AMLNet

AMLNet Synthetic Anti-Money Laundering Transaction Dataset DESCRIPTION:This dataset contains over 1 million synthetic financial transactions (1,090,173) generated using the AMLNet framework for anti-money laundering research. CONTENTS:- 1,090,173 total transactions across multiple categories- 1,745 labeled money laundering transactions (~0.16%)- 195-day simulation period- AUSTRAC-compliant suspicious patterns DATA FORMAT:CSV file with 17 columns containing transaction details. CORE TRANSACTION DATA:- step: Sequential transaction step/ID- type: Payment method (TRANSFER, OSKO, BPAY, EFTPOS, DEBIT, NPP)- amount: Transaction amount in Australian Dollar - category: 11 Transaction categories (Education, Housing, Food, Healthcare, Transport, Recreation, Cryptocurrency, Property Investment, Shell Company, Utilities, Other)- nameOrig: Originating customer ID (e.g., C3511)- nameDest: Destination customer/merchant ID (e.g., C4945, M558)- oldbalanceOrg: Account balance before transaction- newbalanceOrig: Account balance after transaction LABELS:- isFraud: Binary fraud indicator (0=legitimate, 1=fraudulent)- isMoneyLaundering: Binary AML label (0=normal, 1=suspicious)- laundering_typology: Specific money laundering pattern type (structuring, layering, integration, normal)- fraud_probability: Calculated fraud risk score (may be empty for some transactions) TEMPORAL FEATURES:- hour: Hour of transaction (0-23)- day_of_week: Day of week (0=Sunday, 1=Monday, ..., 6=Saturday)- day_of_month: Day of month (1-31)- month: Month number (1-12) METADATA:- metadata: JSON object containing: * timestamp: Exact transaction datetime (datetime object) * location: Geographic information (city, state, country, postcode) * device_info: Device information (type: Mobile/Web/ATM, OS: Android/iOS/Windows/MacOS, IP address) * payment_method: Specific payment method (BSB_Account, CardNumber, PayID, etc.) * merchant_info: Merchant details (merchant_id, category, risk_level, avg_transaction) or None for P2P transactions * risk_indicators: Risk assessment metrics (amount_vs_average, customer_risk_score, category_risk, risk_score, unusual_time, unusual_location) * integration_info: Integration laundering details (type, legitimacy_score, detection_risk, location/sector, total_amount, num_sources, average_amount) * structuring: Structuring pattern details (sophistication, threshold_proximity, pattern_size) * layering: Detailed layering structure with layer information, splits, accounts, and time delays * layering_sophistication: Complexity level of layering operations * sophistication: Overall transaction sophistication level (low, medium, high) DATASET STATISTICS:- Total transactions: 1,090,173 (1M+)- Legitimate transactions: 1,088,428 (99.84%)- Money laundering transactions: 1,745 (0.16%)- Payment types: 8 different methods- Transaction categories: Multiple categories including financial services- Time period: 195-day simulation- Geographic coverage: Australian cities and postcodes USAGE:- Anti-money laundering research and algorithm development- Financial fraud detection benchmarking- Machine learning model training and validation- Academic research in financial crime detection- Educational purposes and student projects- Commercial Use: For commercial AML system development and testing, please contact [e-mail] for licensing permissions. Licensed under Creative Commons Attribution - Non Commercial 4.0 International License (CC BY-NC 4.0). Free to use for non-commercial purposes with proper attribution. See LICENSE.txt for full terms. CITATION:If you use this dataset, please cite: Huda, S., Foo, E., Jadidi, Z., Newton, M.A.H., & Sattar, A. (2025). AMLNet: A Knowledge-Based Multi-Agent Framework to Generate and Detect Realistic Money Laundering Transactions. Preprint Version v1, https://arxiv.org/abs/2509.11595. Under review in Expert Systems with Applications. Dataset: https://doi.org/10.5281/zenodo.16736515. CONTACT:[e-mail] VERSION: 1.0DATE: August 2025 **Supersedes deprecated earlier version (DOI: https://doi.org/10.5281/zenodo.16482144).