Please cite our following paper if you use the data set for your publications.
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs https://arxiv.org/abs/1902.10191
Scalable Graph Learning for Anti-Money Laundering: A First Look https://arxiv.org/abs/1812.00076
Important: Please use the "master" branch for the practical use and testing. Other branches such as "new-schema" are outdated and unstable. Wiki pages are still under construction and some of them do not catch up with the latest implementations. Please refer this README.md instead.
This project aims at building a multi-agent simulator of anti-money laundering - namely AML, and sharing synthetically generated data so that researchers can design and implement their new algorithms over the unified data.
jars
directory: See also jars/README.md
)
pip3 install -r requirements.txt
)
See Wiki page Directory Structure for details.
See Wiki page Quick Introduction to AMLSim for details.
Before running the Python script, please check and edit configuration file conf.json
.
{
//...
"input": {
"directory": "paramFiles/1K", // Parameter directory
"schema": "schema.json", // Configuration file of output CSV schema
"accounts": "accounts.csv", // Account list parameter file
"alert_patterns": "alertPatterns.csv", // Alert list parameter file
"degree": "degree.csv", // Degree sequence parameter file
"transaction_type": "transactionType.csv", // Transaction type list file
"is_aggregated_accounts": true // Whether the account list represents aggregated (true) or raw (false) accounts
},
//...
}
Then, please run transaction graph generator script.
cd /path/to/AMLSim
python3 scripts/transaction_graph_generator.py conf.json
Parameters for the simulator are defined at the "general" section of conf.json
.
{
"general": {
"random_seed": 0, // Seed of random number
"simulation_name": "sample", // Simulation name (identifier)
"total_steps": 720, // Total simulation steps
"base_date": "2017-01-01" // The date corresponds to the step 0 (the beginning date of this simulation)
},
//...
}
Please compile Java files (if not yet) and launch the simulator.
sh scripts/build_AMLSim.sh
sh scripts/run_AMLSim.sh conf.json
The file names of the output data are defined at the "output" section of conf.json
.
{
//...
"output": {
"directory": "outputs", // Output directory
"accounts": "accounts.csv", // Account list CSV
"transactions": "transactions.csv", // All transaction list CSV
"cash_transactions": "cash_tx.csv", // Cash transaction list CSV
"alert_members": "alert_accounts.csv", // Alerted account list CSV
"alert_transactions": "alert_transactions.csv", // Alerted transaction list CSV
"sar_accounts": "sar_accounts.csv", // SAR account list CSV
"party_individuals": "individuals-bulkload.csv",
"party_organizations": "organizations-bulkload.csv",
"account_mapping": "accountMapping.csv",
"resolved_entities": "resolvedentities.csv",
"transaction_log": "tx_log.csv",
"counter_log": "tx_count.csv",
"diameter_log": "diameter.csv"
},
//...
}
python3 scripts/convert_logs.py conf.json
python3 scripts/visualize/plot_distributions.py conf.json
python3 scripts/validation/validate_alerts.py conf.json
outputs
directory and a temporal directorysh scripts/clean_logs.sh