Case 015Private credit and direct lendingHard
Sahajik Finance's loan tape has 2,000 borrowers in four internal grades with a year of default counts. Compute the default rate by grade and the expected loss at 55% loss given default, check whether the grades rank risk correctly, and suggest pricing by grade.
1The situation
Sahajik Finance lends to small businesses and grades each borrower A to D at approval. You receive a tape of 2,000 loans with one year of performance. Grade A: 600 borrowers, 6 defaults, average loan Rs 25 lakh. Grade B: 700 borrowers, 21 defaults, Rs 20 lakh. Grade C: 450 borrowers, 9 defaults, Rs 15 lakh. Grade D: 250 borrowers, 20 defaults, Rs 10 lakh.
Loss given default is 55% across the book. Sahajik's cost of funds is 8.0%, operating costs are 1.5% of loans, and it wants a 2.0% charge for the capital it holds against each loan.
2Your task
Compute default rates and expected loss, test whether the grades rank risk, and propose a lending rate for each grade.
Quick check
What should you do about grade C defaulting less than grade B?
Worked solution
Try it on paper, then open one step at a time.
30-second answerThe answer to give first
Default rates run A 1.0%, B 3.0%, C 2.0% and D 8.0%, and expected loss on the Rs 382.5 crore book is about Rs 4.98 crore, 1.30% of loans. Grade C is out of order, but the gap to B is only about 1.1 standard errors, so I would not re-grade on one year. Price A at 12.05%, B at 13.15%, C at 12.93% on the pooled B and C rate, and D at 15.90%.
Step 1What does the tape say grade by grade?
Divide defaults by borrowers in each grade, then turn default rates into money. Expected loss is exposure times the probability of defaultThe chance that a borrower fails to pay within a set period, usually one year, estimated from past default counts. times the loss given defaultThe share of the loan the lender loses if the borrower defaults, after recoveries from collateral and guarantees., and across the book it comes to about Rs 4.98 crore on Rs 382.5 crore of loans. Grade B carries the most expected loss in rupees because it is large and fairly risky; grade D carries the highest rate but the smallest book.
| Grade | Borrowers | Defaults | Default rate | Standard error | Exposure, Rs crore | Expected loss, Rs crore |
|---|---|---|---|---|---|---|
| Grade A | 600 | 6 | 1.0% | 0.41% | 150.0 | 0.83 |
| Grade B | 700 | 21 | 3.0% | 0.64% | 140.0 | 2.31 |
| Grade C | 450 | 9 | 2.0% | 0.66% | 67.5 | 0.74 |
| Grade D | 250 | 20 | 8.0% | 1.72% | 25.0 | 1.10 |
| Book | 2,000 | 56 | 2.8% | 382.5 | 4.98 |
Step 2Do the grades rank risk correctly?
A rating scale is a promise that each step down is riskier than the one above, like exam grades that should predict who struggles next year. A, B and D behave; C, at 2.0%, defaults less than B at 3.0%, so on this tape the scale is out of order between B and C. If it is real, the lender is overcharging C borrowers and undercharging B borrowers, or approving the wrong mix of both.
Step 3Is the inversion real or noise?
Nine defaults is a small number. The standard error of a default rate is the square root of p times one minus p over n: about 0.64 points for B and 0.66 for C. The one-point gap is only about 1.1 standard errors, well within what chance alone produces, so one year of data cannot tell you the grades are wrong. The next steps are to pull more years and vintages, check whether C borrowers are newer loans that have not had time to default, and look at what drives the grade.
Step 4How would you price each grade?
Build the rate from the same blocks for every grade: cost of funds, operating cost, capital charge, and expected loss, which is the default rate times 55%. That gives A 12.05%, B 13.15% and D 15.90%; for C, until more data arrives, use the pooled B and C default rate of 2.61%, which gives 12.93%. Pooling avoids both mistakes at once: it does not reward C for a result that may be luck, and does not punish it with B's rate.
State the limits. A single capital charge is generous to grade D, which needs more capital against the chance of a bad year, and a flat 55% severity ignores that larger A loans may be better secured. Both are the first refinements to suggest.
Where candidates lose it
The common miss is spotting that C defaults less than B and immediately recommending the grades be swapped. On 9 and 21 defaults the difference is not statistically meaningful, and changing a rating model on one year of noise can be worse than leaving it alone.
The second is computing expected loss per borrower and adding up counts instead of rupees. Grades have different loan sizes, so expected loss has to be weighted by exposure.
What the interviewer asks next
- How many years of data would you want before re-grading, and why?
- Grade C loans are on average eight months old. What does that suggest?
- How would you validate the grades with Python on a larger tape?
- What would a stress year with default rates doubled do to the book's expected loss?
Asked at Jane Street, Credit Risk, London, 2025 (Wall Street Oasis): The case study consisted of credit risk data that I had to analyse using Excel or Python
Company names and figures are illustrative.

