Risk Management interview preparation

Questions

100

Traced to a firm

37

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12

Updated

September 2026

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1. 079Estimate next year's credit cost for a mid-sized Indian bank's unsecured personal loan book.Indian regulationHardcase studyIndian bank risk and treasuryBank credit risk

   Say this

   I'd build it bottom-up from vintage delinquency. For a mid-sized Indian unsecured book, I'd expect credit cost somewhere in the 3 to 5 percent range of the book, and I'd build to that number rather than assert it, then say which assumption moves it most.

   Then walk it

   1. Structure: credit cost equals flow rate into default times loss given default, applied to the average book, plus the change in provision stock on existing stages. Keep it as a flow, because a stock-based estimate hides the growth effect.
   2. Size the book and its mix. Say 20,000 crore of unsecured personal loans, average ticket 3 lakh, tenor three to four years, so roughly a third of the book amortises each year and new origination is a large share. A fast-growing book has a young average vintage, which understates delinquency until it seasons.
   3. Flow rate: start from observed 30-plus delinquency and apply roll rates. If 30-plus is 4 percent and roughly 60 to 70 percent of 30-plus rolls to 90-plus over the following quarters, forward flow into NPA is roughly 2.5 to 3 percent annualised. Bureau data and RBI's Financial Stability Report give you a system benchmark to sanity-check against.
   4. LGD: unsecured, so recovery is low. Collections and settlements might recover 15 to 25 percent over two years, so LGD of 75 to 85 percent. Multiply: 3 percent flow times 80 percent LGD gives roughly 2.4 percent, then add the provision build on the growing performing book and you get to 3 to 4 percent.
   5. Then the adjustments that actually decide the answer. Seasoning: if origination grew 40 percent last year, next year's delinquency is set by that cohort, and personal loan defaults peak 12 to 24 months after disbursal. Vintage curves, not current delinquency, are the honest input.
   6. Macro and policy overlay: RBI raised risk weights on unsecured consumer credit to 125 percent in late 2023 specifically because growth was running far ahead of secured lending. That slows origination and tightens underwriting, which improves next year's cohort and worsens the growth denominator.
   7. Segment sensitivity: new-to-credit borrowers, fintech-sourced and app-based loans, and small-ticket loans run multiples of the delinquency of salaried, bureau-scored, existing-customer lending. A weighted average across a book with a rising fintech share drifts upward even if each segment is stable.
   8. So I'd give the range, name my two swing assumptions, roll rate and the fintech-sourced share, and say what I'd need to tighten it: vintage curves by origination channel and the bureau's segment-level delinquency trend.

   Where candidates lose it

   Producing a single confident number. The interviewer wants a structure, two or three named assumptions and a range, plus awareness that a fast-growing unsecured book looks artificially clean because the loans haven't seasoned. Vintage analysis is the concept that has to appear.

   Expect next

   • Why does a fast-growing book look clean?
   • What did RBI's risk weight increase actually change?
   • How would you split this by origination channel?

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