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451 Corporate Risk Miner
Team Members
Elena Dulskyte linkedin Marko Sahan github linkedin Peter Zatka-Haas github linkedin
Tool Description
Financial crime journalists need to dig through complex corporate ownership databases (i.e. databases of companies and the people/companies that control those companies) in order to find potentially interesting people/companies related to financial crime. They face several problems along the way:
- It is difficult to search across multiple publicly-available databases (UK Companies House, ICIJ Leaks, VK)
- There are multiple ‘risk signatures’ associated with criminal activity (e.g. Cyclical or long-chain ownership, links to sanctions, etc) and different journalists prioritise different kinds of signatures in their investigation.
- It is hard to prioritise which corporate ownership structures are more ‘risky’ than others
- It is hard to see the visualise corporate ownership with different risk signals
Corporate Risk Miner is a web app which allows users to evaluate different risk signatures of financial crime applied to the UK Companies House (UKCH) corporate ownership database. These risk signatures include:
- Cyclic ownership: (to explain.....)
- Long-chain ownership: Long chains of corporate ownership (e.g. Person A controls company A. Company A is an officer for Company B. Company B is an officer of company C. etc)
- Links to tax havens: Corporate networks which involve companies/people associated with tax haven jurisdictions
- Multi-jurisdictionness: Corporate networsk which span many jurisdictions
- Presence of proxy directors: Proxy directors are individual people who are registered as a company director but who are likely never involved in the running of the business. These people are often directors for many companies.
- Links to sanctioned entities: Official sanctioned people or companies, from sources such as the UN Sanctions List.
- Links to politically-exposed persons (PEPs)
- Links to disqualified directors
The user can customise the relative 'importance' of each risk signature for their search. For example one user may rate 'cyclic ownership' as a less important feature than 'association with tax havens' in flagging up potentially dodgy corporate networks. One the user chooses their signature preferences, the app generates a risk score associated with each corporate network and displays the structure of those networks with the highest risk scores.
Installation
-
Make sure you have Python version 3.8 or greater installed
-
Download the tool's repository using the command:
git clone https://github.com/sahanmar/451
- Move to the tool's directory and install the tool
cd 451
pip install -r requirements.txt
- Start the streamlit app
streamlit run app/app.py
- On your web browser, load http://localhost:8501
Usage
TBD
Additional Information
This section includes any additional information that you want to mention about the tool, including:
- Potential next steps for the tool (i.e. what you would implement if you had more time)
- Any limitations of the current implementation of the tool
- Motivation for design/architecture decisions