This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Calculate Profit of 4-Month Loan at 30% APR states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
##### Scenario
Credit Risk – Short-term personal loan profitability evaluation
##### Question
What is the profit of a 4-month loan with principal $1,000 and 30% APR when there is no compounding, inflation, or opportunity cost?
##### Hints
Convert APR to monthly interest, multiply by four months, total interest equals profit.
Quick Answer: This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Calculate Profit of 4-Month Loan at 30% APR states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Credit Risk — Short-term Personal Loan Profitability
Context
You are evaluating the profit on a simple-interest personal loan. Assume no compounding, inflation, opportunity cost, fees, defaults, or servicing expenses. The principal remains outstanding for the full term and is repaid at maturity.
Question
What is the profit on a 4-month loan with principal $1,000 and a 30% APR under these assumptions?
Constraints & Assumptions
Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.
Clarifying Questions to Ask Guidance
Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
State assumptions about instrumentation, randomization, sample size, and data quality.
Separate descriptive analysis from causal claims.
What a Strong Answer Covers Guidance
A metric framework with primary, guardrail, and diagnostic metrics.
A credible analysis or experiment design with clear assumptions and bias checks.
SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
An actionable recommendation that explains trade-offs and next steps.
Follow-up Questions Guidance
What sanity checks would you run before trusting the result?
How would you handle novelty effects, seasonality, or selection bias?