CashWash

2018 will remain in history as the year in which the puzzle of populist souverainism started coming together in a big network of illicit cash flows. Having experience in crime prevention and bank transactions, at the end of December 2018 I got hold of my first money laundering data set. The result is an open source project I called CashWash and this is its code repository.

This project is using data from:

• the Azerbaijani Laundromat investigation by OCCRP and Berlingske, available under the CreativeCommons Attribution 4.0 license.
• OpenCorporates under their CreativeCommons Atrribution-ShareAlike 3.0 licence.
• Bank.codes.
• Wikipedia available under Creative Commons Attribution-ShareAlike 3.0 license.
• FontAwesome under CreativeCommons Attribution 4.0 according to their free license.

No data from any of these data sources is published here. The platform allows you to provide your own API keys and extract any data yourself. To this end consider requesting your own license and adding it as indicated in the private.py file.

There are currently several alternative versions supported, using different web frameworks:

• flask
• bottle
• fastapi

The version you want would determine the dependencies you want to install. If unsure, I suggest going for bottle.

Installation and configuration

This project requires python3 and a SQLite server.

To install all dependencies, run:

pip3 install -r requirements.txt

To install only one specific web framework you need one of these:

pip3 install bottle sqlalchemy requests pyquery confusable_homoglyphs

pip3 install flask sqlalchemy requests pyquery confusable_homoglyphs

pip3 install fastapi sqlalchemy requests pyquery confusable_homoglyphs

Once project and dependencies downloaded, request your private keys for the external data sources, as indicated. These will give you access to the data sources, they will not extract the data. Data itself is fetched on-demand.

Finally, you might wish to review your settings, but the defaults should work fine.

Use

To Initialize the database, run

python3 import_laundromat.py

Then, running the server is simple, for bottle (recommended) just use:

python3 app_bottle.py

Or alternatively:

python3 app_flask.py

python3 app_fastapi.py