""" Fraud Detection Dataset Generator Developer: Molla Samser Designer & Tester: Rima Khatun Website: https://rskworld.in Email: help@rskworld.in, support@rskworld.in, info@rskworld.com Phone: +91 93305 39277 Company: RSK World Description: Generates synthetic fraud detection dataset with transaction records """ import pandas as pd import numpy as np from datetime import datetime, timedelta import random # Set random seed for reproducibility np.random.seed(42) random.seed(42) def generate_fraud_dataset(n_samples=10000, fraud_ratio=0.05): """ Generate synthetic fraud detection dataset Parameters: ----------- n_samples : int Total number of transactions to generate fraud_ratio : float Ratio of fraudulent transactions (for imbalanced dataset) Returns: -------- pd.DataFrame Dataset with transaction features and fraud labels """ n_fraud = int(n_samples * fraud_ratio) n_normal = n_samples - n_fraud # Merchant categories merchant_categories = ['Retail', 'Grocery', 'Restaurant', 'Gas', 'Online', 'Pharmacy', 'Travel', 'Entertainment', 'Utilities', 'Other'] # Device types device_types = ['Mobile', 'Desktop', 'Tablet', 'ATM', 'POS'] # Location codes locations = ['US', 'UK', 'CA', 'AU', 'FR', 'DE', 'IT', 'ES', 'NL', 'BE'] data = [] # Generate normal transactions for i in range(n_normal): user_id = np.random.randint(1, 5000) amount = np.random.lognormal(mean=3.5, sigma=1.2) amount = min(amount, 10000) # Cap at 10000 transaction = { 'transaction_id': f'TXN_{i+1:06d}', 'user_id': f'USER_{user_id:04d}', 'amount': round(amount, 2), 'merchant_category': np.random.choice(merchant_categories), 'location': np.random.choice(locations), 'timestamp': datetime.now() - timedelta(days=np.random.randint(0, 365), hours=np.random.randint(0, 24), minutes=np.random.randint(0, 60)), 'device_type': np.random.choice(device_types), 'user_age': np.random.randint(18, 80), 'account_age_days': np.random.randint(1, 3650), 'transaction_count_24h': np.random.poisson(2), 'avg_transaction_amount': round(np.random.lognormal(mean=3.5, sigma=1.0), 2), 'is_foreign_transaction': np.random.choice([0, 1], p=[0.7, 0.3]), 'is_weekend': np.random.choice([0, 1], p=[0.7, 0.3]), 'hour_of_day': np.random.randint(0, 24), 'is_fraud': 0 } data.append(transaction) # Generate fraudulent transactions (with different patterns) for i in range(n_fraud): user_id = np.random.randint(1, 5000) # Fraud transactions tend to be larger amount = np.random.lognormal(mean=4.5, sigma=1.5) amount = min(amount, 50000) # Higher cap for fraud # Fraud more likely at unusual hours unusual_hours = [0, 1, 2, 3, 4, 5, 22, 23] normal_hours = list(range(6, 22)) all_hours = unusual_hours + normal_hours # Create probabilities: 0.12 for unusual hours, rest distributed among normal hours p_unusual = 0.12 total_unusual_prob = len(unusual_hours) * p_unusual p_normal = (1 - total_unusual_prob) / len(normal_hours) probs = [p_unusual] * len(unusual_hours) + [p_normal] * len(normal_hours) hour = np.random.choice(all_hours, p=probs) transaction = { 'transaction_id': f'TXN_{n_normal+i+1:06d}', 'user_id': f'USER_{user_id:04d}', 'amount': round(amount, 2), 'merchant_category': np.random.choice(merchant_categories), 'location': np.random.choice(locations), # Fraud more likely foreign 'timestamp': datetime.now() - timedelta(days=np.random.randint(0, 365), hours=int(hour), minutes=np.random.randint(0, 60)), 'device_type': np.random.choice(device_types), 'user_age': np.random.randint(18, 80), 'account_age_days': np.random.randint(1, 365), # Newer accounts more risky 'transaction_count_24h': np.random.poisson(5), # More transactions 'avg_transaction_amount': round(np.random.lognormal(mean=4.0, sigma=1.2), 2), 'is_foreign_transaction': np.random.choice([0, 1], p=[0.3, 0.7]), # More foreign 'is_weekend': np.random.choice([0, 1], p=[0.6, 0.4]), 'hour_of_day': hour, 'is_fraud': 1 } data.append(transaction) # Create DataFrame df = pd.DataFrame(data) # Convert timestamp to datetime df['timestamp'] = pd.to_datetime(df['timestamp']) df = df.sort_values(['user_id', 'timestamp']).reset_index(drop=True) # Advanced Feature Engineering print("Generating advanced features...") # Time-based features df['day_of_week'] = df['timestamp'].dt.dayofweek df['month'] = df['timestamp'].dt.month df['is_month_end'] = (df['timestamp'].dt.day > 25).astype(int) df['is_month_start'] = (df['timestamp'].dt.day <= 5).astype(int) # Transaction velocity features df['time_since_last_transaction'] = df.groupby('user_id')['timestamp'].diff().dt.total_seconds() / 3600 # hours df['time_since_last_transaction'] = df['time_since_last_transaction'].fillna(24) # First transaction assumed 24h ago # Transaction count features (simplified - count previous transactions) df['transaction_count_7d'] = df.groupby('user_id').cumcount() + 1 df['transaction_count_30d'] = df.groupby('user_id').cumcount() + 1 # Amount-based features df['amount_zscore'] = df.groupby('user_id')['amount'].transform(lambda x: (x - x.mean()) / x.std() if x.std() > 0 else 0) df['amount_zscore'] = df['amount_zscore'].fillna(0) df['amount_percentile'] = df.groupby('user_id')['amount'].transform(lambda x: x.rank(pct=True)) df['amount_percentile'] = df['amount_percentile'].fillna(0.5) # Rolling statistics (using expanding window for simplicity) df['amount_rolling_mean_7d'] = df.groupby('user_id')['amount'].transform(lambda x: x.expanding(min_periods=1).mean()) df['amount_rolling_std_7d'] = df.groupby('user_id')['amount'].transform(lambda x: x.expanding(min_periods=1).std()) df['amount_rolling_std_7d'] = df['amount_rolling_std_7d'].fillna(0) # Location change indicator df['location_changed'] = (df.groupby('user_id')['location'].shift() != df['location']).astype(int) df['location_changed'] = df['location_changed'].fillna(0) # Device change indicator df['device_changed'] = (df.groupby('user_id')['device_type'].shift() != df['device_type']).astype(int) df['device_changed'] = df['device_changed'].fillna(0) # Merchant category change df['merchant_category_changed'] = (df.groupby('user_id')['merchant_category'].shift() != df['merchant_category']).astype(int) df['merchant_category_changed'] = df['merchant_category_changed'].fillna(0) # Risk score based on multiple factors df['risk_score'] = ( (df['is_foreign_transaction'] * 0.2) + (df['transaction_count_24h'] > 5).astype(int) * 0.15 + (df['amount'] > df.groupby('user_id')['amount'].transform('quantile', 0.95)).astype(int) * 0.2 + (df['account_age_days'] < 30).astype(int) * 0.15 + (df['hour_of_day'].isin([0, 1, 2, 3, 4, 5, 22, 23])).astype(int) * 0.1 + (df['time_since_last_transaction'] < 1).astype(int) * 0.1 + (df['location_changed'] * 0.1) ) # Interaction features df['amount_x_transaction_count'] = df['amount'] * df['transaction_count_24h'] df['amount_x_is_foreign'] = df['amount'] * df['is_foreign_transaction'] df['transaction_count_x_is_foreign'] = df['transaction_count_24h'] * df['is_foreign_transaction'] # Time patterns df['is_rush_hour'] = df['hour_of_day'].isin([7, 8, 9, 17, 18, 19]).astype(int) df['is_off_hours'] = df['hour_of_day'].isin([0, 1, 2, 3, 4, 5, 22, 23]).astype(int) # User behavior patterns df['avg_amount_ratio'] = df['amount'] / (df['avg_transaction_amount'] + 1) df['transaction_velocity'] = df['transaction_count_24h'] / (df['time_since_last_transaction'] + 0.1) # Fill NaN values df = df.fillna(0) # Shuffle the dataset df = df.sample(frac=1, random_state=42).reset_index(drop=True) return df if __name__ == "__main__": print("Generating fraud detection dataset...") print("Developer: Molla Samser") print("Website: https://rskworld.in") print("-" * 50) # Generate dataset df = generate_fraud_dataset(n_samples=10000, fraud_ratio=0.05) # Save to CSV df.to_csv('fraud_detection_dataset.csv', index=False) print(f"Dataset saved to fraud_detection_dataset.csv") print(f"Total transactions: {len(df)}") print(f"Fraudulent transactions: {df['is_fraud'].sum()}") print(f"Normal transactions: {(df['is_fraud'] == 0).sum()}") print(f"Fraud ratio: {df['is_fraud'].mean():.2%}") # Save to Excel (optional) try: df.to_excel('fraud_detection_dataset.xlsx', index=False) print(f"Dataset saved to fraud_detection_dataset.xlsx") except Exception as e: print(f"Note: Excel file not generated ({str(e)}). CSV file is available.") # Display sample print("\nSample data:") print(df.head(10)) print("\nDataset statistics:") print(df.describe())