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451 Corporate Risk Miner
Team Members
Elena Dulskyte 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 associations to criminal activity. They face several problems along the way:
- It is difficult to search across multiple publicly-available databases (UK Companies House, Sanction lists, 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
- The number of corporate networks is overwhelming, and so it is hard to prioritise which corporate ownership structures are more ‘risky’ than others
451 Corporate Risk Miner allows a user to navigate over different corporate ownership networks extracted from UK Companies House (UKCH) to identify and visualise those exhibiting risk signatures associated with financial crime. Example risk signatures include:
- Cyclic ownership: Circular company ownership (e.g. Company A owns Company B which owns Company C which owns Company A)
- 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 or secrecy jurisdictions
- Presence of proxy directors: Proxy directors are individual people who are registered as a company director on paper but who are likely never involved in the running of the business.
- 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. The app then computes a total risk score for each corporate network in UKCH, and outlines the details of the most high-risk networks. The user can export these network results as a .csv file for later viewing.
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
Limitations
- Limited to cliques of ??? hop distance owing to space limitation
- Cyclicity calculation assumes an undirected graph to save computational time. This could be improved by taking into account specific directions of ownership.
- Entity resolution for company/people entities could be improved
- Graph visualisation for large corporate networks can be too cluttered to be useful.
Potential next steps
- Expand to corporate ownership databases outside of the UK, for example using OpenCorporates data.
- Incorporate more external data sources identifying criminal or potentially-criminal activity for companies and people.