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 potentiallyinterestingpeople/companiesrelated to financialcrime. They face several problems along the way:
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1. It is difficult to search across multiple publicly-available databases (UK Companies House, ICIJ Leaks, VK)
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2. 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.
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3. It is hard to prioritise which corporate ownership structures are more ‘risky’ than others
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4. It is hard to see the visualise corporate ownership with different risk signals
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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 criminalactivity. They face several problems along the way:
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1. It is difficult to search across multiple publicly-available databases (UK Companies House, Sanctionlists,ICIJ Leaks, VK)
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2. 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
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3. Thenumberofcorporatenetworksisoverwhelming,andsoit is hard to prioritise which corporate ownership structures are more ‘risky’ than others
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Corporate Risk Miner is a web app which evaluates different risk signatures of financial crime applied to the UK Companies House (UKCH) corporate ownership networks. These risk signatures include:
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* Cyclic ownership: (to explain.....)
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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:
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* Cyclic ownership: Circular company ownership (e.g. Company A owns Company B which owns Company C which owns Company A)
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* 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)
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* Links to tax havens: Corporate networks which involve companies/people associated with tax haven jurisdictions
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* Multi-jurisdictionness: Corporate networsk which span many jurisdictions
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* 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.
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* Links to tax havens: Corporate networks which involve companies/people associated with tax haven orsecrecyjurisdictions
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* 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.
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* Links to sanctioned entities: Official sanctioned people or companies, from sources such as the UN Sanctions List.
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* Links to politically-exposed persons (PEPs)
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* Links to disqualified directors
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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.
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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.
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## Installation
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- Any limitations of the current implementation of the tool
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- Motivation for design/architecture decisions
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### Limitations
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* Limited to cliques of ??? hop distance owing to space limitation
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* Cyclicity calculation assumes an undirected graph to save computational time. This could be improved by taking into account specific directions of ownership.
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* Entity resolution for company/people entities could be improved
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* Graph visualisation for large corporate networks can be too cluttered to be useful.
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### Potential next steps
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* Expand to corporate ownership databases outside of the UK, for example using OpenCorporates data.
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* Incorporate more external data sources identifying criminal or potentially-criminal activity for companies and people.
Peter Zatka-Haas
committed
2 years ago
1 parent ad223edc