### Executive Financial Audit Relational retail banking systems require rigorous transaction monitoring and loan risk assessments to protect capital reserves. In this case study, we walk through auditing active debtor profiles at **ApexBank** to identify critical financial exposures. #### 💡 The Real-World Scenario Imagine you are an Analyst at a retail bank. The Chief Risk Officer (CRO) informs you that loan write-offs spiked by 8% in the previous quarter. You are tasked with scanning the loan ledgers to classify exposure thresholds based on principal thresholds and interest margins. ---\n\n### 🔍 Building the Analytical SQL Pipeline To assess this, we construct a conditional classifier query using a `CASE WHEN` statement and join debtor profiles: 1. **`INNER JOIN`:** Links the `bank_loans` records and customer details in `bank_customers`. 2. **`CASE WHEN`:** Segments risk bands. Any client carrying a loan greater than $50,000 at a high-interest rate (>= 8%) is tagged as **Critical Risk**. 3. **`Arithmetic Yield Calculations`:** Multiplies loan amounts by interest fractions on the fly to estimate annual revenue generation. ---\n\n### ⚠️ Common Pitfalls for Beginners * **The Zero-Division Trap:** When calculating ratios, if you divide columns that may hold zero values, the compiler will crash! Always wrap divisors with protective conditionals or use SQLite's `NULLIF` function. * **Omit Prefix Qualifiers:** Joining tables means multiple columns might share name keys (like `id`). Always write `l.customer_id = c.id` rather than plain `customer_id = id` to prevent the compiler from throwing ambiguous identifier exceptions.