## 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.