudential margin.
   4. Discounting. IFRS 9 discounts cash shortfalls at the effective interest rate. Basel EL is undiscounted.
   5. Then the reconciliation, which is where real work happens. For IRB banks, if accounting provisions exceed Basel EL, the excess counts in Tier 2 up to a cap of 0.6 percent of credit RWA. If provisions fall short, the shortfall is deducted straight from CET1. So the two frameworks meet in the capital ratio.
   6. For standardised-approach banks it's different again: general provisions can count in Tier 2 up to 1.25 percent of credit RWA, and specific provisions reduce the exposure value.
   7. And the transitional arrangements matter historically. When IFRS 9 came in, supervisors allowed a phase-in of the day-one CET1 hit precisely because the provision increase was large enough to be destabilising.

   Where candidates lose it

   Treating them as the same number with different labels. Naming the four parameter differences is table stakes; the answer that lands explains the CET1 shortfall deduction and the Tier 2 excess cap, because that's the bit that actually affects a bank's capital ratio.

   Expect next

   • What happens to CET1 if provisions are below Basel EL?
   • Why is Basel LGD downturn and IFRS 9 LGD neutral?
   • Which framework produced a bigger provision in 2020?
4. 032What is structured finance, how would you evaluate it, and what are the credit risks?Credit riskHardtechnicalMoody'sCredit Risk · New York · 2024

   Say this

   Structured finance is taking a pool of cash-flow-generating assets, putting it in a bankruptcy-remote vehicle, and slicing the cash flows into tranches of different seniority. You evaluate it in three layers: the collateral, the structure, and the parties.

   Then walk it

   1. Layer one, the collateral. Pool composition, weighted average life, seasoning, geographic and obligor concentration, historical default and prepayment behaviour, and how the underwriting was done. Everything downstream depends on this, and it's where the 2007 failure actually was.
   2. Layer two, the structure. Where does the cash go, and in what order. Credit enhancement comes from subordination, excess spread, overcollateralisation and reserve accounts. Then the triggers: performance triggers that turn a pro-rata waterfall sequential, and cash-trapping mechanics.
   3. Layer three, the parties. Originator, servicer, trustee, swap counterparty. Servicer quality drives recoveries, and servicer failure has broken deals whose collateral was fine. Then the legal question: is the true sale robust, and is the SPV actually bankruptcy remote?
   4. How I'd analyse it: model the pool, run default and prepayment scenarios, and see at what cumulative loss each tranche takes its first rupee of loss. That break-even loss compared with the expected loss is the real measure of a tranche's safety.
   5. The credit risks specific to tranching. Correlation risk: a senior tranche is a bet on correlation, not just on average defaults, because it only fails if losses cluster. Cliff risk: a mezzanine tranche goes from untouched to wiped out over a narrow loss range, so it's far more convex than its rating suggests.
   6. Then prepayment and extension risk on the timing, basis risk if the assets and liabilities reprice off different benchmarks, and originator alignment. Skin in the game is why post-crisis rules require the sponsor to retain a slice.
   7. The Indian version worth naming: pass-through certificates and direct assignments on NBFC loan pools, where the live risks are servicer concentration, priority-sector motivation on the buyer side, and the 2018 to 2019 NBFC liquidity episode showing how quickly refinancing assumptions fail.
   8. And the honest limitation: the rating of a structured tranche is far more model-dependent than a corporate rating. Small changes in a correlation assumption move a AAA to a BBB, and that is exactly what happened to CDOs.

   Where candidates lose it

   Explaining tranching and stopping. The two things a credit risk interviewer at a rating agency wants are the sensitivity of senior tranches to correlation rather than to average default rates, and the cliff-risk convexity of mezzanine. Naming the servicer and the true-sale question shows you've read a deal document, not a textbook.

   Expect next

   • Why is a senior tranche a bet on correlation?
   • What actually went wrong with CDO ratings in 2007?
   • How would you analyse an Indian NBFC pass-through certificate?

   Reported by candidates at Moody's (Credit Risk, New York, 2024). Source: Wall Street Oasis.

5. 035Explain the Merton model, and the difference between structural and reduced-form credit models.Credit riskHardtechnicalBank credit riskModel validation

   Say this

   Merton treats equity as a call option on the firm's assets with a strike equal to its debt. Default happens when asset value falls below debt at maturity, so you can back out a default probability from the equity price and its volatility. That's the structural family; reduced-form models skip the story and fit default intensity straight from market spreads.

   Then walk it

   1. The Merton set-up: firm assets follow a lognormal process, equity holders own a call with strike equal to the debt face value, and default probability is the chance the asset value ends below that strike. Distance to default is how many asset standard deviations you are above the barrier.
   2. The clever part is that it's forward-looking and uses market data. Equity prices update every second, so a structural PD reacts long before a rating agency does. That's what Moody's KMV commercialised as EDF.
   3. Its weaknesses are specific. It underpredicts short-term default because the asset process is continuous and can't jump. It needs asset value and asset volatility, neither of which is observable. It assumes one debt maturity. And it produces credit spreads well below observed ones, the credit spread puzzle.
   4. Reduced-form, or intensity models like Jarrow-Turnbull and Duffie-Singleton, take default as an exogenous Poisson-type event with a hazard rate calibrated from CDS or bond spreads. No story about why the firm defaults, just a fit to prices.
   5. So the trade-off: structural models explain and give you economic intuition and a link to the equity market. Reduced-form models fit market prices and are what you use to value and hedge credit derivatives.
   6. In practice a bank uses both and for different jobs. Structural or hybrid models for wholesale PD estimation and early warning; reduced-form for pricing and for CVA. And the risk-management caveat: a structural model's PD spikes whenever equity vol spikes, so it's cyclical and noisy, which is fine as an early warning signal and bad as a provisioning input.

   Where candidates lose it

   Describing equity as a call option and stopping. The interviewer will ask what's wrong with Merton, and 'it underpredicts short-horizon default because assets can't jump' plus 'asset value and asset volatility aren't observable' are the answers. Also be able to say which model you'd use for pricing versus for PD estimation.

   Expect next

   • What is distance to default?
   • Why does Merton understate short-term default risk?
   • Which would you use to price a CDS?

Firm tags come from public, anonymous candidate reports on Wall Street Oasis: strong signal, not sworn testimony. Firms are named as the places a question was reported, not as partners of Fin Maverick. Answers are written for this page to show how to think out loud; they are not scripts to recite.