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

Level

AnyCoreIntermediateHard

Type

AnyTechnicalCaseBrainteaserMarket viewFit

1. 063How would you build a survival horizon for a bank treasury?Liquidity risk and ALMIntermediatetechnicalTreasury and ALMIndian bank risk and treasury

   Say this

   Build a daily cash flow ladder under stress and find the first day the counterbalancing capacity runs out. That day count is the survival horizon, and the useful output isn't the number itself but which assumption drives it.

   Then walk it

   1. Start with contractual cash flows by day: maturing loans in, maturing deposits and wholesale funding out, coupon and interest flows, and known commitments. That's the easy part and the least informative.
   2. Then layer behavioural assumptions, which is where the answer is actually made. Deposit run-off rates by segment, drawdown on committed undrawn facilities, no rollover of wholesale funding, and rating-trigger-driven collateral calls.
   3. Then the counterbalancing capacity: unencumbered HQLA at stressed haircuts, central bank facilities and what collateral qualifies, committed lines you can genuinely draw, and asset sales with a realistic time-to-cash. Repo is the fast one, whole-loan sales are not.
   4. Then run at least three severities: an idiosyncratic name-specific stress where markets function but nobody will lend to you, a market-wide stress where everyone is short cash, and a combined scenario. The combined one is what regulators require and it's the one that binds.
   5. Read off the first day of negative cumulative net cash. A typical internal appetite is 30 days for a combined stress and 90 days for an idiosyncratic one, plus the regulatory 30-day LCR as a floor.
   6. Then the genuinely valuable step: sensitivity. If the horizon goes from 45 days to 12 when retail run-off moves from 10 to 20 percent, the number is an assumption, not a fact. I'd present the horizon as a range with the binding driver named.
   7. Two things people forget. Intraday and currency granularity: being liquid in rupees and short dollars on day three is a failure even if the aggregate is fine. And encumbrance, because assets already pledged in repo are not available however liquid they look.
   8. And the escalation link: each horizon threshold should map to a contingency funding plan trigger with named actions, or the measurement is an academic exercise.

   Where candidates lose it

   Building a contractual maturity ladder and calling it done. Contractual flows tell you almost nothing, because the risk lives in behaviour. And failing to split by currency is the classic error: an aggregate survival horizon can hide a dollar funding gap that kills you first.

   Expect next

   • What run-off rate would you assume on uninsured corporate deposits?
   • How do you treat central bank facilities in the counterbalancing capacity?
   • How would this differ for a non-bank finance company?
2. 064How would you model a bank's savings deposits, which have no contractual maturity?Liquidity risk and ALMIntermediatesuperdayTreasury and ALMIndian bank risk and treasury

   Say this

   Split the balance into a stable core and a volatile portion, assign a behavioural maturity to the core, and estimate a deposit beta for how much of a policy rate move you pass through. Those two parameters, core share and beta, drive the entire banking book rate risk answer.

   Then walk it

   1. Volume modelling first. Look at the historical balance series per segment, strip out trend and seasonality, and take a low percentile of the remaining distribution as the stable core. Many banks use something like the balance exceeded 95 percent of the time over five years.
   2. Assign a repricing or behavioural maturity to the core. If it has been sticky for a decade, you might treat it as five to seven years of effective duration, capped by supervisory limits. Basel caps the average repricing maturity of core retail non-maturity deposits at five years in its standardised framework, and that cap exists because banks were assuming longer and flattering their rate risk.
   3. Deposit beta: regress the rate you actually paid against the policy rate. Indian savings rates have historically been very sticky, so betas on savings accounts are low, maybe 0.2 to 0.4, while term deposits and bulk deposits run much higher, 0.6 to 0.9. Segment or the average is meaningless.
   4. Betas are asymmetric and non-linear, and that's the point most candidates miss. They're low on the way up until competition bites and then they jump, and they're low on the way down because you can't pay less than zero. Modelling a single symmetric beta understates the squeeze in a rising cycle.
   5. Segmentation matters more than technique: retail versus corporate, insured versus uninsured, digitally active versus branch-only, relationship versus rate-shopping. A digitally active uninsured corporate depositor behaves nothing like a pensioner with a branch passbook.
   6. Then validate the assumptions against a real episode rather than trusting the regression. What actually happened to your balances and your pass-through in the 2022 to 2023 hiking cycle? That's the out-of-time test.
   7. The limitation to volunteer, and it's the important one: these models are all calibrated on a world where moving money was slow. Mobile banking and instant transfers have shortened behavioural maturities in a way the history doesn't contain. SVB lost a quarter of its deposits in a day, which no core-stability model would have produced. So I'd stress the assumption hard rather than trust it.
   8. RBI's 2024 draft LCR revisions add a run-off add-on for internet and mobile banking enabled deposits for exactly this reason, which is a good example of supervisors updating faster than the models.

   Where candidates lose it

   Treating it as purely a statistical problem. The regression is the easy part; the judgement is in segmentation, in the asymmetry of beta, and in recognising that the historical data predates instant digital withdrawal. A candidate who says 'the history no longer applies' and then says what they'd do about it stands out.

   Expect next

   • What deposit beta would you assume for Indian savings accounts?
   • How would you stress the core assumption?
   • Why does Basel cap the assumed maturity at five years?

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.