Alphaeus Mote
b30733ccad
- 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
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
-- 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
-- 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
-- Your query here
Hint: Use the WHERE clause with the > operator
Solution:
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
-- Your query here
Hint: Use WHERE with date comparison
Solution:
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
-- Your query here
Solution:
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
-- Your query here
Hint: Use ORDER BY with LIMIT
Solution:
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
-- Your query here
Solution:
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
-- Your query here
Solution:
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
-- Your query here
Hint: Join merchants with merchant_categories, use IN or OR
Solution:
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
-- Your query here
Solution:
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