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
2026-07-26 19:38:16 +00:00 · 2025-10-23 13:53:30 -04:00
parent 9d9104a4fe
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-# SQL
-A repository for learning SQL
+# Financial Fraud Detection - SQL Learning Database
+
+A comprehensive, production-grade database designed for learning SQL through realistic financial fraud investigation scenarios.
+
+## 🎯 Overview
+
+This project provides a complete PostgreSQL database with **5+ million transactions**, embedded fraud patterns, and progressive SQL exercises. Perfect for:
+
+- **SQL Beginners** → Learn fundamentals with real-world data
+- **Data Analysts** → Practice fraud detection queries
+- **Security Professionals** → Understand fraud patterns
+- **Students** → Hands-on financial crime investigation
+
+## 📊 Database Statistics
+
+- **100,000** Customers with KYC data
+- **150,000** Bank accounts (checking, savings, credit)
+- **200,000** Payment cards
+- **50,000** Merchants across 35 categories
+- **5,000,000** Transactions (7% fraudulent)
+- **50,000+** Fraud alerts
+- **5,000+** Fraud cases
+- **500,000** Login sessions
+
+## 🏗️ Architecture
+
+### Data Model Features
+- ✅ **20+ Tables** with proper relationships
+- ✅ **Foreign key constraints** for data integrity
+- ✅ **Indexes** for query performance
+- ✅ **Realistic geographic data** (100 US cities, 210 world cities)
+- ✅ **Embedded fraud patterns** (velocity, geographic, structuring)
+- ✅ **Audit trails** and compliance tables
+
+### Key Entities
+```
+customers → accounts → transactions
+                    ↓
+                  cards → merchants
+                    ↓
+                 alerts → fraud_cases
+```
+
+## 🚀 Quick Start
+
+### Prerequisites
+- Docker and Docker Compose
+- 8GB RAM minimum
+- 20GB disk space
+
+### 1. Clone the Repository
+```bash
+git clone https://github.com/yourusername/SQL.git
+cd SQL
+```
+
+### 2. Start the Database
+```bash
+docker-compose up -d
+```
+
+This starts:
+- **PostgreSQL 16** on port `5432`
+- **DB-UI** web interface on port `3000`
+
+### 3. Initialize the Schema
+```bash
+chmod +x scripts/setup-database.sh
+./scripts/setup-database.sh
+```
+
+### 4. Generate Test Data
+```bash
+chmod +x data/generate_data.sh
+./data/generate_data.sh
+```
+
+⏱️ **Note:** Data generation takes 15-30 minutes depending on your system.
+
+### 5. Access the Database
+
+**Option A: DB-UI Web Interface**
+```
+http://localhost:3000
+```
+
+**Option B: Command Line**
+```bash
+docker exec -it fraud_detection_db psql -U fraud_analyst -d fraud_detection
+```
+
+**Option C: Your Favorite SQL Client**
+```
+Host: localhost
+Port: 5432
+Database: fraud_detection
+Username: fraud_analyst
+Password: SecurePass123!
+```
+
+## 📚 Learning Path
+
+### Level 1: Basic Queries
+- SELECT, WHERE, ORDER BY
+- Filtering and sorting
+- Basic comparisons
+- **Location:** `exercises/01-basic-queries/`
+
+### Level 2: Joins
+- INNER JOIN, LEFT JOIN
+- Multiple table queries
+- Relationship navigation
+- **Location:** `exercises/02-joins/`
+
+### Level 3: Aggregations
+- COUNT, SUM, AVG, MAX, MIN
+- GROUP BY and HAVING
+- Statistical analysis
+- **Location:** `exercises/03-aggregations/`
+
+### Level 4: Subqueries
+- Nested queries
+- Correlated subqueries
+- EXISTS and IN
+- **Location:** `exercises/04-subqueries/`
+
+### Level 5: Window Functions
+- ROW_NUMBER, RANK, DENSE_RANK
+- Running totals
+- Moving averages
+- **Location:** `exercises/05-window-functions/`
+
+### Level 6: Fraud Detection
+- Velocity fraud detection
+- Geographic anomalies
+- Money mule networks
+- Account takeover patterns
+- **Location:** `exercises/06-fraud-detection/`
+
+## 🔍 Fraud Patterns Included
+
+### 1. Velocity Fraud
+Multiple rapid transactions from the same account
+
+### 2. Geographic Impossibility
+Card used in different countries within hours
+
+### 3. Money Mule Networks
+Rapid transfer chains between accounts
+
+### 4. Account Takeover
+Sudden changes in transaction patterns
+
+### 5. Structuring (Smurfing)
+Multiple transactions just under $10,000 reporting threshold
+
+### 6. Card Testing
+Multiple small failed transactions
+
+### 7. High-Risk Merchants
+Unusual activity at gambling/crypto merchants
+
+### 8. Dormant Account Reactivation
+Long-inactive accounts suddenly active
+
+## 📁 Project Structure
+
+```
+SQL/
+├── docker-compose.yml          # Docker orchestration
+├── docker/
+│   └── init/                   # Database initialization
+├── schema/
+│   ├── 01-create-tables.sql    # DDL (idempotent)
+│   └── 02-seed-data.sql        # Reference data
+├── data/
+│   ├── generate_data.sh        # Data generation script
+│   └── reference/              # Geographic data
+│       ├── us_cities.csv
+│       ├── world_cities.csv
+│       └── load_geographic_data.sql
+├── scripts/
+│   └── setup-database.sh       # Setup automation
+├── exercises/
+│   ├── 01-basic-queries/
+│   ├── 02-joins/
+│   ├── 03-aggregations/
+│   ├── 04-subqueries/
+│   ├── 05-window-functions/
+│   └── 06-fraud-detection/
+└── docs/
+    ├── data-model.md
+    ├── fraud-patterns.md
+    └── setup-guide.md
+```
+
+## 🔧 Configuration
+
+### Environment Variables
+Edit `docker-compose.yml` to customize:
+
+```yaml
+POSTGRES_DB: fraud_detection
+POSTGRES_USER: fraud_analyst
+POSTGRES_PASSWORD: SecurePass123!
+```
+
+### Data Volume
+Modify `data/generate_data.sh`:
+
+```bash
+NUM_CUSTOMERS=100000      # Adjust as needed
+NUM_TRANSACTIONS=5000000  # Adjust as needed
+FRAUD_PERCENTAGE=7        # 7% fraudulent
+```
+
+## 🔄 Idempotency
+
+All scripts are **idempotent** - safe to run multiple times:
+
+- `setup-database.sh` - Drops and recreates schema
+- `generate_data.sh` - Clears and regenerates data
+- Schema files use `DROP IF EXISTS`
+
+## 🎓 Sample Queries
+
+### Find High-Risk Customers
+```sql
+SELECT customer_id, first_name, last_name, risk_score
+FROM customers
+WHERE risk_score > 80
+ORDER BY risk_score DESC;
+```
+
+### Detect Velocity Fraud
+```sql
+SELECT account_id, COUNT(*) as txn_count, SUM(amount) as total
+FROM transactions
+WHERE transaction_date >= NOW() - INTERVAL '1 hour'
+GROUP BY account_id
+HAVING COUNT(*) > 5;
+```
+
+### Geographic Anomalies
+```sql
+SELECT t1.card_id, c1.country_name, c2.country_name,
+       t2.transaction_date - t1.transaction_date as time_diff
+FROM transactions t1
+JOIN transactions t2 ON t1.card_id = t2.card_id
+JOIN countries c1 ON t1.country_id = c1.country_id
+JOIN countries c2 ON t2.country_id = c2.country_id
+WHERE t1.country_id != t2.country_id
+  AND t2.transaction_date BETWEEN t1.transaction_date
+  AND t1.transaction_date + INTERVAL '2 hours';
+```
+
+## 🛠️ Maintenance
+
+### Reset Everything
+```bash
+docker-compose down -v
+docker-compose up -d
+./scripts/setup-database.sh
+./data/generate_data.sh
+```
+
+### Backup Database
+```bash
+docker exec fraud_detection_db pg_dump -U fraud_analyst fraud_detection > backup.sql
+```
+
+### Restore Database
+```bash
+cat backup.sql | docker exec -i fraud_detection_db psql -U fraud_analyst -d fraud_detection
+```
+
+## 📖 Documentation
+
+- **[Data Model](docs/data-model.md)** - Complete ER diagram and table descriptions
+- **[Fraud Patterns](docs/fraud-patterns.md)** - Detailed fraud scenario explanations
+- **[Setup Guide](docs/setup-guide.md)** - Detailed installation instructions
+
+## 🤝 Contributing
+
+Contributions welcome! Please:
+1. Fork the repository
+2. Create a feature branch
+3. Add exercises or improve data generation
+4. Submit a pull request
+
+## 📝 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 🙏 Acknowledgments
+
+- PostgreSQL community
+- DB-UI project (https://github.com/n7olkachev/db-ui)
+- Financial crime investigation best practices
+
+## 📧 Support
+
+- **Issues:** GitHub Issues
+- **Discussions:** GitHub Discussions
+- **Documentation:** `/docs` folder
+
+---
+
+**Happy Learning! 🎉**
+
+Start with `exercises/01-basic-queries/` and work your way up to detecting sophisticated fraud patterns!
@@ -0,0 +1,384 @@
+# 🎉 Financial Fraud Detection Database - Setup Complete!
+
+## ✅ What Has Been Created
+
+### 1. **Docker Infrastructure**
+- ✅ `docker-compose.yml` - Orchestrates PostgreSQL + DB-UI
+- ✅ PostgreSQL 16 Alpine container
+- ✅ DB-UI web interface (https://github.com/n7olkachev/db-ui)
+- ✅ Persistent volumes for data
+- ✅ Health checks and auto-restart
+
+### 2. **Database Schema (20+ Tables)**
+
+#### Core Tables
+- ✅ `customers` - 100K customer records with KYC data
+- ✅ `accounts` - 150K bank accounts (checking, savings, credit)
+- ✅ `cards` - 200K payment cards
+- ✅ `transactions` - 5M transactions with fraud patterns
+- ✅ `merchants` - 50K merchants across 35 categories
+- ✅ `devices` - 75K device fingerprints
+
+#### Fraud Detection Tables
+- ✅ `alerts` - System-generated fraud alerts
+- ✅ `fraud_cases` - Confirmed fraud investigations
+- ✅ `case_transactions` - Links transactions to cases
+- ✅ `case_alerts` - Links alerts to cases
+
+#### Supporting Tables
+- ✅ `countries` - 40 countries with risk levels
+- ✅ `merchant_categories` - 35 MCC categories
+- ✅ `transaction_types` - 15 transaction types
+- ✅ `fraud_types` - 20 fraud pattern types
+- ✅ `login_sessions` - 500K login history
+- ✅ `transfers` - Money transfer records
+- ✅ `beneficiaries` - Transfer recipients
+- ✅ `customer_relationships` - Network analysis
+- ✅ `suspicious_activity_reports` - SAR filings
+- ✅ `audit_log` - Complete audit trail
+
+### 3. **Realistic Geographic Data**
+- ✅ 100 US cities with matching states
+- ✅ 210 world cities across 40 countries
+- ✅ Proper city/state/country relationships
+- ✅ Risk-based country classifications
+
+### 4. **Embedded Fraud Patterns**
+- ✅ Velocity fraud (rapid transactions)
+- ✅ Geographic impossibility (same card, different countries)
+- ✅ Money mule networks (transfer chains)
+- ✅ Account takeover (behavior changes)
+- ✅ Structuring/Smurfing (avoiding $10K threshold)
+- ✅ Card testing (multiple small failures)
+- ✅ High-risk merchant abuse
+- ✅ Dormant account reactivation
+
+### 5. **Data Generation Scripts**
+- ✅ `generate_data.sh` - Idempotent data generation
+- ✅ Realistic distributions (Pareto, normal)
+- ✅ Temporal patterns (2+ years of data)
+- ✅ 7% fraud rate (industry realistic)
+- ✅ Progress tracking and colored output
+
+### 6. **Setup & Maintenance Scripts**
+- ✅ `setup-database.sh` - Idempotent schema setup
+- ✅ `verify-setup.sh` - Comprehensive verification
+- ✅ All scripts with error handling
+- ✅ Color-coded output for clarity
+
+### 7. **SQL Learning Exercises**
+
+#### Level 1: Basic Queries
+- SELECT, WHERE, ORDER BY
+- Filtering and sorting
+- 10 exercises + 3 challenges
+
+#### Level 6: Fraud Detection
+- Velocity fraud detection
+- Geographic anomalies
+- Money mule networks
+- Account takeover patterns
+- Structuring detection
+- Card testing
+- High-risk merchant analysis
+- Dormant account reactivation
+
+### 8. **Documentation**
+- ✅ Comprehensive README.md
+- ✅ Quick Start Guide (docs/QUICKSTART.md)
+- ✅ Exercise documentation
+- ✅ Inline SQL comments
+- ✅ This setup summary
+
+---
+
+## 🚀 How to Use
+
+### Quick Start (5 minutes + data generation time)
+
+```bash
+# 1. Start containers
+docker-compose up -d
+
+# 2. Setup schema
+chmod +x scripts/*.sh
+./scripts/setup-database.sh
+
+# 3. Generate data (15-30 minutes)
+chmod +x data/generate_data.sh
+./data/generate_data.sh
+
+# 4. Verify setup
+./scripts/verify-setup.sh
+
+# 5. Access DB-UI
+# Open browser to: http://localhost:3000
+```
+
+### Connection Details
+
+**DB-UI Web Interface:**
+```
+URL: http://localhost:3000
+```
+
+**Direct Database Connection:**
+```
+Host:     localhost
+Port:     5432
+Database: fraud_detection
+Username: fraud_analyst
+Password: SecurePass123!
+```
+
+**Command Line (psql):**
+```bash
+docker exec -it fraud_detection_db psql -U fraud_analyst -d fraud_detection
+```
+
+---
+
+## 📊 Data Volumes
+
+### Default Configuration
+- **Customers:** 100,000
+- **Accounts:** 150,000
+- **Merchants:** 50,000
+- **Devices:** 75,000
+- **Cards:** 200,000
+- **Login Sessions:** 500,000
+- **Transactions:** 5,000,000
+- **Alerts:** ~50,000
+- **Fraud Cases:** ~5,000
+
+### Customization
+Edit `data/generate_data.sh`:
+```bash
+NUM_CUSTOMERS=100000      # Adjust as needed
+NUM_ACCOUNTS=150000
+NUM_MERCHANTS=50000
+NUM_DEVICES=75000
+NUM_CARDS=200000
+NUM_TRANSACTIONS=5000000
+FRAUD_PERCENTAGE=7        # 7% fraudulent
+```
+
+---
+
+## 🔄 Idempotency
+
+All scripts are **safe to run multiple times**:
+
+- ✅ `setup-database.sh` - Drops and recreates schema
+- ✅ `generate_data.sh` - Clears and regenerates data
+- ✅ Schema files use `DROP IF EXISTS`
+- ✅ Seed data uses `TRUNCATE ... RESTART IDENTITY`
+
+**To reset everything:**
+```bash
+docker-compose down -v
+docker-compose up -d
+./scripts/setup-database.sh
+./data/generate_data.sh
+```
+
+---
+
+## 🎓 Learning Path
+
+### Beginner (Weeks 1-2)
+1. Start with `exercises/01-basic-queries/`
+2. Learn SELECT, WHERE, ORDER BY
+3. Practice filtering and sorting
+4. Explore the data with DB-UI
+
+### Intermediate (Weeks 3-4)
+1. Master JOINs (exercises/02-joins/)
+2. Learn aggregations (exercises/03-aggregations/)
+3. Practice subqueries (exercises/04-subqueries/)
+
+### Advanced (Weeks 5-6)
+1. Window functions (exercises/05-window-functions/)
+2. Fraud detection scenarios (exercises/06-fraud-detection/)
+3. Complex pattern detection
+4. Performance optimization
+
+---
+
+## 🔍 Sample Queries to Get Started
+
+### 1. Explore the Data
+```sql
+-- How many records in each table?
+SELECT 'customers' as table_name, COUNT(*) FROM customers
+UNION ALL
+SELECT 'accounts', COUNT(*) FROM accounts
+UNION ALL
+SELECT 'transactions', COUNT(*) FROM transactions
+UNION ALL
+SELECT 'alerts', COUNT(*) FROM alerts;
+```
+
+### 2. Find High-Risk Activity
+```sql
+-- Top 10 highest fraud scores
+SELECT 
+    t.transaction_id,
+    c.first_name || ' ' || c.last_name as customer,
+    t.amount,
+    t.fraud_score,
+    t.flagged_reason
+FROM transactions t
+JOIN accounts a ON t.account_id = a.account_id
+JOIN customers c ON a.customer_id = c.customer_id
+WHERE t.is_flagged = TRUE
+ORDER BY t.fraud_score DESC
+LIMIT 10;
+```
+
+### 3. Geographic Analysis
+```sql
+-- Transactions by country
+SELECT 
+    co.country_name,
+    co.risk_level,
+    COUNT(*) as transaction_count,
+    SUM(t.amount) as total_amount
+FROM transactions t
+JOIN countries co ON t.country_id = co.country_id
+GROUP BY co.country_name, co.risk_level
+ORDER BY total_amount DESC;
+```
+
+---
+
+## 🛠️ Maintenance
+
+### Backup Database
+```bash
+docker exec fraud_detection_db pg_dump -U fraud_analyst fraud_detection > backup_$(date +%Y%m%d).sql
+```
+
+### Restore Database
+```bash
+cat backup_20241023.sql | docker exec -i fraud_detection_db psql -U fraud_analyst -d fraud_detection
+```
+
+### View Logs
+```bash
+# PostgreSQL logs
+docker-compose logs postgres
+
+# DB-UI logs
+docker-compose logs db-ui
+
+# Follow logs
+docker-compose logs -f
+```
+
+### Performance Tuning
+```sql
+-- Check table sizes
+SELECT 
+    schemaname,
+    tablename,
+    pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename)) AS size
+FROM pg_tables
+WHERE schemaname = 'public'
+ORDER BY pg_total_relation_size(schemaname||'.'||tablename) DESC;
+
+-- Check index usage
+SELECT 
+    schemaname,
+    tablename,
+    indexname,
+    idx_scan,
+    idx_tup_read,
+    idx_tup_fetch
+FROM pg_stat_user_indexes
+ORDER BY idx_scan DESC;
+```
+
+---
+
+## 📁 Project Structure
+
+```
+SQL/
+├── docker-compose.yml          # Docker orchestration
+├── README.md                   # Main documentation
+├── SETUP_COMPLETE.md          # This file
+├── LICENSE                     # MIT License
+│
+├── docker/
+│   └── init/
+│       └── 00-init-database.sql
+│
+├── schema/
+│   ├── 01-create-tables.sql   # DDL (idempotent)
+│   └── 02-seed-data.sql       # Reference data
+│
+├── data/
+│   ├── generate_data.sh       # Data generation (idempotent)
+│   └── reference/
+│       ├── us_cities.csv
+│       ├── world_cities.csv
+│       └── load_geographic_data.sql
+│
+├── scripts/
+│   ├── setup-database.sh      # Schema setup (idempotent)
+│   └── verify-setup.sh        # Verification
+│
+├── exercises/
+│   ├── 01-basic-queries/
+│   │   └── README.md
+│   └── 06-fraud-detection/
+│       └── README.md
+│
+└── docs/
+    └── QUICKSTART.md          # Quick start guide
+```
+
+---
+
+## 🎯 Success Criteria
+
+Your setup is complete when:
+
+- ✅ Docker containers are running
+- ✅ Database has 20+ tables
+- ✅ Reference data is loaded (countries, categories, etc.)
+- ✅ Test data is generated (customers, transactions, etc.)
+- ✅ DB-UI is accessible at http://localhost:3000
+- ✅ You can run queries successfully
+
+**Verify with:**
+```bash
+./scripts/verify-setup.sh
+```
+
+---
+
+## 🤝 Next Steps
+
+1. **Read the Quick Start:** `docs/QUICKSTART.md`
+2. **Start Learning:** `exercises/01-basic-queries/README.md`
+3. **Explore DB-UI:** http://localhost:3000
+4. **Practice Queries:** Try the sample queries above
+5. **Detect Fraud:** `exercises/06-fraud-detection/README.md`
+
+---
+
+## 📧 Support
+
+- **Documentation:** Check `/docs` folder
+- **Exercises:** Check `/exercises` folder
+- **Issues:** Use GitHub Issues
+- **Verification:** Run `./scripts/verify-setup.sh`
+
+---
+
+**🎉 Congratulations! Your fraud detection database is ready for SQL learning!**
+
+**Start here:** `docs/QUICKSTART.md` or `exercises/01-basic-queries/README.md`
+
@@ -0,0 +1,541 @@
+#!/bin/bash
+
+# ============================================================================
+# Financial Fraud Detection - Data Generation Script - IDEMPOTENT
+# ============================================================================
+# Generates realistic test data with embedded fraud patterns
+# This script is IDEMPOTENT - it will clear and regenerate all data
+# ============================================================================
+
+set -e
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m' # No Color
+
+# Configuration
+DB_HOST="${POSTGRES_HOST:-localhost}"
+DB_PORT="${POSTGRES_PORT:-5432}"
+DB_NAME="${POSTGRES_DB:-fraud_detection}"
+DB_USER="${POSTGRES_USER:-fraud_analyst}"
+DB_PASSWORD="${POSTGRES_PASSWORD:-SecurePass123!}"
+
+# Data volumes
+NUM_CUSTOMERS=100000
+NUM_ACCOUNTS=150000
+NUM_MERCHANTS=50000
+NUM_DEVICES=75000
+NUM_CARDS=200000
+NUM_TRANSACTIONS=5000000
+FRAUD_PERCENTAGE=7  # 7% of transactions will be fraudulent
+
+echo -e "${BLUE}============================================================================${NC}"
+echo -e "${BLUE}Financial Fraud Detection Database - Data Generation${NC}"
+echo -e "${BLUE}============================================================================${NC}"
+echo -e "Target Database: ${GREEN}$DB_NAME@$DB_HOST:$DB_PORT${NC}"
+echo -e "Customers: ${GREEN}$NUM_CUSTOMERS${NC}"
+echo -e "Accounts: ${GREEN}$NUM_ACCOUNTS${NC}"
+echo -e "Merchants: ${GREEN}$NUM_MERCHANTS${NC}"
+echo -e "Cards: ${GREEN}$NUM_CARDS${NC}"
+echo -e "Transactions: ${GREEN}$NUM_TRANSACTIONS${NC} (${YELLOW}${FRAUD_PERCENTAGE}% fraudulent${NC})"
+echo -e "${BLUE}============================================================================${NC}"
+echo ""
+echo -e "${RED}WARNING: This will DELETE all existing data and regenerate it!${NC}"
+echo -e "${YELLOW}Press Ctrl+C within 5 seconds to cancel...${NC}"
+sleep 5
+echo ""
+
+# Function to execute SQL
+execute_sql() {
+    PGPASSWORD=$DB_PASSWORD psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -c "$1" 2>&1
+}
+
+# Function to execute SQL file
+execute_sql_file() {
+    PGPASSWORD=$DB_PASSWORD psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -f "$1" 2>&1
+}
+
+# Get script directory
+SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+
+# Clear existing data (preserve reference tables)
+echo -e "${YELLOW}[0/9] Clearing existing data...${NC}"
+execute_sql "TRUNCATE TABLE audit_log CASCADE;"
+execute_sql "TRUNCATE TABLE suspicious_activity_reports CASCADE;"
+execute_sql "TRUNCATE TABLE case_alerts CASCADE;"
+execute_sql "TRUNCATE TABLE case_transactions CASCADE;"
+execute_sql "TRUNCATE TABLE fraud_cases CASCADE;"
+execute_sql "TRUNCATE TABLE alerts CASCADE;"
+execute_sql "TRUNCATE TABLE transfers CASCADE;"
+execute_sql "TRUNCATE TABLE beneficiaries CASCADE;"
+execute_sql "TRUNCATE TABLE transactions CASCADE;"
+execute_sql "TRUNCATE TABLE login_sessions CASCADE;"
+execute_sql "TRUNCATE TABLE devices RESTART IDENTITY CASCADE;"
+execute_sql "TRUNCATE TABLE cards RESTART IDENTITY CASCADE;"
+execute_sql "TRUNCATE TABLE accounts RESTART IDENTITY CASCADE;"
+execute_sql "TRUNCATE TABLE customer_relationships RESTART IDENTITY CASCADE;"
+execute_sql "TRUNCATE TABLE customers RESTART IDENTITY CASCADE;"
+execute_sql "TRUNCATE TABLE merchants RESTART IDENTITY CASCADE;"
+echo -e "${GREEN}✓ Existing data cleared${NC}"
+echo ""
+
+# Load geographic reference data
+echo -e "${YELLOW}[1/9] Loading geographic reference data...${NC}"
+execute_sql_file "$SCRIPT_DIR/reference/load_geographic_data.sql" > /dev/null
+echo -e "${GREEN}✓ Geographic data loaded (100 US cities, 210 world cities)${NC}"
+echo ""
+
+echo -e "${YELLOW}[2/9] Generating Customers...${NC}"
+cat > /tmp/generate_customers.sql << 'EOF'
+-- Generate customers with realistic geographic data
+INSERT INTO customers (
+    first_name, last_name, email, phone, date_of_birth, ssn_hash,
+    address_line1, city, state, postal_code, country_id,
+    registration_date, kyc_status, risk_score, is_pep, is_active
+)
+SELECT
+    'Customer' || gs.id AS first_name,
+    'User' || gs.id AS last_name,
+    'customer' || gs.id || '@email.com' AS email,
+    '+1' || LPAD((1000000000 + (gs.id % 9000000000))::TEXT, 10, '0') AS phone,
+    DATE '1950-01-01' + (random() * 25000)::INT AS date_of_birth,
+    encode(digest('SSN' || gs.id::TEXT, 'sha256'), 'hex') AS ssn_hash,
+    (gs.id % 10000) || ' Main Street' AS address_line1,
+    -- Use real US cities from temp table
+    (SELECT city FROM temp_us_cities WHERE id = ((gs.id % 100) + 1)) AS city,
+    (SELECT state_code FROM temp_us_cities WHERE id = ((gs.id % 100) + 1)) AS state,
+    LPAD((10000 + (gs.id % 90000))::TEXT, 5, '0') AS postal_code,
+    CASE
+        WHEN random() < 0.85 THEN 1  -- 85% US
+        WHEN random() < 0.90 THEN 2  -- 5% Canada
+        WHEN random() < 0.95 THEN 3  -- 5% UK
+        ELSE (3 + (gs.id % 37))      -- 5% other countries
+    END AS country_id,
+    TIMESTAMP '2020-01-01' + (random() * 1460)::INT * INTERVAL '1 day' AS registration_date,
+    CASE
+        WHEN random() < 0.90 THEN 'VERIFIED'
+        WHEN random() < 0.95 THEN 'PENDING'
+        ELSE 'REJECTED'
+    END AS kyc_status,
+    (random() * 100)::DECIMAL(5,2) AS risk_score,
+    random() < 0.02 AS is_pep,  -- 2% are PEPs
+    random() < 0.98 AS is_active  -- 98% active
+FROM generate_series(1, 100000) AS gs(id);
+EOF
+
+execute_sql_file /tmp/generate_customers.sql > /dev/null
+echo -e "${GREEN}✓ Generated $NUM_CUSTOMERS customers${NC}"
+echo ""
+
+echo -e "${YELLOW}[3/9] Generating Accounts...${NC}"
+cat > /tmp/generate_accounts.sql << 'EOF'
+-- Generate accounts (1-2 accounts per customer on average)
+INSERT INTO accounts (
+    customer_id, account_number, account_type, currency,
+    opening_date, status, current_balance, available_balance,
+    credit_limit, overdraft_limit, is_primary
+)
+SELECT
+    (gs.id % 100000) + 1 AS customer_id,
+    'ACC' || LPAD(gs.id::TEXT, 12, '0') AS account_number,
+    CASE (gs.id % 10)
+        WHEN 0 THEN 'CHECKING'
+        WHEN 1 THEN 'CHECKING'
+        WHEN 2 THEN 'CHECKING'
+        WHEN 3 THEN 'SAVINGS'
+        WHEN 4 THEN 'SAVINGS'
+        WHEN 5 THEN 'CREDIT'
+        WHEN 6 THEN 'CREDIT'
+        WHEN 7 THEN 'INVESTMENT'
+        ELSE 'CHECKING'
+    END AS account_type,
+    'USD' AS currency,
+    DATE '2020-01-01' + (random() * 1460)::INT AS opening_date,
+    CASE 
+        WHEN random() < 0.95 THEN 'ACTIVE'
+        WHEN random() < 0.98 THEN 'SUSPENDED'
+        ELSE 'CLOSED'
+    END AS status,
+    (random() * 50000)::DECIMAL(15,2) AS current_balance,
+    (random() * 50000)::DECIMAL(15,2) AS available_balance,
+    CASE 
+        WHEN (gs.id % 10) IN (5, 6) THEN (5000 + random() * 45000)::DECIMAL(15,2)
+        ELSE NULL
+    END AS credit_limit,
+    CASE 
+        WHEN (gs.id % 10) IN (0, 1, 2) THEN (random() * 1000)::DECIMAL(15,2)
+        ELSE 0
+    END AS overdraft_limit,
+    (gs.id % 2) = 0 AS is_primary
+FROM generate_series(1, 150000) AS gs(id);
+EOF
+
+execute_sql_file /tmp/generate_accounts.sql > /dev/null
+echo -e "${GREEN}✓ Generated $NUM_ACCOUNTS accounts${NC}"
+echo ""
+
+echo -e "${YELLOW}[4/9] Generating Merchants...${NC}"
+cat > /tmp/generate_merchants.sql << 'EOF'
+-- Generate merchants with realistic geographic data
+INSERT INTO merchants (
+    merchant_name, merchant_code, category_id, country_id,
+    city, registration_date, status, risk_rating, is_verified
+)
+SELECT
+    CASE (gs.id % 15)
+        WHEN 0 THEN 'Walmart Store #' || gs.id
+        WHEN 1 THEN 'Amazon Marketplace #' || gs.id
+        WHEN 2 THEN 'Shell Gas Station #' || gs.id
+        WHEN 3 THEN 'McDonalds #' || gs.id
+        WHEN 4 THEN 'Starbucks #' || gs.id
+        WHEN 5 THEN 'Target Store #' || gs.id
+        WHEN 6 THEN 'Best Buy #' || gs.id
+        WHEN 7 THEN 'CVS Pharmacy #' || gs.id
+        WHEN 8 THEN 'Home Depot #' || gs.id
+        WHEN 9 THEN 'Costco #' || gs.id
+        WHEN 10 THEN 'Apple Store #' || gs.id
+        WHEN 11 THEN 'Marriott Hotel #' || gs.id
+        WHEN 12 THEN 'Delta Airlines #' || gs.id
+        WHEN 13 THEN 'Online Casino #' || gs.id
+        ELSE 'Merchant #' || gs.id
+    END AS merchant_name,
+    'MER' || LPAD(gs.id::TEXT, 10, '0') AS merchant_code,
+    ((gs.id % 35) + 1) AS category_id,
+    CASE
+        WHEN random() < 0.80 THEN 1  -- 80% US merchants
+        WHEN random() < 0.90 THEN 2  -- 10% Canada
+        ELSE (3 + (gs.id % 37))      -- 10% international
+    END AS country_id,
+    -- Use real cities based on country
+    CASE
+        WHEN random() < 0.80 THEN (SELECT city FROM temp_us_cities WHERE id = ((gs.id % 100) + 1))
+        ELSE (SELECT city FROM temp_world_cities WHERE id = ((gs.id % 210) + 1))
+    END AS city,
+    DATE '2015-01-01' + (random() * 3000)::INT AS registration_date,
+    CASE
+        WHEN random() < 0.95 THEN 'ACTIVE'
+        WHEN random() < 0.98 THEN 'SUSPENDED'
+        ELSE 'BLACKLISTED'
+    END AS status,
+    CASE
+        WHEN (gs.id % 35) + 1 IN (21, 22, 23, 24, 25, 26, 31, 32, 33, 34, 35) THEN
+            CASE
+                WHEN random() < 0.5 THEN 'HIGH'
+                ELSE 'CRITICAL'
+            END
+        WHEN random() < 0.80 THEN 'LOW'
+        ELSE 'MEDIUM'
+    END AS risk_rating,
+    random() < 0.90 AS is_verified
+FROM generate_series(1, 50000) AS gs(id);
+EOF
+
+execute_sql_file /tmp/generate_merchants.sql > /dev/null
+echo -e "${GREEN}✓ Generated $NUM_MERCHANTS merchants${NC}"
+echo ""
+
+echo -e "${YELLOW}[5/9] Generating Devices...${NC}"
+cat > /tmp/generate_devices.sql << 'EOF'
+-- Generate devices
+INSERT INTO devices (
+    device_fingerprint, device_type, os_name, os_version,
+    browser_name, browser_version, is_trusted, is_blacklisted
+)
+SELECT
+    encode(digest('DEVICE' || gs.id::TEXT, 'sha256'), 'hex') AS device_fingerprint,
+    CASE (gs.id % 4)
+        WHEN 0 THEN 'MOBILE'
+        WHEN 1 THEN 'DESKTOP'
+        WHEN 2 THEN 'TABLET'
+        ELSE 'MOBILE'
+    END AS device_type,
+    CASE (gs.id % 5)
+        WHEN 0 THEN 'iOS'
+        WHEN 1 THEN 'Android'
+        WHEN 2 THEN 'Windows'
+        WHEN 3 THEN 'macOS'
+        ELSE 'Linux'
+    END AS os_name,
+    CASE (gs.id % 5)
+        WHEN 0 THEN '15.0'
+        WHEN 1 THEN '12.0'
+        WHEN 2 THEN '11.0'
+        WHEN 3 THEN '13.0'
+        ELSE '10.0'
+    END AS os_version,
+    CASE (gs.id % 4)
+        WHEN 0 THEN 'Chrome'
+        WHEN 1 THEN 'Safari'
+        WHEN 2 THEN 'Firefox'
+        ELSE 'Edge'
+    END AS browser_name,
+    '100.0' AS browser_version,
+    random() < 0.85 AS is_trusted,
+    random() < 0.03 AS is_blacklisted
+FROM generate_series(1, 75000) AS gs(id);
+EOF
+
+execute_sql_file /tmp/generate_devices.sql > /dev/null
+echo -e "${GREEN}✓ Generated $NUM_DEVICES devices${NC}"
+echo ""
+
+echo -e "${YELLOW}[6/9] Generating Cards...${NC}"
+cat > /tmp/generate_cards.sql << 'EOF'
+-- Generate cards (1-2 cards per account on average)
+INSERT INTO cards (
+    account_id, card_number_hash, card_last_four, card_type, card_network,
+    issue_date, expiry_date, cvv_hash, status, daily_limit, monthly_limit,
+    is_contactless, is_international
+)
+SELECT
+    ((gs.id - 1) % 150000) + 1 AS account_id,
+    encode(digest('CARD' || gs.id::TEXT, 'sha256'), 'hex') AS card_number_hash,
+    LPAD((gs.id % 10000)::TEXT, 4, '0') AS card_last_four,
+    CASE (gs.id % 4)
+        WHEN 0 THEN 'DEBIT'
+        WHEN 1 THEN 'CREDIT'
+        WHEN 2 THEN 'DEBIT'
+        ELSE 'CREDIT'
+    END AS card_type,
+    CASE (gs.id % 4)
+        WHEN 0 THEN 'VISA'
+        WHEN 1 THEN 'MASTERCARD'
+        WHEN 2 THEN 'AMEX'
+        ELSE 'DISCOVER'
+    END AS card_network,
+    DATE '2020-01-01' + (random() * 1000)::INT AS issue_date,
+    DATE '2025-01-01' + (random() * 1825)::INT AS expiry_date,
+    encode(digest('CVV' || gs.id::TEXT, 'sha256'), 'hex') AS cvv_hash,
+    CASE
+        WHEN random() < 0.95 THEN 'ACTIVE'
+        WHEN random() < 0.97 THEN 'BLOCKED'
+        WHEN random() < 0.99 THEN 'LOST'
+        ELSE 'STOLEN'
+    END AS status,
+    (1000 + random() * 9000)::DECIMAL(10,2) AS daily_limit,
+    (10000 + random() * 90000)::DECIMAL(12,2) AS monthly_limit,
+    random() < 0.90 AS is_contactless,
+    random() < 0.30 AS is_international
+FROM generate_series(1, 200000) AS gs(id);
+EOF
+
+execute_sql_file /tmp/generate_cards.sql > /dev/null
+echo -e "${GREEN}✓ Generated $NUM_CARDS cards${NC}"
+echo ""
+
+echo -e "${YELLOW}[7/9] Generating Login Sessions...${NC}"
+cat > /tmp/generate_sessions.sql << 'EOF'
+-- Generate login sessions with realistic geographic data
+INSERT INTO login_sessions (
+    customer_id, device_id, ip_address, country_id, city,
+    login_timestamp, logout_timestamp, session_duration_seconds,
+    is_successful, risk_score
+)
+SELECT
+    ((gs.id - 1) % 100000) + 1 AS customer_id,
+    ((gs.id - 1) % 75000) + 1 AS device_id,
+    ('192.168.' || ((gs.id % 255) + 1) || '.' || ((gs.id % 255) + 1))::INET AS ip_address,
+    CASE
+        WHEN random() < 0.85 THEN 1
+        ELSE ((gs.id % 40) + 1)
+    END AS country_id,
+    -- Use real cities
+    CASE
+        WHEN random() < 0.85 THEN (SELECT city FROM temp_us_cities WHERE id = ((gs.id % 100) + 1))
+        ELSE (SELECT city FROM temp_world_cities WHERE id = ((gs.id % 210) + 1))
+    END AS city,
+    TIMESTAMP '2023-01-01' + (random() * 730)::INT * INTERVAL '1 day' + (random() * 86400)::INT * INTERVAL '1 second' AS login_timestamp,
+    TIMESTAMP '2023-01-01' + (random() * 730)::INT * INTERVAL '1 day' + (random() * 86400)::INT * INTERVAL '1 second' + (random() * 7200)::INT * INTERVAL '1 second' AS logout_timestamp,
+    (300 + random() * 7200)::INT AS session_duration_seconds,
+    random() < 0.98 AS is_successful,
+    (random() * 100)::DECIMAL(5,2) AS risk_score
+FROM generate_series(1, 500000) AS gs(id);
+EOF
+
+execute_sql_file /tmp/generate_sessions.sql > /dev/null
+echo -e "${GREEN}✓ Generated 500,000 login sessions${NC}"
+echo ""
+
+echo -e "${YELLOW}[8/9] Generating Transactions (this may take a while)...${NC}"
+echo -e "${BLUE}This step generates $NUM_TRANSACTIONS transactions with fraud patterns${NC}"
+
+# Generate transactions in batches to avoid memory issues
+BATCH_SIZE=500000
+NUM_BATCHES=$((NUM_TRANSACTIONS / BATCH_SIZE))
+
+for batch in $(seq 1 $NUM_BATCHES); do
+    START_ID=$(( (batch - 1) * BATCH_SIZE + 1 ))
+    END_ID=$(( batch * BATCH_SIZE ))
+
+    echo -e "${BLUE}  Batch $batch/$NUM_BATCHES (transactions $START_ID to $END_ID)...${NC}"
+
+    cat > /tmp/generate_transactions_batch.sql << EOF
+-- Generate transactions batch
+INSERT INTO transactions (
+    account_id, type_id, transaction_date, amount, currency,
+    merchant_id, card_id, device_id, ip_address, country_id, city,
+    description, reference_number, status, is_online, is_international,
+    is_card_present, fraud_score, is_flagged
+)
+SELECT
+    ((gs.id - 1) % 150000) + 1 AS account_id,
+    ((gs.id % 15) + 1) AS type_id,
+    TIMESTAMP '2023-01-01' + (random() * 730)::INT * INTERVAL '1 day' + (random() * 86400)::INT * INTERVAL '1 second' AS transaction_date,
+    CASE
+        WHEN random() < 0.60 THEN (5 + random() * 95)::DECIMAL(15,2)
+        WHEN random() < 0.85 THEN (100 + random() * 400)::DECIMAL(15,2)
+        WHEN random() < 0.95 THEN (500 + random() * 2000)::DECIMAL(15,2)
+        WHEN random() < 0.98 THEN (2500 + random() * 7500)::DECIMAL(15,2)
+        ELSE (10000 + random() * 90000)::DECIMAL(15,2)
+    END AS amount,
+    'USD' AS currency,
+    CASE
+        WHEN ((gs.id % 15) + 1) IN (1, 7, 13) THEN ((gs.id % 50000) + 1)
+        ELSE NULL
+    END AS merchant_id,
+    CASE
+        WHEN ((gs.id % 15) + 1) IN (1, 7, 13) THEN ((gs.id % 200000) + 1)
+        ELSE NULL
+    END AS card_id,
+    ((gs.id % 75000) + 1) AS device_id,
+    ('10.' || ((gs.id % 255) + 1) || '.' || ((gs.id % 255) + 1) || '.' || ((gs.id % 255) + 1))::INET AS ip_address,
+    CASE
+        WHEN random() < 0.90 THEN 1
+        ELSE ((gs.id % 40) + 1)
+    END AS country_id,
+    -- Use real cities based on country
+    CASE
+        WHEN random() < 0.90 THEN (SELECT city FROM temp_us_cities WHERE id = ((gs.id % 100) + 1))
+        ELSE (SELECT city FROM temp_world_cities WHERE id = ((gs.id % 210) + 1))
+    END AS city,
+    'Transaction #' || gs.id AS description,
+    'REF' || LPAD(gs.id::TEXT, 15, '0') AS reference_number,
+    CASE
+        WHEN random() < 0.95 THEN 'COMPLETED'
+        WHEN random() < 0.98 THEN 'PENDING'
+        ELSE 'FAILED'
+    END AS status,
+    random() < 0.70 AS is_online,
+    random() < 0.10 AS is_international,
+    random() < 0.30 AS is_card_present,
+    (random() * 100)::DECIMAL(5,2) AS fraud_score,
+    random() < 0.07 AS is_flagged
+FROM generate_series($START_ID, $END_ID) AS gs(id);
+EOF
+
+    execute_sql_file /tmp/generate_transactions_batch.sql > /dev/null
+    echo -e "${GREEN}  ✓ Batch $batch/$NUM_BATCHES completed${NC}"
+done
+
+echo -e "${GREEN}✓ Generated $NUM_TRANSACTIONS transactions${NC}"
+echo ""
+
+echo -e "${YELLOW}[9/9] Generating Fraud Cases and Alerts...${NC}"
+
+# Generate alerts for flagged transactions
+execute_sql "
+INSERT INTO alerts (transaction_id, customer_id, account_id, alert_type, severity, description, risk_score, status)
+SELECT
+    t.transaction_id,
+    a.customer_id,
+    t.account_id,
+    CASE
+        WHEN t.amount > 10000 THEN 'AMOUNT_ANOMALY'
+        WHEN t.is_international THEN 'GEOGRAPHIC_ANOMALY'
+        WHEN t.fraud_score > 80 THEN 'MERCHANT_RISK'
+        ELSE 'VELOCITY_CHECK'
+    END AS alert_type,
+    CASE
+        WHEN t.fraud_score > 80 THEN 'CRITICAL'
+        WHEN t.fraud_score > 60 THEN 'HIGH'
+        ELSE 'MEDIUM'
+    END AS severity,
+    'Suspicious transaction detected: ' || t.description AS description,
+    t.fraud_score,
+    CASE
+        WHEN random() < 0.30 THEN 'CLOSED'
+        WHEN random() < 0.50 THEN 'FALSE_POSITIVE'
+        WHEN random() < 0.70 THEN 'INVESTIGATING'
+        ELSE 'OPEN'
+    END AS status
+FROM transactions t
+JOIN accounts a ON t.account_id = a.account_id
+WHERE t.is_flagged = TRUE
+LIMIT 50000;
+" > /dev/null
+
+echo -e "${GREEN}✓ Generated alerts for flagged transactions${NC}"
+
+# Generate fraud cases
+execute_sql "
+INSERT INTO fraud_cases (
+    case_number, customer_id, account_id, fraud_type_id,
+    detection_date, detection_method, amount_lost, status, priority
+)
+SELECT
+    'CASE' || LPAD(ROW_NUMBER() OVER (ORDER BY a.alert_id)::TEXT, 10, '0') AS case_number,
+    a.customer_id,
+    a.account_id,
+    ((a.alert_id % 20) + 1) AS fraud_type_id,
+    a.alert_date AS detection_date,
+    CASE
+        WHEN random() < 0.70 THEN 'AUTOMATED'
+        WHEN random() < 0.85 THEN 'MANUAL_REVIEW'
+        ELSE 'CUSTOMER_REPORT'
+    END AS detection_method,
+    (random() * 50000)::DECIMAL(15,2) AS amount_lost,
+    CASE
+        WHEN random() < 0.40 THEN 'RESOLVED'
+        WHEN random() < 0.60 THEN 'INVESTIGATING'
+        ELSE 'OPEN'
+    END AS status,
+    CASE
+        WHEN a.severity = 'CRITICAL' THEN 'CRITICAL'
+        WHEN a.severity = 'HIGH' THEN 'HIGH'
+        ELSE 'MEDIUM'
+    END AS priority
+FROM alerts a
+WHERE a.status = 'CONFIRMED_FRAUD'
+   OR (a.severity IN ('CRITICAL', 'HIGH') AND random() < 0.20)
+LIMIT 5000;
+" > /dev/null
+
+echo -e "${GREEN}✓ Generated fraud cases${NC}"
+echo ""
+
+# Final statistics
+echo -e "${BLUE}============================================================================${NC}"
+echo -e "${GREEN}✓ Data generation completed successfully!${NC}"
+echo -e "${BLUE}============================================================================${NC}"
+echo ""
+echo -e "${YELLOW}Database Statistics:${NC}"
+
+customer_count=$(execute_sql "SELECT COUNT(*) FROM customers;" | grep -E '^\s*[0-9]+' | tr -d ' ')
+echo -e "Customers: ${GREEN}$customer_count${NC}"
+
+account_count=$(execute_sql "SELECT COUNT(*) FROM accounts;" | grep -E '^\s*[0-9]+' | tr -d ' ')
+echo -e "Accounts: ${GREEN}$account_count${NC}"
+
+merchant_count=$(execute_sql "SELECT COUNT(*) FROM merchants;" | grep -E '^\s*[0-9]+' | tr -d ' ')
+echo -e "Merchants: ${GREEN}$merchant_count${NC}"
+
+card_count=$(execute_sql "SELECT COUNT(*) FROM cards;" | grep -E '^\s*[0-9]+' | tr -d ' ')
+echo -e "Cards: ${GREEN}$card_count${NC}"
+
+transaction_count=$(execute_sql "SELECT COUNT(*) FROM transactions;" | grep -E '^\s*[0-9]+' | tr -d ' ')
+echo -e "Transactions: ${GREEN}$transaction_count${NC}"
+
+alert_count=$(execute_sql "SELECT COUNT(*) FROM alerts;" | grep -E '^\s*[0-9]+' | tr -d ' ')
+echo -e "Alerts: ${GREEN}$alert_count${NC}"
+
+case_count=$(execute_sql "SELECT COUNT(*) FROM fraud_cases;" | grep -E '^\s*[0-9]+' | tr -d ' ')
+echo -e "Fraud Cases: ${GREEN}$case_count${NC}"
+
+echo ""
+echo -e "${YELLOW}Ready for SQL learning and fraud investigation!${NC}"
+echo -e "Access DB-UI at: ${BLUE}http://localhost:3000${NC}"
+echo ""
+
@@ -0,0 +1,329 @@
+-- ============================================================================
+-- Load Geographic Reference Data into Temporary Tables
+-- ============================================================================
+-- This script creates temporary tables with realistic city/state/country data
+-- to be used during data generation
+-- ============================================================================
+
+-- Drop temporary tables if they exist
+DROP TABLE IF EXISTS temp_us_cities CASCADE;
+DROP TABLE IF EXISTS temp_world_cities CASCADE;
+
+-- Create temporary table for US cities
+CREATE TEMPORARY TABLE temp_us_cities (
+    id SERIAL PRIMARY KEY,
+    city VARCHAR(100) NOT NULL,
+    state VARCHAR(100) NOT NULL,
+    state_code CHAR(2) NOT NULL
+);
+
+-- Create temporary table for world cities
+CREATE TEMPORARY TABLE temp_world_cities (
+    id SERIAL PRIMARY KEY,
+    city VARCHAR(100) NOT NULL,
+    country VARCHAR(100) NOT NULL,
+    country_code CHAR(2) NOT NULL
+);
+
+-- Load US cities data
+COPY temp_us_cities(city, state, state_code) FROM STDIN WITH (FORMAT CSV, HEADER true);
+New York,New York,NY
+Los Angeles,California,CA
+Chicago,Illinois,IL
+Houston,Texas,TX
+Phoenix,Arizona,AZ
+Philadelphia,Pennsylvania,PA
+San Antonio,Texas,TX
+San Diego,California,CA
+Dallas,Texas,TX
+San Jose,California,CA
+Austin,Texas,TX
+Jacksonville,Florida,FL
+Fort Worth,Texas,TX
+Columbus,Ohio,OH
+Charlotte,North Carolina,NC
+San Francisco,California,CA
+Indianapolis,Indiana,IN
+Seattle,Washington,WA
+Denver,Colorado,CO
+Boston,Massachusetts,MA
+Nashville,Tennessee,TN
+Detroit,Michigan,MI
+Portland,Oregon,OR
+Las Vegas,Nevada,NV
+Memphis,Tennessee,TN
+Louisville,Kentucky,KY
+Baltimore,Maryland,MD
+Milwaukee,Wisconsin,WI
+Albuquerque,New Mexico,NM
+Tucson,Arizona,AZ
+Fresno,California,CA
+Sacramento,California,CA
+Kansas City,Missouri,MO
+Mesa,Arizona,AZ
+Atlanta,Georgia,GA
+Omaha,Nebraska,NE
+Colorado Springs,Colorado,CO
+Raleigh,North Carolina,NC
+Miami,Florida,FL
+Long Beach,California,CA
+Virginia Beach,Virginia,VA
+Oakland,California,CA
+Minneapolis,Minnesota,MN
+Tampa,Florida,FL
+Tulsa,Oklahoma,OK
+Arlington,Texas,TX
+New Orleans,Louisiana,LA
+Wichita,Kansas,KS
+Cleveland,Ohio,OH
+Bakersfield,California,CA
+Aurora,Colorado,CO
+Anaheim,California,CA
+Honolulu,Hawaii,HI
+Santa Ana,California,CA
+Riverside,California,CA
+Corpus Christi,Texas,TX
+Lexington,Kentucky,KY
+Stockton,California,CA
+Henderson,Nevada,NV
+Saint Paul,Minnesota,MN
+St. Louis,Missouri,MO
+Cincinnati,Ohio,OH
+Pittsburgh,Pennsylvania,PA
+Greensboro,North Carolina,NC
+Anchorage,Alaska,AK
+Plano,Texas,TX
+Lincoln,Nebraska,NE
+Orlando,Florida,FL
+Irvine,California,CA
+Newark,New Jersey,NJ
+Durham,North Carolina,NC
+Chula Vista,California,CA
+Toledo,Ohio,OH
+Fort Wayne,Indiana,IN
+St. Petersburg,Florida,FL
+Laredo,Texas,TX
+Jersey City,New Jersey,NJ
+Chandler,Arizona,AZ
+Madison,Wisconsin,WI
+Lubbock,Texas,TX
+Scottsdale,Arizona,AZ
+Reno,Nevada,NV
+Buffalo,New York,NY
+Gilbert,Arizona,AZ
+Glendale,Arizona,AZ
+North Las Vegas,Nevada,NV
+Winston-Salem,North Carolina,NC
+Chesapeake,Virginia,VA
+Norfolk,Virginia,VA
+Fremont,California,CA
+Garland,Texas,TX
+Irving,Texas,TX
+Hialeah,Florida,FL
+Richmond,Virginia,VA
+Boise,Idaho,ID
+Spokane,Washington,WA
+Baton Rouge,Louisiana,LA
+\.
+
+-- Load world cities data
+COPY temp_world_cities(city, country, country_code) FROM STDIN WITH (FORMAT CSV, HEADER true);
+Toronto,Canada,CA
+Vancouver,Canada,CA
+Montreal,Canada,CA
+Calgary,Canada,CA
+Ottawa,Canada,CA
+London,United Kingdom,GB
+Manchester,United Kingdom,GB
+Birmingham,United Kingdom,GB
+Edinburgh,United Kingdom,GB
+Glasgow,United Kingdom,GB
+Berlin,Germany,DE
+Munich,Germany,DE
+Hamburg,Germany,DE
+Frankfurt,Germany,DE
+Cologne,Germany,DE
+Paris,France,FR
+Lyon,France,FR
+Marseille,France,FR
+Toulouse,France,FR
+Nice,France,FR
+Rome,Italy,IT
+Milan,Italy,IT
+Naples,Italy,IT
+Turin,Italy,IT
+Florence,Italy,IT
+Madrid,Spain,ES
+Barcelona,Spain,ES
+Valencia,Spain,ES
+Seville,Spain,ES
+Bilbao,Spain,ES
+Sydney,Australia,AU
+Melbourne,Australia,AU
+Brisbane,Australia,AU
+Perth,Australia,AU
+Adelaide,Australia,AU
+Tokyo,Japan,JP
+Osaka,Japan,JP
+Kyoto,Japan,JP
+Yokohama,Japan,JP
+Nagoya,Japan,JP
+Beijing,China,CN
+Shanghai,China,CN
+Guangzhou,China,CN
+Shenzhen,China,CN
+Chengdu,China,CN
+Mumbai,India,IN
+Delhi,India,IN
+Bangalore,India,IN
+Hyderabad,India,IN
+Chennai,India,IN
+Sao Paulo,Brazil,BR
+Rio de Janeiro,Brazil,BR
+Brasilia,Brazil,BR
+Salvador,Brazil,BR
+Fortaleza,Brazil,BR
+Mexico City,Mexico,MX
+Guadalajara,Mexico,MX
+Monterrey,Mexico,MX
+Puebla,Mexico,MX
+Tijuana,Mexico,MX
+Moscow,Russia,RU
+Saint Petersburg,Russia,RU
+Novosibirsk,Russia,RU
+Yekaterinburg,Russia,RU
+Kazan,Russia,RU
+Lagos,Nigeria,NG
+Kano,Nigeria,NG
+Ibadan,Nigeria,NG
+Abuja,Nigeria,NG
+Port Harcourt,Nigeria,NG
+Karachi,Pakistan,PK
+Lahore,Pakistan,PK
+Islamabad,Pakistan,PK
+Rawalpindi,Pakistan,PK
+Faisalabad,Pakistan,PK
+Tehran,Iran,IR
+Mashhad,Iran,IR
+Isfahan,Iran,IR
+Karaj,Iran,IR
+Tabriz,Iran,IR
+Pyongyang,North Korea,KP
+Hamhung,North Korea,KP
+Chongjin,North Korea,KP
+Nampo,North Korea,KP
+Wonsan,North Korea,KP
+Damascus,Syria,SY
+Aleppo,Syria,SY
+Homs,Syria,SY
+Latakia,Syria,SY
+Hama,Syria,SY
+Caracas,Venezuela,VE
+Maracaibo,Venezuela,VE
+Valencia,Venezuela,VE
+Barquisimeto,Venezuela,VE
+Maracay,Venezuela,VE
+Havana,Cuba,CU
+Santiago de Cuba,Cuba,CU
+Camaguey,Cuba,CU
+Holguin,Cuba,CU
+Santa Clara,Cuba,CU
+Yangon,Myanmar,MM
+Mandalay,Myanmar,MM
+Naypyidaw,Myanmar,MM
+Mawlamyine,Myanmar,MM
+Bago,Myanmar,MM
+Kabul,Afghanistan,AF
+Kandahar,Afghanistan,AF
+Herat,Afghanistan,AF
+Mazar-i-Sharif,Afghanistan,AF
+Jalalabad,Afghanistan,AF
+Baghdad,Iraq,IQ
+Basra,Iraq,IQ
+Mosul,Iraq,IQ
+Erbil,Iraq,IQ
+Kirkuk,Iraq,IQ
+Tripoli,Libya,LY
+Benghazi,Libya,LY
+Misrata,Libya,LY
+Zawiya,Libya,LY
+Bayda,Libya,LY
+Khartoum,Sudan,SD
+Omdurman,Sudan,SD
+Port Sudan,Sudan,SD
+Kassala,Sudan,SD
+Nyala,Sudan,SD
+Mogadishu,Somalia,SO
+Hargeisa,Somalia,SO
+Bosaso,Somalia,SO
+Kismayo,Somalia,SO
+Merca,Somalia,SO
+Sanaa,Yemen,YE
+Aden,Yemen,YE
+Taiz,Yemen,YE
+Hodeidah,Yemen,YE
+Ibb,Yemen,YE
+Harare,Zimbabwe,ZW
+Bulawayo,Zimbabwe,ZW
+Chitungwiza,Zimbabwe,ZW
+Mutare,Zimbabwe,ZW
+Gweru,Zimbabwe,ZW
+Amsterdam,Netherlands,NL
+Rotterdam,Netherlands,NL
+The Hague,Netherlands,NL
+Utrecht,Netherlands,NL
+Eindhoven,Netherlands,NL
+Stockholm,Sweden,SE
+Gothenburg,Sweden,SE
+Malmo,Sweden,SE
+Uppsala,Sweden,SE
+Vasteras,Sweden,SE
+Oslo,Norway,NO
+Bergen,Norway,NO
+Trondheim,Norway,NO
+Stavanger,Norway,NO
+Drammen,Norway,NO
+Copenhagen,Denmark,DK
+Aarhus,Denmark,DK
+Odense,Denmark,DK
+Aalborg,Denmark,DK
+Esbjerg,Denmark,DK
+Helsinki,Finland,FI
+Espoo,Finland,FI
+Tampere,Finland,FI
+Vantaa,Finland,FI
+Oulu,Finland,FI
+Zurich,Switzerland,CH
+Geneva,Switzerland,CH
+Basel,Switzerland,CH
+Lausanne,Switzerland,CH
+Bern,Switzerland,CH
+Singapore,Singapore,SG
+Hong Kong,Hong Kong,HK
+Kowloon,Hong Kong,HK
+Seoul,South Korea,KR
+Busan,South Korea,KR
+Incheon,South Korea,KR
+Daegu,South Korea,KR
+Daejeon,South Korea,KR
+Taipei,Taiwan,TW
+Kaohsiung,Taiwan,TW
+Taichung,Taiwan,TW
+Tainan,Taiwan,TW
+Hsinchu,Taiwan,TW
+Auckland,New Zealand,NZ
+Wellington,New Zealand,NZ
+Christchurch,New Zealand,NZ
+Hamilton,New Zealand,NZ
+Tauranga,New Zealand,NZ
+\.
+
+-- Create indexes for faster lookups
+CREATE INDEX idx_temp_us_cities_id ON temp_us_cities(id);
+CREATE INDEX idx_temp_world_cities_id ON temp_world_cities(id);
+CREATE INDEX idx_temp_world_cities_country_code ON temp_world_cities(country_code);
+
+-- Show counts
+SELECT 'US Cities loaded: ' || COUNT(*) FROM temp_us_cities;
+SELECT 'World Cities loaded: ' || COUNT(*) FROM temp_world_cities;
+
@@ -0,0 +1,99 @@
+city,state,state_code
+New York,New York,NY
+Los Angeles,California,CA
+Chicago,Illinois,IL
+Houston,Texas,TX
+Phoenix,Arizona,AZ
+Philadelphia,Pennsylvania,PA
+San Antonio,Texas,TX
+San Diego,California,CA
+Dallas,Texas,TX
+San Jose,California,CA
+Austin,Texas,TX
+Jacksonville,Florida,FL
+Fort Worth,Texas,TX
+Columbus,Ohio,OH
+Charlotte,North Carolina,NC
+San Francisco,California,CA
+Indianapolis,Indiana,IN
+Seattle,Washington,WA
+Denver,Colorado,CO
+Boston,Massachusetts,MA
+Nashville,Tennessee,TN
+Detroit,Michigan,MI
+Portland,Oregon,OR
+Las Vegas,Nevada,NV
+Memphis,Tennessee,TN
+Louisville,Kentucky,KY
+Baltimore,Maryland,MD
+Milwaukee,Wisconsin,WI
+Albuquerque,New Mexico,NM
+Tucson,Arizona,AZ
+Fresno,California,CA
+Sacramento,California,CA
+Kansas City,Missouri,MO
+Mesa,Arizona,AZ
+Atlanta,Georgia,GA
+Omaha,Nebraska,NE
+Colorado Springs,Colorado,CO
+Raleigh,North Carolina,NC
+Miami,Florida,FL
+Long Beach,California,CA
+Virginia Beach,Virginia,VA
+Oakland,California,CA
+Minneapolis,Minnesota,MN
+Tampa,Florida,FL
+Tulsa,Oklahoma,OK
+Arlington,Texas,TX
+New Orleans,Louisiana,LA
+Wichita,Kansas,KS
+Cleveland,Ohio,OH
+Bakersfield,California,CA
+Aurora,Colorado,CO
+Anaheim,California,CA
+Honolulu,Hawaii,HI
+Santa Ana,California,CA
+Riverside,California,CA
+Corpus Christi,Texas,TX
+Lexington,Kentucky,KY
+Stockton,California,CA
+Henderson,Nevada,NV
+Saint Paul,Minnesota,MN
+St. Louis,Missouri,MO
+Cincinnati,Ohio,OH
+Pittsburgh,Pennsylvania,PA
+Greensboro,North Carolina,NC
+Anchorage,Alaska,AK
+Plano,Texas,TX
+Lincoln,Nebraska,NE
+Orlando,Florida,FL
+Irvine,California,CA
+Newark,New Jersey,NJ
+Durham,North Carolina,NC
+Chula Vista,California,CA
+Toledo,Ohio,OH
+Fort Wayne,Indiana,IN
+St. Petersburg,Florida,FL
+Laredo,Texas,TX
+Jersey City,New Jersey,NJ
+Chandler,Arizona,AZ
+Madison,Wisconsin,WI
+Lubbock,Texas,TX
+Scottsdale,Arizona,AZ
+Reno,Nevada,NV
+Buffalo,New York,NY
+Gilbert,Arizona,AZ
+Glendale,Arizona,AZ
+North Las Vegas,Nevada,NV
+Winston-Salem,North Carolina,NC
+Chesapeake,Virginia,VA
+Norfolk,Virginia,VA
+Fremont,California,CA
+Garland,Texas,TX
+Irving,Texas,TX
+Hialeah,Florida,FL
+Richmond,Virginia,VA
+Boise,Idaho,ID
+Spokane,Washington,WA
+Baton Rouge,Louisiana,LA
+
@@ -0,0 +1,190 @@
+city,country,country_code
+Toronto,Canada,CA
+Vancouver,Canada,CA
+Montreal,Canada,CA
+Calgary,Canada,CA
+Ottawa,Canada,CA
+London,United Kingdom,GB
+Manchester,United Kingdom,GB
+Birmingham,United Kingdom,GB
+Edinburgh,United Kingdom,GB
+Glasgow,United Kingdom,GB
+Berlin,Germany,DE
+Munich,Germany,DE
+Hamburg,Germany,DE
+Frankfurt,Germany,DE
+Cologne,Germany,DE
+Paris,France,FR
+Lyon,France,FR
+Marseille,France,FR
+Toulouse,France,FR
+Nice,France,FR
+Rome,Italy,IT
+Milan,Italy,IT
+Naples,Italy,IT
+Turin,Italy,IT
+Florence,Italy,IT
+Madrid,Spain,ES
+Barcelona,Spain,ES
+Valencia,Spain,ES
+Seville,Spain,ES
+Bilbao,Spain,ES
+Sydney,Australia,AU
+Melbourne,Australia,AU
+Brisbane,Australia,AU
+Perth,Australia,AU
+Adelaide,Australia,AU
+Tokyo,Japan,JP
+Osaka,Japan,JP
+Kyoto,Japan,JP
+Yokohama,Japan,JP
+Nagoya,Japan,JP
+Beijing,China,CN
+Shanghai,China,CN
+Guangzhou,China,CN
+Shenzhen,China,CN
+Chengdu,China,CN
+Mumbai,India,IN
+Delhi,India,IN
+Bangalore,India,IN
+Hyderabad,India,IN
+Chennai,India,IN
+Sao Paulo,Brazil,BR
+Rio de Janeiro,Brazil,BR
+Brasilia,Brazil,BR
+Salvador,Brazil,BR
+Fortaleza,Brazil,BR
+Mexico City,Mexico,MX
+Guadalajara,Mexico,MX
+Monterrey,Mexico,MX
+Puebla,Mexico,MX
+Tijuana,Mexico,MX
+Moscow,Russia,RU
+Saint Petersburg,Russia,RU
+Novosibirsk,Russia,RU
+Yekaterinburg,Russia,RU
+Kazan,Russia,RU
+Lagos,Nigeria,NG
+Kano,Nigeria,NG
+Ibadan,Nigeria,NG
+Abuja,Nigeria,NG
+Port Harcourt,Nigeria,NG
+Karachi,Pakistan,PK
+Lahore,Pakistan,PK
+Islamabad,Pakistan,PK
+Rawalpindi,Pakistan,PK
+Faisalabad,Pakistan,PK
+Tehran,Iran,IR
+Mashhad,Iran,IR
+Isfahan,Iran,IR
+Karaj,Iran,IR
+Tabriz,Iran,IR
+Pyongyang,North Korea,KP
+Hamhung,North Korea,KP
+Chongjin,North Korea,KP
+Nampo,North Korea,KP
+Wonsan,North Korea,KP
+Damascus,Syria,SY
+Aleppo,Syria,SY
+Homs,Syria,SY
+Latakia,Syria,SY
+Hama,Syria,SY
+Caracas,Venezuela,VE
+Maracaibo,Venezuela,VE
+Valencia,Venezuela,VE
+Barquisimeto,Venezuela,VE
+Maracay,Venezuela,VE
+Havana,Cuba,CU
+Santiago de Cuba,Cuba,CU
+Camaguey,Cuba,CU
+Holguin,Cuba,CU
+Santa Clara,Cuba,CU
+Yangon,Myanmar,MM
+Mandalay,Myanmar,MM
+Naypyidaw,Myanmar,MM
+Mawlamyine,Myanmar,MM
+Bago,Myanmar,MM
+Kabul,Afghanistan,AF
+Kandahar,Afghanistan,AF
+Herat,Afghanistan,AF
+Mazar-i-Sharif,Afghanistan,AF
+Jalalabad,Afghanistan,AF
+Baghdad,Iraq,IQ
+Basra,Iraq,IQ
+Mosul,Iraq,IQ
+Erbil,Iraq,IQ
+Kirkuk,Iraq,IQ
+Tripoli,Libya,LY
+Benghazi,Libya,LY
+Misrata,Libya,LY
+Zawiya,Libya,LY
+Bayda,Libya,LY
+Khartoum,Sudan,SD
+Omdurman,Sudan,SD
+Port Sudan,Sudan,SD
+Kassala,Sudan,SD
+Nyala,Sudan,SD
+Mogadishu,Somalia,SO
+Hargeisa,Somalia,SO
+Bosaso,Somalia,SO
+Kismayo,Somalia,SO
+Merca,Somalia,SO
+Sanaa,Yemen,YE
+Aden,Yemen,YE
+Taiz,Yemen,YE
+Hodeidah,Yemen,YE
+Ibb,Yemen,YE
+Harare,Zimbabwe,ZW
+Bulawayo,Zimbabwe,ZW
+Chitungwiza,Zimbabwe,ZW
+Mutare,Zimbabwe,ZW
+Gweru,Zimbabwe,ZW
+Amsterdam,Netherlands,NL
+Rotterdam,Netherlands,NL
+The Hague,Netherlands,NL
+Utrecht,Netherlands,NL
+Eindhoven,Netherlands,NL
+Stockholm,Sweden,SE
+Gothenburg,Sweden,SE
+Malmo,Sweden,SE
+Uppsala,Sweden,SE
+Vasteras,Sweden,SE
+Oslo,Norway,NO
+Bergen,Norway,NO
+Trondheim,Norway,NO
+Stavanger,Norway,NO
+Drammen,Norway,NO
+Copenhagen,Denmark,DK
+Aarhus,Denmark,DK
+Odense,Denmark,DK
+Aalborg,Denmark,DK
+Esbjerg,Denmark,DK
+Helsinki,Finland,FI
+Espoo,Finland,FI
+Tampere,Finland,FI
+Vantaa,Finland,FI
+Oulu,Finland,FI
+Zurich,Switzerland,CH
+Geneva,Switzerland,CH
+Basel,Switzerland,CH
+Lausanne,Switzerland,CH
+Bern,Switzerland,CH
+Singapore,Singapore,SG
+Hong Kong,Hong Kong,HK
+Kowloon,Hong Kong,HK
+Seoul,South Korea,KR
+Busan,South Korea,KR
+Incheon,South Korea,KR
+Daegu,South Korea,KR
+Daejeon,South Korea,KR
+Taipei,Taiwan,TW
+Kaohsiung,Taiwan,TW
+Taichung,Taiwan,TW
+Tainan,Taiwan,TW
+Hsinchu,Taiwan,TW
+Auckland,New Zealand,NZ
+Wellington,New Zealand,NZ
+Christchurch,New Zealand,NZ
+Hamilton,New Zealand,NZ
+Tauranga,New Zealand,NZ
+
@@ -0,0 +1,52 @@
+version: '3.8'
+
+services:
+  postgres:
+    image: postgres:16-alpine
+    container_name: fraud_detection_db
+    environment:
+      POSTGRES_DB: fraud_detection
+      POSTGRES_USER: fraud_analyst
+      POSTGRES_PASSWORD: SecurePass123!
+      PGDATA: /var/lib/postgresql/data/pgdata
+    ports:
+      - "5432:5432"
+    volumes:
+      - postgres_data:/var/lib/postgresql/data
+      - ./schema:/docker-entrypoint-initdb.d
+      - ./data:/data
+    networks:
+      - fraud_network
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U fraud_analyst -d fraud_detection"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+    restart: unless-stopped
+
+  db-ui:
+    image: ghcr.io/n7olkachev/db-ui:latest
+    container_name: fraud_detection_ui
+    environment:
+      POSTGRES_HOST: postgres
+      POSTGRES_USER: fraud_analyst
+      POSTGRES_PASSWORD: SecurePass123!
+      POSTGRES_DB: fraud_detection
+      POSTGRES_PORT: 5432
+    ports:
+      - "3000:3000"
+    networks:
+      - fraud_network
+    depends_on:
+      postgres:
+        condition: service_healthy
+    restart: unless-stopped
+
+networks:
+  fraud_network:
+    driver: bridge
+
+volumes:
+  postgres_data:
+    driver: local
+
@@ -0,0 +1,22 @@
+-- ============================================================================
+-- Database Initialization Script
+-- ============================================================================
+-- This script ensures the database is created and ready
+-- It runs automatically when the PostgreSQL container starts
+-- ============================================================================
+
+-- Ensure the database exists (this runs in the default postgres database)
+SELECT 'Database initialization starting...' AS status;
+
+-- Set timezone
+SET timezone = 'UTC';
+
+-- Show current database
+SELECT current_database() AS current_db, current_user AS current_user, version() AS pg_version;
+
+-- Enable required extensions
+CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
+CREATE EXTENSION IF NOT EXISTS "pgcrypto";
+
+SELECT 'Extensions enabled successfully' AS status;
+
@@ -0,0 +1,313 @@
+# Quick Start Guide
+
+## 🚀 Get Up and Running in 5 Minutes
+
+### Step 1: Start Docker Containers (1 minute)
+
+```bash
+# From the project root directory
+docker-compose up -d
+```
+
+**What this does:**
+- Starts PostgreSQL 16 database
+- Starts DB-UI web interface
+- Creates network and volumes
+
+**Verify it's running:**
+```bash
+docker-compose ps
+```
+
+You should see both `fraud_detection_db` and `fraud_detection_ui` running.
+
+---
+
+### Step 2: Initialize Database Schema (1 minute)
+
+```bash
+# Make script executable (first time only)
+chmod +x scripts/setup-database.sh
+
+# Run setup
+./scripts/setup-database.sh
+```
+
+**What this does:**
+- Creates all 20+ tables
+- Sets up indexes and constraints
+- Loads reference data (countries, merchant categories, etc.)
+
+**Expected output:**
+```
+✓ PostgreSQL is ready
+✓ Creating tables, indexes, and constraints
+✓ Loading reference data
+✓ Database setup completed successfully!
+```
+
+---
+
+### Step 3: Generate Test Data (15-30 minutes)
+
+```bash
+# Make script executable (first time only)
+chmod +x data/generate_data.sh
+
+# Run data generation
+./data/generate_data.sh
+```
+
+**What this does:**
+- Generates 100,000 customers
+- Creates 150,000 accounts
+- Generates 5,000,000 transactions
+- Creates fraud patterns and alerts
+
+**⏱️ Time estimate:**
+- Fast machine (SSD, 16GB RAM): ~15 minutes
+- Average machine: ~20-25 minutes
+- Slower machine: ~30 minutes
+
+**You can monitor progress:**
+The script shows progress for each step:
+```
+[1/9] Loading geographic reference data...
+[2/9] Generating Customers...
+[3/9] Generating Accounts...
+...
+```
+
+---
+
+### Step 4: Access the Database
+
+#### Option A: DB-UI Web Interface (Recommended for Beginners)
+
+1. Open your browser to: **http://localhost:3000**
+2. You'll see the database tables in the sidebar
+3. Click any table to browse data
+4. Use the "Custom SQL" tab to run queries
+
+**Features:**
+- Visual table browser
+- SQL query editor with syntax highlighting
+- Export results to CSV
+- Schema introspection
+
+#### Option B: Command Line (psql)
+
+```bash
+docker exec -it fraud_detection_db psql -U fraud_analyst -d fraud_detection
+```
+
+**Quick commands:**
+```sql
+-- List all tables
+\dt
+
+-- Describe a table
+\d customers
+
+-- Run a query
+SELECT COUNT(*) FROM transactions;
+
+-- Exit
+\q
+```
+
+#### Option C: Your Favorite SQL Client
+
+**Connection Details:**
+```
+Host:     localhost
+Port:     5432
+Database: fraud_detection
+Username: fraud_analyst
+Password: SecurePass123!
+```
+
+**Popular clients:**
+- DBeaver (free, cross-platform)
+- pgAdmin (free, PostgreSQL-specific)
+- DataGrip (paid, JetBrains)
+- TablePlus (paid, macOS/Windows)
+
+---
+
+## 🎓 Your First Queries
+
+### 1. Check Data Counts
+
+```sql
+-- How many customers?
+SELECT COUNT(*) FROM customers;
+
+-- How many transactions?
+SELECT COUNT(*) FROM transactions;
+
+-- How many fraud alerts?
+SELECT COUNT(*) FROM alerts WHERE status = 'OPEN';
+```
+
+### 2. Find High-Risk Customers
+
+```sql
+SELECT 
+    customer_id,
+    first_name,
+    last_name,
+    email,
+    risk_score
+FROM customers
+WHERE risk_score > 80
+ORDER BY risk_score DESC
+LIMIT 10;
+```
+
+### 3. View Recent Transactions
+
+```sql
+SELECT 
+    transaction_id,
+    account_id,
+    amount,
+    transaction_date,
+    is_flagged
+FROM transactions
+ORDER BY transaction_date DESC
+LIMIT 20;
+```
+
+### 4. Find Flagged Transactions
+
+```sql
+SELECT 
+    t.transaction_id,
+    t.amount,
+    t.fraud_score,
+    t.flagged_reason,
+    c.first_name,
+    c.last_name
+FROM transactions t
+JOIN accounts a ON t.account_id = a.account_id
+JOIN customers c ON a.customer_id = c.customer_id
+WHERE t.is_flagged = TRUE
+ORDER BY t.fraud_score DESC
+LIMIT 10;
+```
+
+---
+
+## 📚 Next Steps
+
+### Start Learning SQL
+
+1. **Begin with basics:** `exercises/01-basic-queries/README.md`
+2. **Progress through levels:** Work through exercises 01-06
+3. **Practice fraud detection:** `exercises/06-fraud-detection/README.md`
+
+### Explore the Data
+
+```sql
+-- What countries are represented?
+SELECT country_name, COUNT(*) as customer_count
+FROM customers c
+JOIN countries co ON c.country_id = co.country_id
+GROUP BY country_name
+ORDER BY customer_count DESC;
+
+-- What are the top merchant categories?
+SELECT mc.category_name, COUNT(*) as transaction_count
+FROM transactions t
+JOIN merchants m ON t.merchant_id = m.merchant_id
+JOIN merchant_categories mc ON m.category_id = mc.category_id
+GROUP BY mc.category_name
+ORDER BY transaction_count DESC;
+
+-- How many fraud cases by type?
+SELECT ft.fraud_name, COUNT(*) as case_count
+FROM fraud_cases fc
+JOIN fraud_types ft ON fc.fraud_type_id = ft.fraud_type_id
+GROUP BY ft.fraud_name
+ORDER BY case_count DESC;
+```
+
+---
+
+## 🔧 Troubleshooting
+
+### Database won't start
+
+```bash
+# Check logs
+docker-compose logs postgres
+
+# Restart containers
+docker-compose restart
+```
+
+### Can't connect to database
+
+```bash
+# Check if PostgreSQL is ready
+docker exec fraud_detection_db pg_isready -U fraud_analyst
+
+# Check port is not in use
+netstat -an | grep 5432
+```
+
+### Data generation fails
+
+```bash
+# Check disk space
+df -h
+
+# Check memory
+free -h
+
+# Try with smaller dataset
+# Edit data/generate_data.sh and reduce:
+NUM_CUSTOMERS=10000
+NUM_TRANSACTIONS=500000
+```
+
+### Reset everything
+
+```bash
+# Stop and remove everything
+docker-compose down -v
+
+# Start fresh
+docker-compose up -d
+./scripts/setup-database.sh
+./data/generate_data.sh
+```
+
+---
+
+## 💡 Tips
+
+1. **Use DB-UI for exploration** - Great for browsing and understanding the schema
+2. **Use psql for practice** - Best for learning SQL commands
+3. **Start simple** - Begin with basic SELECT queries before complex joins
+4. **Check the exercises** - They're designed to build your skills progressively
+5. **Experiment** - The database is yours to explore and learn from!
+
+---
+
+## 🎯 Learning Goals
+
+After completing this tutorial, you'll be able to:
+
+- ✅ Write complex SQL queries
+- ✅ Understand database relationships
+- ✅ Detect fraud patterns in data
+- ✅ Use window functions and CTEs
+- ✅ Optimize queries with indexes
+- ✅ Investigate financial crimes
+
+---
+
+**Ready to start? Head to `exercises/01-basic-queries/README.md`!** 🚀
+
@@ -0,0 +1,206 @@
+# 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
+
@@ -0,0 +1,360 @@
+# Level 6: Fraud Detection Scenarios
+
+## Introduction
+Now you'll apply your SQL skills to real-world fraud detection scenarios. These exercises simulate actual fraud investigation tasks.
+
+## Learning Objectives
+- Detect velocity fraud patterns
+- Identify geographic anomalies
+- Find money mule networks
+- Detect account takeover attempts
+- Identify structuring patterns
+
+---
+
+## Fraud Pattern Detection
+
+### Scenario 1: Velocity Fraud Detection
+**Objective:** Find accounts with more than 5 transactions in a 1-hour window
+
+```sql
+-- Detect rapid-fire transactions (velocity check)
+WITH transaction_windows AS (
+    SELECT 
+        t1.account_id,
+        t1.transaction_id,
+        t1.transaction_date,
+        t1.amount,
+        COUNT(t2.transaction_id) as transactions_in_hour
+    FROM transactions t1
+    JOIN transactions t2 ON t1.account_id = t2.account_id
+        AND t2.transaction_date BETWEEN t1.transaction_date - INTERVAL '1 hour' 
+        AND t1.transaction_date
+    GROUP BY t1.account_id, t1.transaction_id, t1.transaction_date, t1.amount
+)
+SELECT 
+    account_id,
+    transaction_date,
+    transactions_in_hour,
+    SUM(amount) as total_amount
+FROM transaction_windows
+WHERE transactions_in_hour > 5
+GROUP BY account_id, transaction_date, transactions_in_hour
+ORDER BY transactions_in_hour DESC;
+```
+
+---
+
+### Scenario 2: Geographic Impossibility
+**Objective:** Find transactions from the same card in different countries within 2 hours
+
+```sql
+-- Detect impossible travel (card used in different countries too quickly)
+SELECT 
+    t1.card_id,
+    t1.transaction_id as trans1_id,
+    t1.transaction_date as trans1_date,
+    c1.country_name as country1,
+    t1.city as city1,
+    t2.transaction_id as trans2_id,
+    t2.transaction_date as trans2_date,
+    c2.country_name as country2,
+    t2.city as city2,
+    EXTRACT(EPOCH FROM (t2.transaction_date - t1.transaction_date))/3600 as hours_between
+FROM transactions t1
+JOIN transactions t2 ON t1.card_id = t2.card_id
+    AND t2.transaction_date > t1.transaction_date
+    AND t2.transaction_date <= t1.transaction_date + INTERVAL '2 hours'
+JOIN countries c1 ON t1.country_id = c1.country_id
+JOIN countries c2 ON t2.country_id = c2.country_id
+WHERE t1.country_id != t2.country_id
+ORDER BY hours_between ASC;
+```
+
+---
+
+### Scenario 3: Money Mule Network Detection
+**Objective:** Find clusters of accounts that transfer money in a chain pattern
+
+```sql
+-- Detect potential money mule networks (rapid transfer chains)
+WITH transfer_chains AS (
+    SELECT 
+        tr1.from_account_id as account1,
+        tr1.to_account_id as account2,
+        tr2.to_account_id as account3,
+        tr1.transfer_id as transfer1,
+        tr2.transfer_id as transfer2,
+        t1.amount as amount1,
+        t2.amount as amount2,
+        t1.transaction_date as date1,
+        t2.transaction_date as date2,
+        EXTRACT(EPOCH FROM (t2.transaction_date - t1.transaction_date))/3600 as hours_between
+    FROM transfers tr1
+    JOIN transfers tr2 ON tr1.to_account_id = tr2.from_account_id
+    JOIN transactions t1 ON tr1.transaction_id = t1.transaction_id
+    JOIN transactions t2 ON tr2.transaction_id = t2.transaction_id
+    WHERE t2.transaction_date BETWEEN t1.transaction_date AND t1.transaction_date + INTERVAL '24 hours'
+)
+SELECT 
+    account1,
+    account2,
+    account3,
+    amount1,
+    amount2,
+    hours_between,
+    CASE 
+        WHEN ABS(amount1 - amount2) / amount1 < 0.1 THEN 'SUSPICIOUS - Similar amounts'
+        ELSE 'Review'
+    END as risk_flag
+FROM transfer_chains
+WHERE hours_between < 24
+ORDER BY hours_between ASC;
+```
+
+---
+
+### Scenario 4: Account Takeover Detection
+**Objective:** Find accounts with sudden changes in transaction patterns
+
+```sql
+-- Detect account takeover by analyzing behavior changes
+WITH customer_baseline AS (
+    SELECT 
+        a.customer_id,
+        a.account_id,
+        AVG(t.amount) as avg_transaction,
+        STDDEV(t.amount) as stddev_transaction,
+        COUNT(*) as transaction_count
+    FROM accounts a
+    JOIN transactions t ON a.account_id = t.account_id
+    WHERE t.transaction_date < CURRENT_DATE - INTERVAL '30 days'
+    GROUP BY a.customer_id, a.account_id
+),
+recent_transactions AS (
+    SELECT 
+        a.customer_id,
+        a.account_id,
+        t.transaction_id,
+        t.amount,
+        t.transaction_date,
+        t.country_id,
+        t.device_id
+    FROM accounts a
+    JOIN transactions t ON a.account_id = t.account_id
+    WHERE t.transaction_date >= CURRENT_DATE - INTERVAL '7 days'
+)
+SELECT 
+    rt.customer_id,
+    rt.account_id,
+    rt.transaction_id,
+    rt.amount,
+    cb.avg_transaction,
+    (rt.amount - cb.avg_transaction) / NULLIF(cb.stddev_transaction, 0) as z_score,
+    CASE 
+        WHEN ABS((rt.amount - cb.avg_transaction) / NULLIF(cb.stddev_transaction, 0)) > 3 
+        THEN 'HIGH RISK - Amount anomaly'
+        WHEN ABS((rt.amount - cb.avg_transaction) / NULLIF(cb.stddev_transaction, 0)) > 2 
+        THEN 'MEDIUM RISK'
+        ELSE 'Normal'
+    END as risk_level
+FROM recent_transactions rt
+JOIN customer_baseline cb ON rt.account_id = cb.account_id
+WHERE cb.transaction_count > 10
+ORDER BY ABS((rt.amount - cb.avg_transaction) / NULLIF(cb.stddev_transaction, 0)) DESC;
+```
+
+---
+
+### Scenario 5: Structuring Detection (Smurfing)
+**Objective:** Find patterns of transactions just under $10,000 (reporting threshold)
+
+```sql
+-- Detect structuring - multiple transactions just under reporting threshold
+WITH daily_transactions AS (
+    SELECT 
+        account_id,
+        DATE(transaction_date) as transaction_day,
+        COUNT(*) as num_transactions,
+        SUM(amount) as total_amount,
+        AVG(amount) as avg_amount,
+        MAX(amount) as max_amount
+    FROM transactions
+    WHERE amount BETWEEN 9000 AND 9999
+        AND transaction_date >= CURRENT_DATE - INTERVAL '30 days'
+    GROUP BY account_id, DATE(transaction_date)
+)
+SELECT 
+    dt.account_id,
+    c.first_name,
+    c.last_name,
+    dt.transaction_day,
+    dt.num_transactions,
+    dt.total_amount,
+    dt.avg_amount,
+    CASE 
+        WHEN dt.num_transactions >= 3 AND dt.total_amount > 25000 
+        THEN 'CRITICAL - Likely structuring'
+        WHEN dt.num_transactions >= 2 AND dt.total_amount > 18000 
+        THEN 'HIGH - Possible structuring'
+        ELSE 'Review'
+    END as risk_assessment
+FROM daily_transactions dt
+JOIN accounts a ON dt.account_id = a.account_id
+JOIN customers c ON a.customer_id = c.customer_id
+WHERE dt.num_transactions >= 2
+ORDER BY dt.total_amount DESC, dt.num_transactions DESC;
+```
+
+---
+
+### Scenario 6: High-Risk Merchant Analysis
+**Objective:** Find customers with unusual activity at high-risk merchants
+
+```sql
+-- Analyze transactions at high-risk merchants
+SELECT 
+    c.customer_id,
+    c.first_name,
+    c.last_name,
+    c.risk_score as customer_risk,
+    mc.category_name,
+    m.merchant_name,
+    m.risk_rating as merchant_risk,
+    COUNT(t.transaction_id) as transaction_count,
+    SUM(t.amount) as total_spent,
+    AVG(t.amount) as avg_transaction,
+    MAX(t.amount) as max_transaction
+FROM customers c
+JOIN accounts a ON c.customer_id = a.customer_id
+JOIN transactions t ON a.account_id = t.account_id
+JOIN merchants m ON t.merchant_id = m.merchant_id
+JOIN merchant_categories mc ON m.category_id = mc.category_id
+WHERE m.risk_rating IN ('HIGH', 'CRITICAL')
+    AND t.transaction_date >= CURRENT_DATE - INTERVAL '90 days'
+GROUP BY c.customer_id, c.first_name, c.last_name, c.risk_score, 
+         mc.category_name, m.merchant_name, m.risk_rating
+HAVING COUNT(t.transaction_id) > 5 OR SUM(t.amount) > 10000
+ORDER BY total_spent DESC;
+```
+
+---
+
+### Scenario 7: Card Testing Detection
+**Objective:** Find cards with multiple small failed transactions (testing stolen cards)
+
+```sql
+-- Detect card testing patterns
+SELECT 
+    t.card_id,
+    c.card_last_four,
+    COUNT(*) as failed_attempts,
+    COUNT(DISTINCT t.merchant_id) as different_merchants,
+    MIN(t.amount) as min_amount,
+    MAX(t.amount) as max_amount,
+    MIN(t.transaction_date) as first_attempt,
+    MAX(t.transaction_date) as last_attempt,
+    EXTRACT(EPOCH FROM (MAX(t.transaction_date) - MIN(t.transaction_date)))/60 as minutes_span
+FROM transactions t
+JOIN cards c ON t.card_id = c.card_id
+WHERE t.status = 'FAILED'
+    AND t.amount < 10
+    AND t.transaction_date >= CURRENT_DATE - INTERVAL '24 hours'
+GROUP BY t.card_id, c.card_last_four
+HAVING COUNT(*) >= 3
+ORDER BY failed_attempts DESC, minutes_span ASC;
+```
+
+---
+
+### Scenario 8: Dormant Account Reactivation
+**Objective:** Find dormant accounts that suddenly become active (potential takeover)
+
+```sql
+-- Detect dormant account reactivation
+WITH account_activity AS (
+    SELECT 
+        account_id,
+        MIN(transaction_date) as first_transaction,
+        MAX(transaction_date) as last_transaction,
+        COUNT(*) as total_transactions
+    FROM transactions
+    GROUP BY account_id
+),
+dormant_accounts AS (
+    SELECT 
+        account_id,
+        last_transaction,
+        total_transactions
+    FROM account_activity
+    WHERE last_transaction < CURRENT_DATE - INTERVAL '180 days'
+),
+recent_activity AS (
+    SELECT 
+        t.account_id,
+        COUNT(*) as recent_transactions,
+        SUM(t.amount) as recent_amount,
+        MIN(t.transaction_date) as reactivation_date
+    FROM transactions t
+    WHERE t.transaction_date >= CURRENT_DATE - INTERVAL '7 days'
+    GROUP BY t.account_id
+)
+SELECT 
+    da.account_id,
+    c.first_name,
+    c.last_name,
+    c.email,
+    da.last_transaction as last_active,
+    EXTRACT(DAY FROM (CURRENT_DATE - da.last_transaction)) as days_dormant,
+    ra.reactivation_date,
+    ra.recent_transactions,
+    ra.recent_amount,
+    'CRITICAL - Dormant account reactivated' as alert_type
+FROM dormant_accounts da
+JOIN recent_activity ra ON da.account_id = ra.account_id
+JOIN accounts a ON da.account_id = a.account_id
+JOIN customers c ON a.customer_id = c.customer_id
+ORDER BY days_dormant DESC;
+```
+
+---
+
+## Investigation Exercises
+
+### Exercise 6.1: Full Customer Investigation
+Create a comprehensive report for a suspicious customer including:
+- All accounts
+- All transactions
+- All alerts
+- All fraud cases
+- Related customers (via relationships)
+
+### Exercise 6.2: Fraud Case Summary
+Generate a summary report of all open fraud cases with:
+- Case details
+- Associated transactions
+- Total amount at risk
+- Investigation status
+
+### Exercise 6.3: Daily Fraud Dashboard
+Create a daily dashboard showing:
+- New alerts by severity
+- High-risk transactions
+- Geographic anomalies
+- Velocity violations
+
+---
+
+## Next Steps
+Congratulations! You've completed the fraud detection scenarios. You now have the skills to:
+- Detect complex fraud patterns
+- Investigate suspicious activity
+- Generate fraud reports
+- Analyze customer behavior
+
+Continue practicing with real data and explore advanced topics like:
+- Machine learning integration
+- Real-time fraud scoring
+- Network analysis
+- Predictive modeling
+
@@ -0,0 +1,477 @@
+-- ============================================================================
+-- Financial Fraud Detection Database Schema
+-- Purpose: Educational SQL learning with realistic fraud investigation scenarios
+-- ============================================================================
+-- This script is IDEMPOTENT - it will drop and recreate all objects
+-- ============================================================================
+
+-- Enable required extensions
+CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
+CREATE EXTENSION IF NOT EXISTS "pgcrypto";
+
+-- ============================================================================
+-- DROP ALL EXISTING OBJECTS (in reverse dependency order)
+-- ============================================================================
+
+-- Drop triggers first
+DROP TRIGGER IF EXISTS trg_update_balance ON transactions;
+DROP TRIGGER IF EXISTS trg_check_suspicious ON transactions;
+
+-- Drop functions
+DROP FUNCTION IF EXISTS update_account_balance() CASCADE;
+DROP FUNCTION IF EXISTS check_suspicious_transaction() CASCADE;
+DROP FUNCTION IF EXISTS generate_fraud_score(DECIMAL, BOOLEAN, DECIMAL, INT, BOOLEAN) CASCADE;
+
+-- Drop tables in reverse dependency order
+DROP TABLE IF EXISTS audit_log CASCADE;
+DROP TABLE IF EXISTS suspicious_activity_reports CASCADE;
+DROP TABLE IF EXISTS case_alerts CASCADE;
+DROP TABLE IF EXISTS case_transactions CASCADE;
+DROP TABLE IF EXISTS fraud_cases CASCADE;
+DROP TABLE IF EXISTS alerts CASCADE;
+DROP TABLE IF EXISTS transfers CASCADE;
+DROP TABLE IF EXISTS beneficiaries CASCADE;
+DROP TABLE IF EXISTS transactions CASCADE;
+DROP TABLE IF EXISTS login_sessions CASCADE;
+DROP TABLE IF EXISTS devices CASCADE;
+DROP TABLE IF EXISTS cards CASCADE;
+DROP TABLE IF EXISTS accounts CASCADE;
+DROP TABLE IF EXISTS customer_relationships CASCADE;
+DROP TABLE IF EXISTS customers CASCADE;
+DROP TABLE IF EXISTS merchants CASCADE;
+DROP TABLE IF EXISTS merchant_categories CASCADE;
+DROP TABLE IF EXISTS fraud_types CASCADE;
+DROP TABLE IF EXISTS transaction_types CASCADE;
+DROP TABLE IF EXISTS countries CASCADE;
+
+-- ============================================================================
+-- REFERENCE/LOOKUP TABLES
+-- ============================================================================
+
+-- Countries reference table
+CREATE TABLE countries (
+    country_id SERIAL PRIMARY KEY,
+    country_code CHAR(2) NOT NULL UNIQUE,
+    country_name VARCHAR(100) NOT NULL,
+    region VARCHAR(50) NOT NULL,
+    risk_level VARCHAR(20) DEFAULT 'LOW' CHECK (risk_level IN ('LOW', 'MEDIUM', 'HIGH', 'CRITICAL')),
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Merchant categories
+CREATE TABLE merchant_categories (
+    category_id SERIAL PRIMARY KEY,
+    category_code VARCHAR(10) NOT NULL UNIQUE,
+    category_name VARCHAR(100) NOT NULL,
+    description TEXT,
+    risk_weight DECIMAL(3,2) DEFAULT 1.00,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Transaction types
+CREATE TABLE transaction_types (
+    type_id SERIAL PRIMARY KEY,
+    type_code VARCHAR(20) NOT NULL UNIQUE,
+    type_name VARCHAR(100) NOT NULL,
+    description TEXT,
+    requires_merchant BOOLEAN DEFAULT FALSE,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Fraud types
+CREATE TABLE fraud_types (
+    fraud_type_id SERIAL PRIMARY KEY,
+    fraud_code VARCHAR(20) NOT NULL UNIQUE,
+    fraud_name VARCHAR(100) NOT NULL,
+    description TEXT,
+    severity VARCHAR(20) DEFAULT 'MEDIUM' CHECK (severity IN ('LOW', 'MEDIUM', 'HIGH', 'CRITICAL')),
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- ============================================================================
+-- CUSTOMER DOMAIN
+-- ============================================================================
+
+-- Customers table
+CREATE TABLE customers (
+    customer_id BIGSERIAL PRIMARY KEY,
+    first_name VARCHAR(100) NOT NULL,
+    last_name VARCHAR(100) NOT NULL,
+    email VARCHAR(255) NOT NULL UNIQUE,
+    phone VARCHAR(20),
+    date_of_birth DATE NOT NULL,
+    ssn_hash VARCHAR(64) NOT NULL UNIQUE, -- Hashed SSN for privacy
+    address_line1 VARCHAR(255),
+    address_line2 VARCHAR(255),
+    city VARCHAR(100),
+    state VARCHAR(50),
+    postal_code VARCHAR(20),
+    country_id INT NOT NULL REFERENCES countries(country_id),
+    registration_date TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    last_login TIMESTAMP,
+    kyc_status VARCHAR(20) DEFAULT 'PENDING' CHECK (kyc_status IN ('PENDING', 'VERIFIED', 'REJECTED', 'EXPIRED')),
+    kyc_verified_date TIMESTAMP,
+    risk_score DECIMAL(5,2) DEFAULT 50.00 CHECK (risk_score BETWEEN 0 AND 100),
+    is_pep BOOLEAN DEFAULT FALSE, -- Politically Exposed Person
+    is_active BOOLEAN DEFAULT TRUE,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Customer relationships (for detecting collusion networks)
+CREATE TABLE customer_relationships (
+    relationship_id BIGSERIAL PRIMARY KEY,
+    customer_id_1 BIGINT NOT NULL REFERENCES customers(customer_id),
+    customer_id_2 BIGINT NOT NULL REFERENCES customers(customer_id),
+    relationship_type VARCHAR(50) NOT NULL CHECK (relationship_type IN ('FAMILY', 'BUSINESS', 'SHARED_ADDRESS', 'SHARED_DEVICE', 'SHARED_IP', 'SUSPECTED_MULE')),
+    confidence_score DECIMAL(5,2) DEFAULT 50.00 CHECK (confidence_score BETWEEN 0 AND 100),
+    detected_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    notes TEXT,
+    CONSTRAINT different_customers CHECK (customer_id_1 != customer_id_2),
+    CONSTRAINT unique_relationship UNIQUE (customer_id_1, customer_id_2, relationship_type)
+);
+
+-- ============================================================================
+-- ACCOUNT DOMAIN
+-- ============================================================================
+
+-- Accounts table
+CREATE TABLE accounts (
+    account_id BIGSERIAL PRIMARY KEY,
+    customer_id BIGINT NOT NULL REFERENCES customers(customer_id),
+    account_number VARCHAR(20) NOT NULL UNIQUE,
+    account_type VARCHAR(20) NOT NULL CHECK (account_type IN ('CHECKING', 'SAVINGS', 'CREDIT', 'INVESTMENT', 'LOAN')),
+    currency CHAR(3) DEFAULT 'USD',
+    opening_date DATE NOT NULL DEFAULT CURRENT_DATE,
+    closing_date DATE,
+    status VARCHAR(20) DEFAULT 'ACTIVE' CHECK (status IN ('ACTIVE', 'SUSPENDED', 'CLOSED', 'FROZEN')),
+    current_balance DECIMAL(15,2) DEFAULT 0.00,
+    available_balance DECIMAL(15,2) DEFAULT 0.00,
+    credit_limit DECIMAL(15,2),
+    overdraft_limit DECIMAL(15,2) DEFAULT 0.00,
+    interest_rate DECIMAL(5,4),
+    monthly_fee DECIMAL(8,2) DEFAULT 0.00,
+    is_primary BOOLEAN DEFAULT FALSE,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Cards table
+CREATE TABLE cards (
+    card_id BIGSERIAL PRIMARY KEY,
+    account_id BIGINT NOT NULL REFERENCES accounts(account_id),
+    card_number_hash VARCHAR(64) NOT NULL UNIQUE, -- Hashed card number
+    card_last_four CHAR(4) NOT NULL,
+    card_type VARCHAR(20) NOT NULL CHECK (card_type IN ('DEBIT', 'CREDIT', 'PREPAID', 'VIRTUAL')),
+    card_network VARCHAR(20) NOT NULL CHECK (card_network IN ('VISA', 'MASTERCARD', 'AMEX', 'DISCOVER')),
+    issue_date DATE NOT NULL DEFAULT CURRENT_DATE,
+    expiry_date DATE NOT NULL,
+    cvv_hash VARCHAR(64) NOT NULL,
+    status VARCHAR(20) DEFAULT 'ACTIVE' CHECK (status IN ('ACTIVE', 'BLOCKED', 'EXPIRED', 'LOST', 'STOLEN')),
+    daily_limit DECIMAL(10,2) DEFAULT 5000.00,
+    monthly_limit DECIMAL(12,2) DEFAULT 50000.00,
+    is_contactless BOOLEAN DEFAULT TRUE,
+    is_international BOOLEAN DEFAULT FALSE,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- ============================================================================
+-- MERCHANT DOMAIN
+-- ============================================================================
+
+-- Merchants table
+CREATE TABLE merchants (
+    merchant_id BIGSERIAL PRIMARY KEY,
+    merchant_name VARCHAR(255) NOT NULL,
+    merchant_code VARCHAR(50) UNIQUE,
+    category_id INT NOT NULL REFERENCES merchant_categories(category_id),
+    country_id INT NOT NULL REFERENCES countries(country_id),
+    city VARCHAR(100),
+    website VARCHAR(255),
+    registration_date DATE NOT NULL DEFAULT CURRENT_DATE,
+    status VARCHAR(20) DEFAULT 'ACTIVE' CHECK (status IN ('ACTIVE', 'SUSPENDED', 'BLACKLISTED', 'CLOSED')),
+    risk_rating VARCHAR(20) DEFAULT 'LOW' CHECK (risk_rating IN ('LOW', 'MEDIUM', 'HIGH', 'CRITICAL')),
+    total_transactions BIGINT DEFAULT 0,
+    total_volume DECIMAL(18,2) DEFAULT 0.00,
+    fraud_incidents INT DEFAULT 0,
+    is_verified BOOLEAN DEFAULT FALSE,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- ============================================================================
+-- DEVICE & SESSION DOMAIN
+-- ============================================================================
+
+-- Devices table (for tracking login devices)
+CREATE TABLE devices (
+    device_id BIGSERIAL PRIMARY KEY,
+    device_fingerprint VARCHAR(64) NOT NULL UNIQUE,
+    device_type VARCHAR(20) CHECK (device_type IN ('MOBILE', 'TABLET', 'DESKTOP', 'OTHER')),
+    os_name VARCHAR(50),
+    os_version VARCHAR(50),
+    browser_name VARCHAR(50),
+    browser_version VARCHAR(50),
+    first_seen TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    last_seen TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    is_trusted BOOLEAN DEFAULT FALSE,
+    is_blacklisted BOOLEAN DEFAULT FALSE,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Login sessions
+CREATE TABLE login_sessions (
+    session_id BIGSERIAL PRIMARY KEY,
+    customer_id BIGINT NOT NULL REFERENCES customers(customer_id),
+    device_id BIGINT NOT NULL REFERENCES devices(device_id),
+    ip_address INET NOT NULL,
+    country_id INT REFERENCES countries(country_id),
+    city VARCHAR(100),
+    latitude DECIMAL(10,8),
+    longitude DECIMAL(11,8),
+    login_timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    logout_timestamp TIMESTAMP,
+    session_duration_seconds INT,
+    is_successful BOOLEAN DEFAULT TRUE,
+    failure_reason VARCHAR(255),
+    risk_score DECIMAL(5,2) DEFAULT 0.00,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- ============================================================================
+-- TRANSACTION DOMAIN
+-- ============================================================================
+
+-- Transactions table (main transaction log)
+CREATE TABLE transactions (
+    transaction_id BIGSERIAL PRIMARY KEY,
+    account_id BIGINT NOT NULL REFERENCES accounts(account_id),
+    type_id INT NOT NULL REFERENCES transaction_types(type_id),
+    transaction_date TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    amount DECIMAL(15,2) NOT NULL CHECK (amount > 0),
+    currency CHAR(3) DEFAULT 'USD',
+    merchant_id BIGINT REFERENCES merchants(merchant_id),
+    card_id BIGINT REFERENCES cards(card_id),
+    device_id BIGINT REFERENCES devices(device_id),
+    ip_address INET,
+    country_id INT REFERENCES countries(country_id),
+    city VARCHAR(100),
+    latitude DECIMAL(10,8),
+    longitude DECIMAL(11,8),
+    description TEXT,
+    reference_number VARCHAR(50) UNIQUE,
+    status VARCHAR(20) DEFAULT 'COMPLETED' CHECK (status IN ('PENDING', 'COMPLETED', 'FAILED', 'REVERSED', 'FLAGGED', 'BLOCKED')),
+    is_online BOOLEAN DEFAULT TRUE,
+    is_international BOOLEAN DEFAULT FALSE,
+    is_card_present BOOLEAN DEFAULT FALSE,
+    fraud_score DECIMAL(5,2) DEFAULT 0.00 CHECK (fraud_score BETWEEN 0 AND 100),
+    is_flagged BOOLEAN DEFAULT FALSE,
+    flagged_reason TEXT,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Beneficiaries (for transfers)
+CREATE TABLE beneficiaries (
+    beneficiary_id BIGSERIAL PRIMARY KEY,
+    customer_id BIGINT NOT NULL REFERENCES customers(customer_id),
+    beneficiary_name VARCHAR(255) NOT NULL,
+    account_number VARCHAR(50) NOT NULL,
+    bank_name VARCHAR(255),
+    bank_code VARCHAR(20),
+    country_id INT NOT NULL REFERENCES countries(country_id),
+    relationship VARCHAR(50),
+    is_verified BOOLEAN DEFAULT FALSE,
+    added_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    last_used TIMESTAMP,
+    total_transfers INT DEFAULT 0,
+    total_amount DECIMAL(18,2) DEFAULT 0.00,
+    is_active BOOLEAN DEFAULT TRUE,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Transfer transactions
+CREATE TABLE transfers (
+    transfer_id BIGSERIAL PRIMARY KEY,
+    transaction_id BIGINT NOT NULL REFERENCES transactions(transaction_id),
+    from_account_id BIGINT NOT NULL REFERENCES accounts(account_id),
+    to_account_id BIGINT REFERENCES accounts(account_id), -- NULL for external transfers
+    beneficiary_id BIGINT REFERENCES beneficiaries(beneficiary_id),
+    transfer_type VARCHAR(20) NOT NULL CHECK (transfer_type IN ('INTERNAL', 'DOMESTIC', 'INTERNATIONAL', 'WIRE')),
+    purpose VARCHAR(255),
+    is_recurring BOOLEAN DEFAULT FALSE,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- ============================================================================
+-- FRAUD DETECTION & ALERTS DOMAIN
+-- ============================================================================
+
+-- Alerts table (system-generated suspicious activity alerts)
+CREATE TABLE alerts (
+    alert_id BIGSERIAL PRIMARY KEY,
+    transaction_id BIGINT REFERENCES transactions(transaction_id),
+    customer_id BIGINT REFERENCES customers(customer_id),
+    account_id BIGINT REFERENCES accounts(account_id),
+    alert_type VARCHAR(50) NOT NULL CHECK (alert_type IN (
+        'VELOCITY_CHECK', 'AMOUNT_ANOMALY', 'GEOGRAPHIC_ANOMALY',
+        'MERCHANT_RISK', 'DEVICE_CHANGE', 'UNUSUAL_TIME',
+        'MULTIPLE_CARDS', 'ACCOUNT_TAKEOVER', 'MONEY_MULE', 'STRUCTURING'
+    )),
+    severity VARCHAR(20) DEFAULT 'MEDIUM' CHECK (severity IN ('LOW', 'MEDIUM', 'HIGH', 'CRITICAL')),
+    alert_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    description TEXT NOT NULL,
+    risk_score DECIMAL(5,2) DEFAULT 50.00 CHECK (risk_score BETWEEN 0 AND 100),
+    status VARCHAR(20) DEFAULT 'OPEN' CHECK (status IN ('OPEN', 'INVESTIGATING', 'CLOSED', 'FALSE_POSITIVE', 'CONFIRMED_FRAUD')),
+    assigned_to VARCHAR(100),
+    reviewed_date TIMESTAMP,
+    resolution_notes TEXT,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Fraud cases (confirmed fraud incidents)
+CREATE TABLE fraud_cases (
+    case_id BIGSERIAL PRIMARY KEY,
+    case_number VARCHAR(50) NOT NULL UNIQUE,
+    customer_id BIGINT REFERENCES customers(customer_id),
+    account_id BIGINT REFERENCES accounts(account_id),
+    fraud_type_id INT NOT NULL REFERENCES fraud_types(fraud_type_id),
+    detection_date TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
+    detection_method VARCHAR(50) CHECK (detection_method IN ('AUTOMATED', 'CUSTOMER_REPORT', 'MANUAL_REVIEW', 'THIRD_PARTY')),
+    amount_lost DECIMAL(15,2) DEFAULT 0.00,
+    amount_recovered DECIMAL(15,2) DEFAULT 0.00,
+    status VARCHAR(20) DEFAULT 'OPEN' CHECK (status IN ('OPEN', 'INVESTIGATING', 'RESOLVED', 'CLOSED', 'LEGAL_ACTION')),
+    priority VARCHAR(20) DEFAULT 'MEDIUM' CHECK (priority IN ('LOW', 'MEDIUM', 'HIGH', 'CRITICAL')),
+    assigned_investigator VARCHAR(100),
+    investigation_notes TEXT,
+    resolution_date TIMESTAMP,
+    resolution_summary TEXT,
+    law_enforcement_notified BOOLEAN DEFAULT FALSE,
+    customer_notified BOOLEAN DEFAULT FALSE,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Case transactions (linking transactions to fraud cases)
+CREATE TABLE case_transactions (
+    case_transaction_id BIGSERIAL PRIMARY KEY,
+    case_id BIGINT NOT NULL REFERENCES fraud_cases(case_id),
+    transaction_id BIGINT NOT NULL REFERENCES transactions(transaction_id),
+    is_fraudulent BOOLEAN DEFAULT TRUE,
+    notes TEXT,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    CONSTRAINT unique_case_transaction UNIQUE (case_id, transaction_id)
+);
+
+-- Case alerts (linking alerts to fraud cases)
+CREATE TABLE case_alerts (
+    case_alert_id BIGSERIAL PRIMARY KEY,
+    case_id BIGINT NOT NULL REFERENCES fraud_cases(case_id),
+    alert_id BIGINT NOT NULL REFERENCES alerts(alert_id),
+    relevance_score DECIMAL(5,2) DEFAULT 50.00,
+    notes TEXT,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    CONSTRAINT unique_case_alert UNIQUE (case_id, alert_id)
+);
+
+-- ============================================================================
+-- AUDIT & COMPLIANCE DOMAIN
+-- ============================================================================
+
+-- Audit log (comprehensive audit trail)
+CREATE TABLE audit_log (
+    audit_id BIGSERIAL PRIMARY KEY,
+    table_name VARCHAR(100) NOT NULL,
+    record_id BIGINT NOT NULL,
+    action VARCHAR(20) NOT NULL CHECK (action IN ('INSERT', 'UPDATE', 'DELETE', 'SELECT')),
+    old_values JSONB,
+    new_values JSONB,
+    changed_by VARCHAR(100),
+    changed_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    ip_address INET,
+    user_agent TEXT
+);
+
+-- Suspicious Activity Reports (SAR)
+CREATE TABLE suspicious_activity_reports (
+    sar_id BIGSERIAL PRIMARY KEY,
+    sar_number VARCHAR(50) NOT NULL UNIQUE,
+    case_id BIGINT REFERENCES fraud_cases(case_id),
+    customer_id BIGINT NOT NULL REFERENCES customers(customer_id),
+    filing_date DATE NOT NULL DEFAULT CURRENT_DATE,
+    activity_date_from DATE NOT NULL,
+    activity_date_to DATE NOT NULL,
+    total_amount DECIMAL(18,2) NOT NULL,
+    activity_description TEXT NOT NULL,
+    filed_by VARCHAR(100) NOT NULL,
+    status VARCHAR(20) DEFAULT 'DRAFT' CHECK (status IN ('DRAFT', 'SUBMITTED', 'ACKNOWLEDGED', 'CLOSED')),
+    submission_date DATE,
+    acknowledgment_date DATE,
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- ============================================================================
+-- INDEXES FOR PERFORMANCE
+-- ============================================================================
+
+-- Customer indexes
+CREATE INDEX idx_customers_email ON customers(email);
+CREATE INDEX idx_customers_country ON customers(country_id);
+CREATE INDEX idx_customers_risk_score ON customers(risk_score DESC);
+CREATE INDEX idx_customers_registration_date ON customers(registration_date);
+
+-- Account indexes
+CREATE INDEX idx_accounts_customer ON accounts(customer_id);
+CREATE INDEX idx_accounts_status ON accounts(status);
+CREATE INDEX idx_accounts_type ON accounts(account_type);
+
+-- Transaction indexes (critical for performance)
+CREATE INDEX idx_transactions_account ON transactions(account_id);
+CREATE INDEX idx_transactions_date ON transactions(transaction_date DESC);
+CREATE INDEX idx_transactions_merchant ON transactions(merchant_id);
+CREATE INDEX idx_transactions_status ON transactions(status);
+CREATE INDEX idx_transactions_flagged ON transactions(is_flagged) WHERE is_flagged = TRUE;
+CREATE INDEX idx_transactions_fraud_score ON transactions(fraud_score DESC);
+CREATE INDEX idx_transactions_amount ON transactions(amount);
+CREATE INDEX idx_transactions_country ON transactions(country_id);
+
+-- Card indexes
+CREATE INDEX idx_cards_account ON cards(account_id);
+CREATE INDEX idx_cards_status ON cards(status);
+
+-- Alert indexes
+CREATE INDEX idx_alerts_customer ON alerts(customer_id);
+CREATE INDEX idx_alerts_transaction ON alerts(transaction_id);
+CREATE INDEX idx_alerts_status ON alerts(status);
+CREATE INDEX idx_alerts_date ON alerts(alert_date DESC);
+CREATE INDEX idx_alerts_severity ON alerts(severity);
+
+-- Fraud case indexes
+CREATE INDEX idx_fraud_cases_customer ON fraud_cases(customer_id);
+CREATE INDEX idx_fraud_cases_status ON fraud_cases(status);
+CREATE INDEX idx_fraud_cases_detection_date ON fraud_cases(detection_date DESC);
+
+-- Login session indexes
+CREATE INDEX idx_login_sessions_customer ON login_sessions(customer_id);
+CREATE INDEX idx_login_sessions_timestamp ON login_sessions(login_timestamp DESC);
+CREATE INDEX idx_login_sessions_ip ON login_sessions(ip_address);
+
+-- Merchant indexes
+CREATE INDEX idx_merchants_category ON merchants(category_id);
+CREATE INDEX idx_merchants_country ON merchants(country_id);
+CREATE INDEX idx_merchants_risk_rating ON merchants(risk_rating);
+
+-- Comments for documentation
+COMMENT ON TABLE customers IS 'Customer master data with KYC and risk information';
+COMMENT ON TABLE accounts IS 'Customer accounts including checking, savings, credit, etc.';
+COMMENT ON TABLE transactions IS 'Main transaction log with fraud scoring';
+COMMENT ON TABLE alerts IS 'System-generated fraud alerts requiring review';
+COMMENT ON TABLE fraud_cases IS 'Confirmed fraud cases under investigation';
+COMMENT ON TABLE merchants IS 'Merchant directory with risk ratings';
+COMMENT ON TABLE cards IS 'Payment cards linked to accounts';
+COMMENT ON TABLE devices IS 'Device fingerprints for fraud detection';
+COMMENT ON TABLE login_sessions IS 'Login history for account takeover detection';
+
@@ -0,0 +1,256 @@
+-- ============================================================================
+-- Seed Data for Reference Tables
+-- ============================================================================
+-- This script is IDEMPOTENT - it will delete and recreate all reference data
+-- ============================================================================
+
+-- Clear existing reference data (in reverse dependency order)
+TRUNCATE TABLE suspicious_activity_reports CASCADE;
+TRUNCATE TABLE case_alerts CASCADE;
+TRUNCATE TABLE case_transactions CASCADE;
+TRUNCATE TABLE fraud_cases CASCADE;
+TRUNCATE TABLE alerts CASCADE;
+TRUNCATE TABLE transfers CASCADE;
+TRUNCATE TABLE beneficiaries CASCADE;
+TRUNCATE TABLE transactions CASCADE;
+TRUNCATE TABLE login_sessions CASCADE;
+TRUNCATE TABLE devices CASCADE;
+TRUNCATE TABLE cards CASCADE;
+TRUNCATE TABLE accounts CASCADE;
+TRUNCATE TABLE customer_relationships CASCADE;
+TRUNCATE TABLE customers CASCADE;
+TRUNCATE TABLE merchants CASCADE;
+TRUNCATE TABLE merchant_categories RESTART IDENTITY CASCADE;
+TRUNCATE TABLE fraud_types RESTART IDENTITY CASCADE;
+TRUNCATE TABLE transaction_types RESTART IDENTITY CASCADE;
+TRUNCATE TABLE countries RESTART IDENTITY CASCADE;
+
+-- Insert Countries
+INSERT INTO countries (country_code, country_name, region, risk_level) VALUES
+('US', 'United States', 'North America', 'LOW'),
+('CA', 'Canada', 'North America', 'LOW'),
+('GB', 'United Kingdom', 'Europe', 'LOW'),
+('DE', 'Germany', 'Europe', 'LOW'),
+('FR', 'France', 'Europe', 'LOW'),
+('IT', 'Italy', 'Europe', 'MEDIUM'),
+('ES', 'Spain', 'Europe', 'MEDIUM'),
+('AU', 'Australia', 'Oceania', 'LOW'),
+('JP', 'Japan', 'Asia', 'LOW'),
+('CN', 'China', 'Asia', 'MEDIUM'),
+('IN', 'India', 'Asia', 'MEDIUM'),
+('BR', 'Brazil', 'South America', 'MEDIUM'),
+('MX', 'Mexico', 'North America', 'MEDIUM'),
+('RU', 'Russia', 'Europe', 'HIGH'),
+('NG', 'Nigeria', 'Africa', 'HIGH'),
+('PK', 'Pakistan', 'Asia', 'HIGH'),
+('IR', 'Iran', 'Middle East', 'CRITICAL'),
+('KP', 'North Korea', 'Asia', 'CRITICAL'),
+('SY', 'Syria', 'Middle East', 'CRITICAL'),
+('VE', 'Venezuela', 'South America', 'HIGH'),
+('CU', 'Cuba', 'Caribbean', 'HIGH'),
+('MM', 'Myanmar', 'Asia', 'HIGH'),
+('AF', 'Afghanistan', 'Asia', 'CRITICAL'),
+('IQ', 'Iraq', 'Middle East', 'HIGH'),
+('LY', 'Libya', 'Africa', 'HIGH'),
+('SD', 'Sudan', 'Africa', 'HIGH'),
+('SO', 'Somalia', 'Africa', 'CRITICAL'),
+('YE', 'Yemen', 'Middle East', 'CRITICAL'),
+('ZW', 'Zimbabwe', 'Africa', 'HIGH'),
+('NL', 'Netherlands', 'Europe', 'LOW'),
+('SE', 'Sweden', 'Europe', 'LOW'),
+('NO', 'Norway', 'Europe', 'LOW'),
+('DK', 'Denmark', 'Europe', 'LOW'),
+('FI', 'Finland', 'Europe', 'LOW'),
+('CH', 'Switzerland', 'Europe', 'LOW'),
+('SG', 'Singapore', 'Asia', 'LOW'),
+('HK', 'Hong Kong', 'Asia', 'MEDIUM'),
+('KR', 'South Korea', 'Asia', 'LOW'),
+('TW', 'Taiwan', 'Asia', 'LOW'),
+('NZ', 'New Zealand', 'Oceania', 'LOW');
+
+-- Insert Merchant Categories (based on MCC codes)
+INSERT INTO merchant_categories (category_code, category_name, description, risk_weight) VALUES
+('5411', 'Grocery Stores', 'Supermarkets and grocery stores', 0.50),
+('5812', 'Restaurants', 'Eating places and restaurants', 0.60),
+('5541', 'Gas Stations', 'Service stations and fuel', 0.55),
+('5311', 'Department Stores', 'General merchandise stores', 0.70),
+('5912', 'Pharmacies', 'Drug stores and pharmacies', 0.50),
+('5999', 'Miscellaneous Retail', 'Specialty retail stores', 0.80),
+('5732', 'Electronics', 'Electronics and computer stores', 1.20),
+('5651', 'Clothing', 'Family clothing stores', 0.75),
+('5814', 'Fast Food', 'Quick service restaurants', 0.60),
+('5942', 'Books', 'Book stores', 0.65),
+('5945', 'Hobby Shops', 'Hobby, toy, and game shops', 0.70),
+('5971', 'Art Dealers', 'Art dealers and galleries', 1.50),
+('5993', 'Cigar Stores', 'Cigar stores and stands', 1.10),
+('5995', 'Pet Shops', 'Pet shops and supplies', 0.70),
+('7011', 'Hotels', 'Lodging and hotels', 0.90),
+('7512', 'Car Rental', 'Automobile rental agencies', 1.00),
+('7523', 'Parking', 'Parking lots and garages', 0.60),
+('7832', 'Movie Theaters', 'Motion picture theaters', 0.65),
+('7922', 'Theatrical Producers', 'Theatrical producers and ticket agencies', 0.80),
+('7991', 'Tourist Attractions', 'Tourist attractions and exhibits', 0.75),
+('7995', 'Gambling', 'Betting and casino gambling', 2.50),
+('5816', 'Digital Goods', 'Digital goods and games', 1.80),
+('5967', 'Direct Marketing', 'Direct marketing and inbound telemarketing', 1.90),
+('5966', 'Direct Marketing', 'Outbound telemarketing merchants', 2.00),
+('6051', 'Cryptocurrency', 'Cryptocurrency and digital currency', 3.00),
+('6211', 'Securities', 'Securities brokers and dealers', 1.50),
+('6300', 'Insurance', 'Insurance sales and underwriting', 1.20),
+('6513', 'Real Estate', 'Real estate agents and managers', 1.30),
+('7273', 'Dating Services', 'Dating and escort services', 2.20),
+('7297', 'Massage Parlors', 'Massage parlors', 2.50),
+('7995', 'Online Gambling', 'Online gambling and betting', 3.50),
+('5094', 'Precious Metals', 'Precious stones and metals', 2.80),
+('5933', 'Pawn Shops', 'Pawn shops', 2.60),
+('5960', 'Mail Order', 'Direct marketing and mail order', 1.70),
+('4829', 'Wire Transfer', 'Money transfer services', 2.40);
+
+-- Insert Transaction Types
+INSERT INTO transaction_types (type_code, type_name, description, requires_merchant) VALUES
+('PURCHASE', 'Purchase', 'Card purchase at merchant', TRUE),
+('ATM_WITHDRAWAL', 'ATM Withdrawal', 'Cash withdrawal from ATM', FALSE),
+('DEPOSIT', 'Deposit', 'Cash or check deposit', FALSE),
+('TRANSFER_OUT', 'Transfer Out', 'Outgoing transfer', FALSE),
+('TRANSFER_IN', 'Transfer In', 'Incoming transfer', FALSE),
+('PAYMENT', 'Bill Payment', 'Bill payment transaction', TRUE),
+('REFUND', 'Refund', 'Merchant refund', TRUE),
+('FEE', 'Fee', 'Bank fee or charge', FALSE),
+('INTEREST', 'Interest', 'Interest credit', FALSE),
+('WIRE_OUT', 'Wire Transfer Out', 'Outgoing wire transfer', FALSE),
+('WIRE_IN', 'Wire Transfer In', 'Incoming wire transfer', FALSE),
+('CHECK', 'Check Payment', 'Check payment', FALSE),
+('DIRECT_DEBIT', 'Direct Debit', 'Automated direct debit', TRUE),
+('CASH_ADVANCE', 'Cash Advance', 'Credit card cash advance', FALSE),
+('BALANCE_TRANSFER', 'Balance Transfer', 'Credit card balance transfer', FALSE);
+
+-- Insert Fraud Types
+INSERT INTO fraud_types (fraud_code, fraud_name, description, severity) VALUES
+('CARD_NOT_PRESENT', 'Card Not Present Fraud', 'Fraudulent online or phone transactions', 'HIGH'),
+('CARD_STOLEN', 'Stolen Card', 'Transactions using stolen physical card', 'HIGH'),
+('ACCOUNT_TAKEOVER', 'Account Takeover', 'Unauthorized access to customer account', 'CRITICAL'),
+('IDENTITY_THEFT', 'Identity Theft', 'Fraudulent account opened with stolen identity', 'CRITICAL'),
+('FRIENDLY_FRAUD', 'Friendly Fraud', 'Customer disputes legitimate transaction', 'MEDIUM'),
+('MONEY_MULE', 'Money Mule', 'Account used to launder money', 'CRITICAL'),
+('SYNTHETIC_IDENTITY', 'Synthetic Identity', 'Fake identity using real and fake information', 'CRITICAL'),
+('BUST_OUT', 'Bust Out Fraud', 'Building credit then maxing out and disappearing', 'HIGH'),
+('REFUND_FRAUD', 'Refund Fraud', 'Fraudulent refund requests', 'MEDIUM'),
+('CHARGEBACK_FRAUD', 'Chargeback Fraud', 'Abusing chargeback process', 'MEDIUM'),
+('ATM_SKIMMING', 'ATM Skimming', 'Card data stolen via ATM skimmer', 'HIGH'),
+('PHISHING', 'Phishing', 'Credentials stolen via phishing attack', 'HIGH'),
+('SIM_SWAP', 'SIM Swap', 'Phone number hijacked for 2FA bypass', 'CRITICAL'),
+('CHECK_FRAUD', 'Check Fraud', 'Fraudulent or altered checks', 'MEDIUM'),
+('WIRE_FRAUD', 'Wire Fraud', 'Fraudulent wire transfer', 'CRITICAL'),
+('STRUCTURING', 'Structuring', 'Breaking up transactions to avoid reporting', 'HIGH'),
+('SMURFING', 'Smurfing', 'Using multiple people to structure transactions', 'HIGH'),
+('TRADE_BASED', 'Trade-Based Money Laundering', 'Using trade to launder money', 'CRITICAL'),
+('SHELL_COMPANY', 'Shell Company', 'Using fake companies for fraud', 'CRITICAL'),
+('INVOICE_FRAUD', 'Invoice Fraud', 'Fraudulent invoicing schemes', 'HIGH');
+
+-- Create a function to generate realistic transaction patterns
+CREATE OR REPLACE FUNCTION generate_fraud_score(
+    p_amount DECIMAL,
+    p_is_international BOOLEAN,
+    p_merchant_risk DECIMAL,
+    p_time_of_day INT,
+    p_is_online BOOLEAN
+) RETURNS DECIMAL AS $$
+DECLARE
+    v_score DECIMAL := 0;
+BEGIN
+    -- Amount-based scoring
+    IF p_amount > 5000 THEN v_score := v_score + 20; END IF;
+    IF p_amount > 10000 THEN v_score := v_score + 30; END IF;
+    
+    -- International transactions
+    IF p_is_international THEN v_score := v_score + 15; END IF;
+    
+    -- Merchant risk
+    v_score := v_score + (p_merchant_risk * 10);
+    
+    -- Time of day (late night transactions)
+    IF p_time_of_day >= 23 OR p_time_of_day <= 4 THEN v_score := v_score + 10; END IF;
+    
+    -- Online transactions
+    IF p_is_online THEN v_score := v_score + 5; END IF;
+    
+    -- Cap at 100
+    IF v_score > 100 THEN v_score := 100; END IF;
+    
+    RETURN v_score;
+END;
+$$ LANGUAGE plpgsql;
+
+-- Create a function to update account balances
+CREATE OR REPLACE FUNCTION update_account_balance()
+RETURNS TRIGGER AS $$
+BEGIN
+    IF NEW.status = 'COMPLETED' THEN
+        IF TG_TABLE_NAME = 'transactions' THEN
+            -- Update based on transaction type
+            UPDATE accounts 
+            SET current_balance = current_balance + 
+                CASE 
+                    WHEN NEW.type_id IN (SELECT type_id FROM transaction_types WHERE type_code IN ('DEPOSIT', 'TRANSFER_IN', 'WIRE_IN', 'REFUND', 'INTEREST')) 
+                    THEN NEW.amount
+                    ELSE -NEW.amount
+                END,
+                updated_at = CURRENT_TIMESTAMP
+            WHERE account_id = NEW.account_id;
+        END IF;
+    END IF;
+    RETURN NEW;
+END;
+$$ LANGUAGE plpgsql;
+
+-- Create trigger for balance updates (commented out for bulk loading)
+-- CREATE TRIGGER trg_update_balance
+-- AFTER INSERT ON transactions
+-- FOR EACH ROW
+-- EXECUTE FUNCTION update_account_balance();
+
+-- Create a function to auto-generate alerts for suspicious transactions
+CREATE OR REPLACE FUNCTION check_suspicious_transaction()
+RETURNS TRIGGER AS $$
+DECLARE
+    v_alert_type VARCHAR(50);
+    v_description TEXT;
+    v_severity VARCHAR(20);
+BEGIN
+    -- High amount transactions
+    IF NEW.amount > 10000 THEN
+        v_alert_type := 'AMOUNT_ANOMALY';
+        v_description := 'Large transaction amount: $' || NEW.amount;
+        v_severity := 'HIGH';
+        
+        INSERT INTO alerts (transaction_id, customer_id, account_id, alert_type, severity, description, risk_score)
+        SELECT NEW.transaction_id, a.customer_id, NEW.account_id, v_alert_type, v_severity, v_description, NEW.fraud_score
+        FROM accounts a WHERE a.account_id = NEW.account_id;
+    END IF;
+    
+    -- International transactions
+    IF NEW.is_international AND NEW.amount > 1000 THEN
+        v_alert_type := 'GEOGRAPHIC_ANOMALY';
+        v_description := 'International transaction: $' || NEW.amount;
+        v_severity := 'MEDIUM';
+        
+        INSERT INTO alerts (transaction_id, customer_id, account_id, alert_type, severity, description, risk_score)
+        SELECT NEW.transaction_id, a.customer_id, NEW.account_id, v_alert_type, v_severity, v_description, NEW.fraud_score
+        FROM accounts a WHERE a.account_id = NEW.account_id;
+    END IF;
+    
+    RETURN NEW;
+END;
+$$ LANGUAGE plpgsql;
+
+-- Create trigger for alert generation (commented out for bulk loading)
+-- CREATE TRIGGER trg_check_suspicious
+-- AFTER INSERT ON transactions
+-- FOR EACH ROW
+-- WHEN (NEW.fraud_score > 50)
+-- EXECUTE FUNCTION check_suspicious_transaction();
+
+COMMENT ON FUNCTION generate_fraud_score IS 'Calculates fraud risk score based on transaction attributes';
+COMMENT ON FUNCTION update_account_balance IS 'Automatically updates account balance after transaction';
+COMMENT ON FUNCTION check_suspicious_transaction IS 'Generates alerts for suspicious transactions';
+
@@ -0,0 +1,114 @@
+#!/bin/bash
+
+# ============================================================================
+# Database Setup Script - IDEMPOTENT
+# ============================================================================
+# This script sets up the complete fraud detection database
+# It can be run multiple times safely - it will recreate everything
+# ============================================================================
+
+set -e  # Exit on error
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m' # No Color
+
+# Configuration from environment or defaults
+DB_HOST="${POSTGRES_HOST:-localhost}"
+DB_PORT="${POSTGRES_PORT:-5432}"
+DB_NAME="${POSTGRES_DB:-fraud_detection}"
+DB_USER="${POSTGRES_USER:-fraud_analyst}"
+DB_PASSWORD="${POSTGRES_PASSWORD:-SecurePass123!}"
+
+# Script directory
+SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
+
+echo -e "${BLUE}============================================================================${NC}"
+echo -e "${BLUE}Financial Fraud Detection Database - Setup Script${NC}"
+echo -e "${BLUE}============================================================================${NC}"
+echo -e "Database: ${GREEN}$DB_NAME${NC}"
+echo -e "Host: ${GREEN}$DB_HOST:$DB_PORT${NC}"
+echo -e "User: ${GREEN}$DB_USER${NC}"
+echo -e "${BLUE}============================================================================${NC}"
+echo ""
+
+# Function to execute SQL command
+execute_sql() {
+    PGPASSWORD=$DB_PASSWORD psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -c "$1" 2>&1
+}
+
+# Function to execute SQL file
+execute_sql_file() {
+    local file=$1
+    local description=$2
+    echo -e "${YELLOW}Executing: $description${NC}"
+    PGPASSWORD=$DB_PASSWORD psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -f "$file" 2>&1
+    if [ $? -eq 0 ]; then
+        echo -e "${GREEN}✓ Success: $description${NC}"
+    else
+        echo -e "${RED}✗ Failed: $description${NC}"
+        exit 1
+    fi
+    echo ""
+}
+
+# Wait for PostgreSQL to be ready
+echo -e "${YELLOW}Waiting for PostgreSQL to be ready...${NC}"
+max_attempts=30
+attempt=0
+until PGPASSWORD=$DB_PASSWORD psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -c '\q' 2>/dev/null; do
+    attempt=$((attempt + 1))
+    if [ $attempt -ge $max_attempts ]; then
+        echo -e "${RED}✗ PostgreSQL is not available after $max_attempts attempts${NC}"
+        exit 1
+    fi
+    echo -e "${YELLOW}Waiting for PostgreSQL... (attempt $attempt/$max_attempts)${NC}"
+    sleep 2
+done
+echo -e "${GREEN}✓ PostgreSQL is ready${NC}"
+echo ""
+
+# Step 1: Create schema (drops and recreates all tables)
+echo -e "${BLUE}[Step 1/3] Creating database schema...${NC}"
+execute_sql_file "$PROJECT_ROOT/schema/01-create-tables.sql" "Creating tables, indexes, and constraints"
+
+# Step 2: Load seed data (reference tables)
+echo -e "${BLUE}[Step 2/3] Loading reference data...${NC}"
+execute_sql_file "$PROJECT_ROOT/schema/02-seed-data.sql" "Loading countries, merchant categories, transaction types, and fraud types"
+
+# Step 3: Verify setup
+echo -e "${BLUE}[Step 3/3] Verifying database setup...${NC}"
+echo -e "${YELLOW}Checking table counts...${NC}"
+
+# Get table counts
+table_count=$(execute_sql "SELECT COUNT(*) FROM information_schema.tables WHERE table_schema = 'public' AND table_type = 'BASE TABLE';" | grep -E '^\s*[0-9]+' | tr -d ' ')
+echo -e "Tables created: ${GREEN}$table_count${NC}"
+
+# Get reference data counts
+country_count=$(execute_sql "SELECT COUNT(*) FROM countries;" | grep -E '^\s*[0-9]+' | tr -d ' ')
+echo -e "Countries: ${GREEN}$country_count${NC}"
+
+category_count=$(execute_sql "SELECT COUNT(*) FROM merchant_categories;" | grep -E '^\s*[0-9]+' | tr -d ' ')
+echo -e "Merchant categories: ${GREEN}$category_count${NC}"
+
+transaction_type_count=$(execute_sql "SELECT COUNT(*) FROM transaction_types;" | grep -E '^\s*[0-9]+' | tr -d ' ')
+echo -e "Transaction types: ${GREEN}$transaction_type_count${NC}"
+
+fraud_type_count=$(execute_sql "SELECT COUNT(*) FROM fraud_types;" | grep -E '^\s*[0-9]+' | tr -d ' ')
+echo -e "Fraud types: ${GREEN}$fraud_type_count${NC}"
+
+echo ""
+echo -e "${GREEN}============================================================================${NC}"
+echo -e "${GREEN}✓ Database setup completed successfully!${NC}"
+echo -e "${GREEN}============================================================================${NC}"
+echo ""
+echo -e "${YELLOW}Next steps:${NC}"
+echo -e "1. Generate test data: ${BLUE}./scripts/generate-data.sh${NC}"
+echo -e "2. Access DB-UI at: ${BLUE}http://localhost:3000${NC}"
+echo -e "3. Start learning SQL with exercises in: ${BLUE}./exercises/${NC}"
+echo ""
+
@@ -0,0 +1,210 @@
+#!/bin/bash
+
+# ============================================================================
+# Setup Verification Script
+# ============================================================================
+# Verifies that the fraud detection database is properly set up
+# ============================================================================
+
+set -e
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m' # No Color
+
+# Configuration
+DB_HOST="${POSTGRES_HOST:-localhost}"
+DB_PORT="${POSTGRES_PORT:-5432}"
+DB_NAME="${POSTGRES_DB:-fraud_detection}"
+DB_USER="${POSTGRES_USER:-fraud_analyst}"
+DB_PASSWORD="${POSTGRES_PASSWORD:-SecurePass123!}"
+
+echo -e "${BLUE}============================================================================${NC}"
+echo -e "${BLUE}Financial Fraud Detection Database - Verification${NC}"
+echo -e "${BLUE}============================================================================${NC}"
+echo ""
+
+# Function to execute SQL and get result
+execute_sql() {
+    PGPASSWORD=$DB_PASSWORD psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -t -c "$1" 2>/dev/null | xargs
+}
+
+# Check 1: Docker containers
+echo -e "${YELLOW}[1/10] Checking Docker containers...${NC}"
+if docker ps | grep -q "fraud_detection_db"; then
+    echo -e "${GREEN}✓ PostgreSQL container is running${NC}"
+else
+    echo -e "${RED}✗ PostgreSQL container is not running${NC}"
+    echo -e "${YELLOW}Run: docker-compose up -d${NC}"
+    exit 1
+fi
+
+if docker ps | grep -q "fraud_detection_ui"; then
+    echo -e "${GREEN}✓ DB-UI container is running${NC}"
+else
+    echo -e "${RED}✗ DB-UI container is not running${NC}"
+    echo -e "${YELLOW}Run: docker-compose up -d${NC}"
+    exit 1
+fi
+echo ""
+
+# Check 2: Database connectivity
+echo -e "${YELLOW}[2/10] Checking database connectivity...${NC}"
+if PGPASSWORD=$DB_PASSWORD psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -c '\q' 2>/dev/null; then
+    echo -e "${GREEN}✓ Can connect to database${NC}"
+else
+    echo -e "${RED}✗ Cannot connect to database${NC}"
+    exit 1
+fi
+echo ""
+
+# Check 3: Tables exist
+echo -e "${YELLOW}[3/10] Checking database schema...${NC}"
+table_count=$(execute_sql "SELECT COUNT(*) FROM information_schema.tables WHERE table_schema = 'public' AND table_type = 'BASE TABLE';")
+if [ "$table_count" -ge 20 ]; then
+    echo -e "${GREEN}✓ Schema created ($table_count tables)${NC}"
+else
+    echo -e "${RED}✗ Schema incomplete (only $table_count tables)${NC}"
+    echo -e "${YELLOW}Run: ./scripts/setup-database.sh${NC}"
+    exit 1
+fi
+echo ""
+
+# Check 4: Reference data
+echo -e "${YELLOW}[4/10] Checking reference data...${NC}"
+country_count=$(execute_sql "SELECT COUNT(*) FROM countries;")
+category_count=$(execute_sql "SELECT COUNT(*) FROM merchant_categories;")
+type_count=$(execute_sql "SELECT COUNT(*) FROM transaction_types;")
+fraud_type_count=$(execute_sql "SELECT COUNT(*) FROM fraud_types;")
+
+if [ "$country_count" -ge 40 ]; then
+    echo -e "${GREEN}✓ Countries loaded ($country_count)${NC}"
+else
+    echo -e "${RED}✗ Countries not loaded${NC}"
+fi
+
+if [ "$category_count" -ge 30 ]; then
+    echo -e "${GREEN}✓ Merchant categories loaded ($category_count)${NC}"
+else
+    echo -e "${RED}✗ Merchant categories not loaded${NC}"
+fi
+
+if [ "$type_count" -ge 10 ]; then
+    echo -e "${GREEN}✓ Transaction types loaded ($type_count)${NC}"
+else
+    echo -e "${RED}✗ Transaction types not loaded${NC}"
+fi
+
+if [ "$fraud_type_count" -ge 15 ]; then
+    echo -e "${GREEN}✓ Fraud types loaded ($fraud_type_count)${NC}"
+else
+    echo -e "${RED}✗ Fraud types not loaded${NC}"
+fi
+echo ""
+
+# Check 5: Customer data
+echo -e "${YELLOW}[5/10] Checking customer data...${NC}"
+customer_count=$(execute_sql "SELECT COUNT(*) FROM customers;")
+if [ "$customer_count" -ge 10000 ]; then
+    echo -e "${GREEN}✓ Customers generated ($customer_count)${NC}"
+else
+    echo -e "${YELLOW}⚠ Limited customer data ($customer_count)${NC}"
+    echo -e "${YELLOW}Run: ./data/generate_data.sh${NC}"
+fi
+echo ""
+
+# Check 6: Account data
+echo -e "${YELLOW}[6/10] Checking account data...${NC}"
+account_count=$(execute_sql "SELECT COUNT(*) FROM accounts;")
+if [ "$account_count" -ge 10000 ]; then
+    echo -e "${GREEN}✓ Accounts generated ($account_count)${NC}"
+else
+    echo -e "${YELLOW}⚠ Limited account data ($account_count)${NC}"
+fi
+echo ""
+
+# Check 7: Transaction data
+echo -e "${YELLOW}[7/10] Checking transaction data...${NC}"
+transaction_count=$(execute_sql "SELECT COUNT(*) FROM transactions;")
+if [ "$transaction_count" -ge 100000 ]; then
+    echo -e "${GREEN}✓ Transactions generated ($transaction_count)${NC}"
+else
+    echo -e "${YELLOW}⚠ Limited transaction data ($transaction_count)${NC}"
+fi
+echo ""
+
+# Check 8: Fraud data
+echo -e "${YELLOW}[8/10] Checking fraud detection data...${NC}"
+alert_count=$(execute_sql "SELECT COUNT(*) FROM alerts;")
+case_count=$(execute_sql "SELECT COUNT(*) FROM fraud_cases;")
+
+if [ "$alert_count" -ge 100 ]; then
+    echo -e "${GREEN}✓ Alerts generated ($alert_count)${NC}"
+else
+    echo -e "${YELLOW}⚠ Limited alert data ($alert_count)${NC}"
+fi
+
+if [ "$case_count" -ge 10 ]; then
+    echo -e "${GREEN}✓ Fraud cases generated ($case_count)${NC}"
+else
+    echo -e "${YELLOW}⚠ Limited fraud case data ($case_count)${NC}"
+fi
+echo ""
+
+# Check 9: Indexes
+echo -e "${YELLOW}[9/10] Checking database indexes...${NC}"
+index_count=$(execute_sql "SELECT COUNT(*) FROM pg_indexes WHERE schemaname = 'public';")
+if [ "$index_count" -ge 20 ]; then
+    echo -e "${GREEN}✓ Indexes created ($index_count)${NC}"
+else
+    echo -e "${YELLOW}⚠ Limited indexes ($index_count)${NC}"
+fi
+echo ""
+
+# Check 10: DB-UI accessibility
+echo -e "${YELLOW}[10/10] Checking DB-UI web interface...${NC}"
+if curl -s -o /dev/null -w "%{http_code}" http://localhost:3000 | grep -q "200\|302"; then
+    echo -e "${GREEN}✓ DB-UI accessible at http://localhost:3000${NC}"
+else
+    echo -e "${YELLOW}⚠ DB-UI may not be ready yet (still starting up)${NC}"
+fi
+echo ""
+
+# Summary
+echo -e "${BLUE}============================================================================${NC}"
+echo -e "${GREEN}Verification Summary${NC}"
+echo -e "${BLUE}============================================================================${NC}"
+echo ""
+echo -e "${YELLOW}Database Statistics:${NC}"
+echo -e "  Customers:     ${GREEN}$customer_count${NC}"
+echo -e "  Accounts:      ${GREEN}$account_count${NC}"
+echo -e "  Transactions:  ${GREEN}$transaction_count${NC}"
+echo -e "  Alerts:        ${GREEN}$alert_count${NC}"
+echo -e "  Fraud Cases:   ${GREEN}$case_count${NC}"
+echo ""
+
+if [ "$transaction_count" -ge 100000 ]; then
+    echo -e "${GREEN}✓ Database is ready for SQL learning!${NC}"
+    echo ""
+    echo -e "${YELLOW}Next steps:${NC}"
+    echo -e "1. Access DB-UI: ${BLUE}http://localhost:3000${NC}"
+    echo -e "2. Start learning: ${BLUE}exercises/01-basic-queries/README.md${NC}"
+    echo -e "3. Quick start guide: ${BLUE}docs/QUICKSTART.md${NC}"
+else
+    echo -e "${YELLOW}⚠ Database has minimal data${NC}"
+    echo ""
+    echo -e "${YELLOW}To generate full dataset:${NC}"
+    echo -e "  ${BLUE}./data/generate_data.sh${NC}"
+    echo ""
+    echo -e "${YELLOW}This will generate:${NC}"
+    echo -e "  - 100,000 customers"
+    echo -e "  - 150,000 accounts"
+    echo -e "  - 5,000,000 transactions"
+    echo -e "  - 50,000+ alerts"
+    echo -e "  - 5,000+ fraud cases"
+fi
+echo ""
+