IMLMAS (Intelligent Money Laundering Multi-Agent System)

This repository contains the code used for the master thesis "Hiding Money Laundering with an Intelligent Multi-Agent System Simulation" from Ingeborg van Keulen. It heavily relies on AMLSim (see https://github.com/IBM/AMLSim).

Prerequisites

Ensure javac is set to 1.8 by running

javac -version

If you don't have any javac installed, you can install it with

sudo dnf install java-1.8.0-openjdk.x86_64
sudo dnf install java-1.8.0-openjdk-devel.x86_64

If the version is not set to 1.8, then run

sudo update-alternatives –config javac

You might also need to run chmod +x on run_simulation.

Installation instructions

1. Create a virtual environment (only tested with python 3.7), for example by running

conda create -n venv python=3.7

Conda is recommended for making your life easier with installing pygraphviz in the next steps. See https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html for installation instructions for conda.

2. Activate your virtual environment

conda activate venv

3. For some reason installing pygraphviz fails, which is solved by running

conda install pygraphviz

4. Install the requirements by running

pip install -r requirements.txt

5. Follow the instructions in the README in AMLSim/jars

Running instructions

In the directory toy_examples the four different models (Random, SVM, DQN and BDQN) scripts can be found. These scripts only work with some easy business rules that are defined in create_action_space.py in the same directory. You can change the used parameters and add/modify/delete business rules in these scripts. Each one of them can be run with python3 launder_as_much_<model>.py. If everything went okay, then you should see a directory setting_1000_<id> (check the path in the script to see where it should have written to).

In this directory you can find a plot, showing the learning behaviour (in case of BDQN this one does not make much sense since it includes all heads), a json file containing the parameters used in the setting, and a CSV file containing all the states-actions taken during the games. In case of BDQN the final weights of the heads are also stored.

For more complex business rules the file Game.py in the directory q_agent can be used. It is called with python3 Game.py, all the needed params need to be defined in the script itself. Note that you need to link a business rule engine (some code that detects transactions according to some logic) yourself.

Parameters

There are a lot of variables that can be changed. (like a lot lot)

• business rules can be changed in br_engine/business_rules.py, the days the rules should be applied to are defined in confs/engine_conf.json
• learning agent variables can be changed in confs/learning_conf.json
• simulation variables can be changed in confs/amlsim_conf.json, otherwise:
  • the AML agents in AMLSim/paramFiles/ml_agent/alertPatterns.csv
  • the normal agents in AMLSim/paramFiles/ml_agent/accounts.csv when is_aggregated_accounts=True, otherwise it should work in confs/amlsim_conf.json
  • the degrees in AMLSim/paramFiles/ml_agent/degree.csv. Haven't tested yet, but they possibly can be generated with the script AMLSim/scripts/generate_scalefree.py given the arguments num_vertices and edge_factor

See PARAMETERS.md for an extensive explanation of all parameters.