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Please cite our following papers if you use the data set for your publications.

BibTeX @misc{AMLSim, author = {Toyotaro Suzumura and Hiroki Kanezashi}, title = {{Anti-Money Laundering Datasets}: {InPlusLab} Anti-Money Laundering DataDatasets}, howpublished = {\url{http://github.com/IBM/AMLSim/}}, year = 2021 }

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 Other branches such as "new-schema" are outdated and unstable.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.

AMLSim

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.

Dependencies

Directory Structure

See Wiki page Directory Structure for details.
NOTE: (October 2021): bin/ folder has been renamed to target/classes/

Introduction for Running AMLSim

See Wiki page Quick Introduction to AMLSim for details.

1. Generate transaction CSV files from parameter files (Python)

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

2. Build and launch the transaction simulator (Java)

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

2.b. Optional: Install and Use Maven as build system.

On Mac: brew install maven If you already have a java installed, you can run brew uninstall --ignore-dependencies openjdk because brew installs that along with maven as a dependency.

If you choose to use Maven, you only manually need to fetch and place 1 jar file (MASON) in your jars/ folder and then install it using the command shown below. If you do not use Maven, you will have to place all the dependency jar files listed above as dependencies in the jars/ folder.
If using Maven, use the following commands to install the MASON dependency to your local Maven repository.

mvn install:install-file \
-Dfile=jars/mason.20.jar \
-DgroupId=mason \
-DartifactId=mason \
-Dversion=20 \
-Dpackaging=jar \
-DgeneratePom=true

Please compile Java files (if not yet) (will detect and use Maven) and launch the simulator.

sh scripts/build_AMLSim.sh
sh scripts/run_AMLSim.sh conf.json

3. Convert the raw transaction log file

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

4. Export statistical information of the output data to image files (optional)

python3 scripts/visualize/plot_distributions.py conf.json

5. Validate alert transaction subgraphs by comparison with the parameter file (optional)

python3 scripts/validation/validate_alerts.py conf.json

6. Remove all log and generated image files from outputs directory and a temporal directory

sh scripts/clean_logs.sh