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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
This commit is contained in:
Alphaeus Mote
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#!/bin/bash
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# ============================================================================
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# Financial Fraud Detection - Data Generation Script - IDEMPOTENT
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# ============================================================================
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# Generates realistic test data with embedded fraud patterns
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# This script is IDEMPOTENT - it will clear and regenerate all data
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# ============================================================================
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set -e
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# Colors for output
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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BLUE='\033[0;34m'
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NC='\033[0m' # No Color
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# Configuration
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DB_HOST="${POSTGRES_HOST:-localhost}"
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DB_PORT="${POSTGRES_PORT:-5432}"
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DB_NAME="${POSTGRES_DB:-fraud_detection}"
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DB_USER="${POSTGRES_USER:-fraud_analyst}"
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DB_PASSWORD="${POSTGRES_PASSWORD:-SecurePass123!}"
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# Data volumes
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NUM_CUSTOMERS=100000
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NUM_ACCOUNTS=150000
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NUM_MERCHANTS=50000
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NUM_DEVICES=75000
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NUM_CARDS=200000
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NUM_TRANSACTIONS=5000000
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FRAUD_PERCENTAGE=7 # 7% of transactions will be fraudulent
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echo -e "${BLUE}============================================================================${NC}"
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echo -e "${BLUE}Financial Fraud Detection Database - Data Generation${NC}"
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echo -e "${BLUE}============================================================================${NC}"
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echo -e "Target Database: ${GREEN}$DB_NAME@$DB_HOST:$DB_PORT${NC}"
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echo -e "Customers: ${GREEN}$NUM_CUSTOMERS${NC}"
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echo -e "Accounts: ${GREEN}$NUM_ACCOUNTS${NC}"
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echo -e "Merchants: ${GREEN}$NUM_MERCHANTS${NC}"
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echo -e "Cards: ${GREEN}$NUM_CARDS${NC}"
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echo -e "Transactions: ${GREEN}$NUM_TRANSACTIONS${NC} (${YELLOW}${FRAUD_PERCENTAGE}% fraudulent${NC})"
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echo -e "${BLUE}============================================================================${NC}"
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echo ""
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echo -e "${RED}WARNING: This will DELETE all existing data and regenerate it!${NC}"
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echo -e "${YELLOW}Press Ctrl+C within 5 seconds to cancel...${NC}"
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sleep 5
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echo ""
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# Function to execute SQL
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execute_sql() {
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PGPASSWORD=$DB_PASSWORD psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -c "$1" 2>&1
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}
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# Function to execute SQL file
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execute_sql_file() {
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PGPASSWORD=$DB_PASSWORD psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -f "$1" 2>&1
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}
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# Get script directory
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SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
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# Clear existing data (preserve reference tables)
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echo -e "${YELLOW}[0/9] Clearing existing data...${NC}"
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execute_sql "TRUNCATE TABLE audit_log CASCADE;"
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execute_sql "TRUNCATE TABLE suspicious_activity_reports CASCADE;"
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execute_sql "TRUNCATE TABLE case_alerts CASCADE;"
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execute_sql "TRUNCATE TABLE case_transactions CASCADE;"
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execute_sql "TRUNCATE TABLE fraud_cases CASCADE;"
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execute_sql "TRUNCATE TABLE alerts CASCADE;"
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execute_sql "TRUNCATE TABLE transfers CASCADE;"
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execute_sql "TRUNCATE TABLE beneficiaries CASCADE;"
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execute_sql "TRUNCATE TABLE transactions CASCADE;"
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execute_sql "TRUNCATE TABLE login_sessions CASCADE;"
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execute_sql "TRUNCATE TABLE devices RESTART IDENTITY CASCADE;"
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execute_sql "TRUNCATE TABLE cards RESTART IDENTITY CASCADE;"
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execute_sql "TRUNCATE TABLE accounts RESTART IDENTITY CASCADE;"
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execute_sql "TRUNCATE TABLE customer_relationships RESTART IDENTITY CASCADE;"
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execute_sql "TRUNCATE TABLE customers RESTART IDENTITY CASCADE;"
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execute_sql "TRUNCATE TABLE merchants RESTART IDENTITY CASCADE;"
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echo -e "${GREEN}✓ Existing data cleared${NC}"
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echo ""
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# Load geographic reference data
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echo -e "${YELLOW}[1/9] Loading geographic reference data...${NC}"
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execute_sql_file "$SCRIPT_DIR/reference/load_geographic_data.sql" > /dev/null
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echo -e "${GREEN}✓ Geographic data loaded (100 US cities, 210 world cities)${NC}"
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echo ""
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echo -e "${YELLOW}[2/9] Generating Customers...${NC}"
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cat > /tmp/generate_customers.sql << 'EOF'
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-- Generate customers with realistic geographic data
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INSERT INTO customers (
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first_name, last_name, email, phone, date_of_birth, ssn_hash,
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address_line1, city, state, postal_code, country_id,
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registration_date, kyc_status, risk_score, is_pep, is_active
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)
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SELECT
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'Customer' || gs.id AS first_name,
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'User' || gs.id AS last_name,
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'customer' || gs.id || '@email.com' AS email,
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'+1' || LPAD((1000000000 + (gs.id % 9000000000))::TEXT, 10, '0') AS phone,
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DATE '1950-01-01' + (random() * 25000)::INT AS date_of_birth,
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encode(digest('SSN' || gs.id::TEXT, 'sha256'), 'hex') AS ssn_hash,
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(gs.id % 10000) || ' Main Street' AS address_line1,
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-- Use real US cities from temp table
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(SELECT city FROM temp_us_cities WHERE id = ((gs.id % 100) + 1)) AS city,
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(SELECT state_code FROM temp_us_cities WHERE id = ((gs.id % 100) + 1)) AS state,
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LPAD((10000 + (gs.id % 90000))::TEXT, 5, '0') AS postal_code,
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CASE
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WHEN random() < 0.85 THEN 1 -- 85% US
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WHEN random() < 0.90 THEN 2 -- 5% Canada
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WHEN random() < 0.95 THEN 3 -- 5% UK
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ELSE (3 + (gs.id % 37)) -- 5% other countries
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END AS country_id,
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TIMESTAMP '2020-01-01' + (random() * 1460)::INT * INTERVAL '1 day' AS registration_date,
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CASE
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WHEN random() < 0.90 THEN 'VERIFIED'
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WHEN random() < 0.95 THEN 'PENDING'
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ELSE 'REJECTED'
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END AS kyc_status,
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(random() * 100)::DECIMAL(5,2) AS risk_score,
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random() < 0.02 AS is_pep, -- 2% are PEPs
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random() < 0.98 AS is_active -- 98% active
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FROM generate_series(1, 100000) AS gs(id);
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EOF
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execute_sql_file /tmp/generate_customers.sql > /dev/null
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echo -e "${GREEN}✓ Generated $NUM_CUSTOMERS customers${NC}"
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echo ""
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echo -e "${YELLOW}[3/9] Generating Accounts...${NC}"
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cat > /tmp/generate_accounts.sql << 'EOF'
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-- Generate accounts (1-2 accounts per customer on average)
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INSERT INTO accounts (
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customer_id, account_number, account_type, currency,
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opening_date, status, current_balance, available_balance,
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credit_limit, overdraft_limit, is_primary
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)
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SELECT
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(gs.id % 100000) + 1 AS customer_id,
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'ACC' || LPAD(gs.id::TEXT, 12, '0') AS account_number,
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CASE (gs.id % 10)
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WHEN 0 THEN 'CHECKING'
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WHEN 1 THEN 'CHECKING'
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WHEN 2 THEN 'CHECKING'
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WHEN 3 THEN 'SAVINGS'
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WHEN 4 THEN 'SAVINGS'
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WHEN 5 THEN 'CREDIT'
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WHEN 6 THEN 'CREDIT'
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WHEN 7 THEN 'INVESTMENT'
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ELSE 'CHECKING'
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END AS account_type,
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'USD' AS currency,
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DATE '2020-01-01' + (random() * 1460)::INT AS opening_date,
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CASE
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WHEN random() < 0.95 THEN 'ACTIVE'
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WHEN random() < 0.98 THEN 'SUSPENDED'
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ELSE 'CLOSED'
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END AS status,
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(random() * 50000)::DECIMAL(15,2) AS current_balance,
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(random() * 50000)::DECIMAL(15,2) AS available_balance,
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CASE
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WHEN (gs.id % 10) IN (5, 6) THEN (5000 + random() * 45000)::DECIMAL(15,2)
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ELSE NULL
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END AS credit_limit,
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CASE
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WHEN (gs.id % 10) IN (0, 1, 2) THEN (random() * 1000)::DECIMAL(15,2)
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ELSE 0
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END AS overdraft_limit,
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(gs.id % 2) = 0 AS is_primary
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FROM generate_series(1, 150000) AS gs(id);
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EOF
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execute_sql_file /tmp/generate_accounts.sql > /dev/null
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echo -e "${GREEN}✓ Generated $NUM_ACCOUNTS accounts${NC}"
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echo ""
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echo -e "${YELLOW}[4/9] Generating Merchants...${NC}"
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cat > /tmp/generate_merchants.sql << 'EOF'
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-- Generate merchants with realistic geographic data
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INSERT INTO merchants (
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merchant_name, merchant_code, category_id, country_id,
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city, registration_date, status, risk_rating, is_verified
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)
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SELECT
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CASE (gs.id % 15)
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WHEN 0 THEN 'Walmart Store #' || gs.id
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WHEN 1 THEN 'Amazon Marketplace #' || gs.id
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WHEN 2 THEN 'Shell Gas Station #' || gs.id
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WHEN 3 THEN 'McDonalds #' || gs.id
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WHEN 4 THEN 'Starbucks #' || gs.id
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WHEN 5 THEN 'Target Store #' || gs.id
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WHEN 6 THEN 'Best Buy #' || gs.id
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WHEN 7 THEN 'CVS Pharmacy #' || gs.id
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WHEN 8 THEN 'Home Depot #' || gs.id
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WHEN 9 THEN 'Costco #' || gs.id
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WHEN 10 THEN 'Apple Store #' || gs.id
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WHEN 11 THEN 'Marriott Hotel #' || gs.id
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WHEN 12 THEN 'Delta Airlines #' || gs.id
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WHEN 13 THEN 'Online Casino #' || gs.id
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ELSE 'Merchant #' || gs.id
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END AS merchant_name,
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'MER' || LPAD(gs.id::TEXT, 10, '0') AS merchant_code,
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((gs.id % 35) + 1) AS category_id,
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CASE
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WHEN random() < 0.80 THEN 1 -- 80% US merchants
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WHEN random() < 0.90 THEN 2 -- 10% Canada
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ELSE (3 + (gs.id % 37)) -- 10% international
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END AS country_id,
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-- Use real cities based on country
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CASE
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WHEN random() < 0.80 THEN (SELECT city FROM temp_us_cities WHERE id = ((gs.id % 100) + 1))
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ELSE (SELECT city FROM temp_world_cities WHERE id = ((gs.id % 210) + 1))
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END AS city,
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DATE '2015-01-01' + (random() * 3000)::INT AS registration_date,
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CASE
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WHEN random() < 0.95 THEN 'ACTIVE'
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WHEN random() < 0.98 THEN 'SUSPENDED'
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ELSE 'BLACKLISTED'
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END AS status,
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CASE
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WHEN (gs.id % 35) + 1 IN (21, 22, 23, 24, 25, 26, 31, 32, 33, 34, 35) THEN
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CASE
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WHEN random() < 0.5 THEN 'HIGH'
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ELSE 'CRITICAL'
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END
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WHEN random() < 0.80 THEN 'LOW'
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ELSE 'MEDIUM'
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END AS risk_rating,
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random() < 0.90 AS is_verified
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FROM generate_series(1, 50000) AS gs(id);
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EOF
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execute_sql_file /tmp/generate_merchants.sql > /dev/null
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echo -e "${GREEN}✓ Generated $NUM_MERCHANTS merchants${NC}"
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echo ""
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echo -e "${YELLOW}[5/9] Generating Devices...${NC}"
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cat > /tmp/generate_devices.sql << 'EOF'
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-- Generate devices
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INSERT INTO devices (
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device_fingerprint, device_type, os_name, os_version,
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browser_name, browser_version, is_trusted, is_blacklisted
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)
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SELECT
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encode(digest('DEVICE' || gs.id::TEXT, 'sha256'), 'hex') AS device_fingerprint,
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CASE (gs.id % 4)
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WHEN 0 THEN 'MOBILE'
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WHEN 1 THEN 'DESKTOP'
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WHEN 2 THEN 'TABLET'
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ELSE 'MOBILE'
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END AS device_type,
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CASE (gs.id % 5)
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WHEN 0 THEN 'iOS'
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WHEN 1 THEN 'Android'
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WHEN 2 THEN 'Windows'
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WHEN 3 THEN 'macOS'
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ELSE 'Linux'
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END AS os_name,
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CASE (gs.id % 5)
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WHEN 0 THEN '15.0'
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WHEN 1 THEN '12.0'
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WHEN 2 THEN '11.0'
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WHEN 3 THEN '13.0'
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ELSE '10.0'
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END AS os_version,
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CASE (gs.id % 4)
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WHEN 0 THEN 'Chrome'
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WHEN 1 THEN 'Safari'
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WHEN 2 THEN 'Firefox'
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ELSE 'Edge'
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END AS browser_name,
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'100.0' AS browser_version,
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random() < 0.85 AS is_trusted,
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random() < 0.03 AS is_blacklisted
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FROM generate_series(1, 75000) AS gs(id);
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EOF
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execute_sql_file /tmp/generate_devices.sql > /dev/null
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echo -e "${GREEN}✓ Generated $NUM_DEVICES devices${NC}"
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echo ""
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echo -e "${YELLOW}[6/9] Generating Cards...${NC}"
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cat > /tmp/generate_cards.sql << 'EOF'
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-- Generate cards (1-2 cards per account on average)
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INSERT INTO cards (
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account_id, card_number_hash, card_last_four, card_type, card_network,
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issue_date, expiry_date, cvv_hash, status, daily_limit, monthly_limit,
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is_contactless, is_international
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)
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SELECT
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((gs.id - 1) % 150000) + 1 AS account_id,
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encode(digest('CARD' || gs.id::TEXT, 'sha256'), 'hex') AS card_number_hash,
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LPAD((gs.id % 10000)::TEXT, 4, '0') AS card_last_four,
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CASE (gs.id % 4)
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WHEN 0 THEN 'DEBIT'
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WHEN 1 THEN 'CREDIT'
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WHEN 2 THEN 'DEBIT'
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ELSE 'CREDIT'
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END AS card_type,
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CASE (gs.id % 4)
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WHEN 0 THEN 'VISA'
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WHEN 1 THEN 'MASTERCARD'
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WHEN 2 THEN 'AMEX'
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ELSE 'DISCOVER'
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END AS card_network,
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DATE '2020-01-01' + (random() * 1000)::INT AS issue_date,
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DATE '2025-01-01' + (random() * 1825)::INT AS expiry_date,
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encode(digest('CVV' || gs.id::TEXT, 'sha256'), 'hex') AS cvv_hash,
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CASE
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WHEN random() < 0.95 THEN 'ACTIVE'
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WHEN random() < 0.97 THEN 'BLOCKED'
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WHEN random() < 0.99 THEN 'LOST'
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ELSE 'STOLEN'
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END AS status,
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(1000 + random() * 9000)::DECIMAL(10,2) AS daily_limit,
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(10000 + random() * 90000)::DECIMAL(12,2) AS monthly_limit,
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random() < 0.90 AS is_contactless,
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random() < 0.30 AS is_international
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FROM generate_series(1, 200000) AS gs(id);
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EOF
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execute_sql_file /tmp/generate_cards.sql > /dev/null
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echo -e "${GREEN}✓ Generated $NUM_CARDS cards${NC}"
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echo ""
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echo -e "${YELLOW}[7/9] Generating Login Sessions...${NC}"
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cat > /tmp/generate_sessions.sql << 'EOF'
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-- Generate login sessions with realistic geographic data
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INSERT INTO login_sessions (
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customer_id, device_id, ip_address, country_id, city,
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login_timestamp, logout_timestamp, session_duration_seconds,
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is_successful, risk_score
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)
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SELECT
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((gs.id - 1) % 100000) + 1 AS customer_id,
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((gs.id - 1) % 75000) + 1 AS device_id,
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('192.168.' || ((gs.id % 255) + 1) || '.' || ((gs.id % 255) + 1))::INET AS ip_address,
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CASE
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WHEN random() < 0.85 THEN 1
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ELSE ((gs.id % 40) + 1)
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END AS country_id,
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-- Use real cities
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CASE
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WHEN random() < 0.85 THEN (SELECT city FROM temp_us_cities WHERE id = ((gs.id % 100) + 1))
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ELSE (SELECT city FROM temp_world_cities WHERE id = ((gs.id % 210) + 1))
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END AS city,
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TIMESTAMP '2023-01-01' + (random() * 730)::INT * INTERVAL '1 day' + (random() * 86400)::INT * INTERVAL '1 second' AS login_timestamp,
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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,
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(300 + random() * 7200)::INT AS session_duration_seconds,
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random() < 0.98 AS is_successful,
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(random() * 100)::DECIMAL(5,2) AS risk_score
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FROM generate_series(1, 500000) AS gs(id);
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EOF
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execute_sql_file /tmp/generate_sessions.sql > /dev/null
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echo -e "${GREEN}✓ Generated 500,000 login sessions${NC}"
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echo ""
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echo -e "${YELLOW}[8/9] Generating Transactions (this may take a while)...${NC}"
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echo -e "${BLUE}This step generates $NUM_TRANSACTIONS transactions with fraud patterns${NC}"
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# Generate transactions in batches to avoid memory issues
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BATCH_SIZE=500000
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NUM_BATCHES=$((NUM_TRANSACTIONS / BATCH_SIZE))
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for batch in $(seq 1 $NUM_BATCHES); do
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START_ID=$(( (batch - 1) * BATCH_SIZE + 1 ))
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END_ID=$(( batch * BATCH_SIZE ))
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echo -e "${BLUE} Batch $batch/$NUM_BATCHES (transactions $START_ID to $END_ID)...${NC}"
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cat > /tmp/generate_transactions_batch.sql << EOF
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-- Generate transactions batch
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INSERT INTO transactions (
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account_id, type_id, transaction_date, amount, currency,
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merchant_id, card_id, device_id, ip_address, country_id, city,
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description, reference_number, status, is_online, is_international,
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is_card_present, fraud_score, is_flagged
|
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)
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SELECT
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((gs.id - 1) % 150000) + 1 AS account_id,
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((gs.id % 15) + 1) AS type_id,
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TIMESTAMP '2023-01-01' + (random() * 730)::INT * INTERVAL '1 day' + (random() * 86400)::INT * INTERVAL '1 second' AS transaction_date,
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CASE
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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 ""
|
||||
|
||||
Reference in New Issue
Block a user