Interviewer view · keep this screen to yourself
Standard: Nilgiri Credit: loans to small shops using digital payment data
You run the case. Read the prompt, answer questions from the notes below, and share data only when the candidate asks for it or gets stuck. Score at the end.
Case timer
00:00
1. Read the prompt aloud
Read it slowly, then pause. Let the candidate ask questions before they structure.
Nilgiri Credit, a mid-sized Indian non-bank lender (NBFC), wants to lend to small shops, judging their creditworthiness from their digital payment history. Should it launch, and how?
Format note: Difficulty: Standard. Format: candidate-led, with interviewer dialogue. Industry: Lending (non-bank finance). Region: India. Interview length: about 30 minutes. The company is fictional and all figures are illustrative.
2. Answers to clarifying questions
Give these answers only if the candidate asks. If they ask something not listed, give a sensible answer or say it does not matter here.
If asked: How many shops and what loan size?
Answer: About 200,000 shops by year three, borrowing about INR 2 lakh (200,000) each on average.
If asked: What rate, funding cost, and loss rate?
Answer: 18 percent interest, 8 percent funding cost, and expected credit losses of 4 percent of loans a year.
If asked: What does it cost to run each loan?
Answer: About INR 3,000 a year per loan, because checks are digital.
3. The hypothesis a strong candidate states
Listen for an early, testable guess like this one. It does not need to match word for word.
Small-business loans earn a high rate but carry high losses. My hypothesis is that the product is profitable at expected losses, and the key question is how much losses can rise before it stops paying.
4. A model structure
Compare the candidate's structure with this one. A different split can be just as good if it is clean and fits the problem.
- Profit = interest margin - credit losses - operating costs
- Loan book size
- Net interest income
- Key: Credit losses
- Operating costs
5. The working, step by step
Each step shows how a strong candidate works it out. Share a new fact from it only when the candidate asks or is stuck, and let them do the math: the result in the dark box is what they should reach.
Step 1: Loan book
What a strong candidate does: Candidate: "200,000 loans of INR 200,000 is INR 4,000 crore."
Loan book (INR): 200,000 × 200,000 = 40,000,000,000
Step 2: Net interest income
What a strong candidate does: The 10-point gap between the 18 percent rate and the 8 percent funding cost.
NII (INR a year): 40,000,000,000 × (0.18 - 0.08) = 4,000,000,000
Step 3: Credit losses
What a strong candidate does: 4 percent of loans a year.
Credit losses (INR a year): 40,000,000,000 × 0.04 = 1,600,000,000
Step 4: Operating costs
What a strong candidate does: INR 3,000 per loan.
Operating costs (INR a year): 200,000 × 3,000 = 600,000,000
Step 5: Profit before tax
What a strong candidate does: Candidate: "That is INR 180 crore a year."
Profit (INR a year): 4,000,000,000 - 1,600,000,000 - 600,000,000 = 1,800,000,000
Step 6: Break-even loss rate
What a strong candidate does: Candidate: "How high can losses go before profit is zero?"
Break-even loss rate (%): (4,000,000,000 - 600,000,000) ÷ 40,000,000,000 × 100 = 8.5
Step 7: Curveball: a downturn
What a strong candidate does: Interviewer: "In a downturn, losses on similar loans have doubled." Candidate: "At 8 percent, profit is:"
Profit at 8 percent losses (INR a year): 4,000,000,000 - 40,000,000,000 × 0.08 - 600,000,000 = 200,000,000
The recommendation to listen for
At the end, say: "The CEO walks in. What is your recommendation?"
Launch, but grow carefully and price for risk. First, at expected losses the product earns about INR 180 crore a year on a INR 4,000 crore book. Second, profit disappears only if losses pass 8.5 percent, about twice the expected level, but a downturn could bring losses close to that, leaving only about INR 20 crore. Third, digital payment data is the edge: start with shops that have at least 12 months of steady payment history, lend smaller amounts first, and raise limits for shops that repay. Set a loss-rate trigger at 6 percent to slow lending if losses rise.
Risks a strong answer names: Payment data may not predict repayment in a downturn; Rules on digital lending may tighten.
Next steps: Test the scoring model on past data from 10,000 shops; Launch in three cities with small loan limits.
Strong versus weak
A strong answer
Built the lender's profit from the right lines, found the break-even loss rate, and designed a cautious launch with a trigger.
A weak answer
Focused on the size of the market of small shops and never calculated what credit losses would do to profit.
Score the candidate
Score each criterion from 1 to 5. A 2 or a 4 sits between the descriptions.
This case has no exhibit. Score Exhibit reading on how the candidate used the data you gave them: did they pick out the number that matters and say what it means?
Total
0 out of 25
Score all five criteria to see the band and the feedback template.