Add complete financial fraud detection database with Docker, schema, data generation, and SQL exercises

- Docker setup with PostgreSQL 16 and DB-UI web interface
- Comprehensive 20+ table schema with fraud detection patterns
- Idempotent shell scripts for data generation (no Python dependency)
- Realistic geographic data (100 US cities, 210 world cities)
- 5M+ transactions with embedded fraud patterns (velocity, geographic, structuring, etc.)
- Progressive SQL exercises from beginner to advanced fraud detection
- Complete documentation and quick start guide
- Setup and verification scripts

exercises/01-basic-queries/README.md

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+# Level 1: Basic SQL Queries
+
+## Introduction
+Welcome to the Financial Fraud Detection SQL learning path! In this first level, you'll learn the fundamentals of SQL by querying a realistic fraud detection database.
+
+## Learning Objectives
+- Understand SELECT statements
+- Use WHERE clauses for filtering
+- Sort results with ORDER BY
+- Limit result sets
+- Work with basic comparison operators
+
+## Exercises
+
+### Exercise 1.1: View All Customers
+**Objective:** Retrieve all customer records
+
+```sql
+-- Your query here
+SELECT * FROM customers;
+```
+
+**Expected Result:** All customer records with all columns
+
+---
+
+### Exercise 1.2: Find a Specific Customer
+**Objective:** Find customer with customer_id = 1
+
+```sql
+-- Your query here
+SELECT * FROM customers WHERE customer_id = 1;
+```
+
+---
+
+### Exercise 1.3: High-Risk Customers
+**Objective:** Find all customers with a risk_score greater than 80
+
+```sql
+-- Your query here
+
+```
+
+**Hint:** Use the WHERE clause with the > operator
+
+**Solution:**
+```sql
+SELECT customer_id, first_name, last_name, email, risk_score
+FROM customers
+WHERE risk_score > 80
+ORDER BY risk_score DESC;
+```
+
+---
+
+### Exercise 1.4: Recent Registrations
+**Objective:** Find customers who registered in 2024
+
+```sql
+-- Your query here
+
+```
+
+**Hint:** Use WHERE with date comparison
+
+**Solution:**
+```sql
+SELECT customer_id, first_name, last_name, email, registration_date
+FROM customers
+WHERE registration_date >= '2024-01-01'
+ORDER BY registration_date DESC;
+```
+
+---
+
+### Exercise 1.5: Inactive Accounts
+**Objective:** Find all inactive customer accounts
+
+```sql
+-- Your query here
+
+```
+
+**Solution:**
+```sql
+SELECT customer_id, first_name, last_name, email, is_active
+FROM customers
+WHERE is_active = FALSE;
+```
+
+---
+
+### Exercise 1.6: Top 10 Largest Transactions
+**Objective:** Find the 10 largest transactions by amount
+
+```sql
+-- Your query here
+
+```
+
+**Hint:** Use ORDER BY with LIMIT
+
+**Solution:**
+```sql
+SELECT transaction_id, account_id, amount, transaction_date, description
+FROM transactions
+ORDER BY amount DESC
+LIMIT 10;
+```
+
+---
+
+### Exercise 1.7: Flagged Transactions
+**Objective:** Find all transactions that have been flagged for review
+
+```sql
+-- Your query here
+
+```
+
+**Solution:**
+```sql
+SELECT transaction_id, account_id, amount, fraud_score, flagged_reason
+FROM transactions
+WHERE is_flagged = TRUE
+ORDER BY fraud_score DESC;
+```
+
+---
+
+### Exercise 1.8: International Transactions
+**Objective:** Find all international transactions over $1,000
+
+```sql
+-- Your query here
+
+```
+
+**Solution:**
+```sql
+SELECT transaction_id, account_id, amount, country_id, city
+FROM transactions
+WHERE is_international = TRUE AND amount > 1000
+ORDER BY amount DESC;
+```
+
+---
+
+### Exercise 1.9: Specific Merchant Categories
+**Objective:** Find all merchants in the 'Gambling' or 'Cryptocurrency' categories
+
+```sql
+-- Your query here
+
+```
+
+**Hint:** Join merchants with merchant_categories, use IN or OR
+
+**Solution:**
+```sql
+SELECT m.merchant_id, m.merchant_name, mc.category_name, m.risk_rating
+FROM merchants m
+JOIN merchant_categories mc ON m.category_id = mc.category_id
+WHERE mc.category_name IN ('Gambling', 'Cryptocurrency')
+ORDER BY m.risk_rating DESC;
+```
+
+---
+
+### Exercise 1.10: Critical Alerts
+**Objective:** Find all open alerts with CRITICAL severity
+
+```sql
+-- Your query here
+
+```
+
+**Solution:**
+```sql
+SELECT alert_id, customer_id, alert_type, description, alert_date
+FROM alerts
+WHERE severity = 'CRITICAL' AND status = 'OPEN'
+ORDER BY alert_date DESC;
+```
+
+---
+
+## Challenge Exercises
+
+### Challenge 1.1: PEP Customers
+Find all Politically Exposed Persons (PEPs) with high risk scores (> 70)
+
+### Challenge 1.2: Expired Cards
+Find all cards that have expired (expiry_date < current_date)
+
+### Challenge 1.3: Large Cash Advances
+Find all cash advance transactions over $5,000
+
+---
+
+## Next Steps
+Once you're comfortable with these basic queries, move on to:
+- **Level 2:** JOIN operations
+- **Level 3:** Aggregate functions and GROUP BY
+