Evaluate credit-limit increase profitability
Company: Capital One
Role: Data Engineer
Category: System Design
Difficulty: medium
Interview Round: Technical Screen
## Business/Analytics Case: Credit Limit Increase Strategy
You are a data scientist supporting a consumer credit business.
### Scenario
The company is considering a **credit-limit increase** program for a specific customer segment (e.g., customers with 6–12 months tenure and mid FICO). You must recommend whether to launch, and how to size/target the program.
### What to do
1. **Clarify the objective**: Is success measured by revenue growth, profit, lower default rate, higher approval rate, retention, or a combination?
2. Build a simple **P&L / unit economics** model:
- Use a profit identity such as:
\(\text{Profit} = \text{Revenue} - \text{Loss} - \text{Operational Cost}\)
- Define what counts as revenue (e.g., interest, interchange, fees) and loss (e.g., charge-offs, fraud, cost of funds).
3. Identify key **levers** and trade-offs (e.g., limit size, eligibility rules, APR/pricing, risk policy, model thresholding).
4. Propose an **evaluation plan** (data needed, segmentation, experiment design) and explain how you would sanity-check numbers with quick mental math.
### Output
Provide a structured recommendation: what to launch (or not), who to target, what metrics to optimize, and how to measure incremental profit and risk.
Quick Answer: This question evaluates a data engineer's competency in business analytics, unit-economics P&L modeling, risk-versus-revenue trade-off analysis, and experiment-driven evaluation of credit product changes.