3. The number of corporate networks is overwhelming, and so it is hard to prioritise which corporate ownership structures are more ‘risky’ than others
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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: Circularcompanyownership (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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* Cyclic ownership: measureofnetworkinterconnectedness (e.g. Company A owns Company B which owns Company C which owns Company A,orcasewhenthesamepeopledirectmultiplecompanies.)
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* Links to tax havens: Corporate networks which involve companies/people associated with tax haven or secrecy jurisdictions
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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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* Presence of proxy directors: Proxy directors are entities that have links to more than 50 companies.
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* Links to politically-exposed persons (PEPs)
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* Links to disqualified directors
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* Links to russian politicians
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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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## Usage
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TBD
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- Access the app online.
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Data cache can either be:
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- Downloaded from <a href="https://drive.google.com/drive/folders/15I2-spww_5ZG6tzslLyU4gGwb3zDNZU-?usp=sharing" target="_blank">GoogleDrive.</a>
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(Then unzipped and stored in `/data` folder.)
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- Recomputed with fresh UK Company House Downloads. Follow README and notebook in `/data_cache/` folder.
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## Additional Information
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In this project we used UK Company House Datasets. All information regarding the dataset, input schema and data processing can be found in [data_cache](https://github.com/sahanmar/451/tree/main/data_cache).
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#### Data enrichment
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The original UKCH data did not provide niethersanctionednorpep information. Hence, the data were enriched with the additional information from the publicly available external datasets. We have scraped [UN sanctions](https://www.un.org/securitycouncil/content/un-sc-consolidated-list), [Russian and Belorussian PEPs](https://rupep.org/en/persons_list/) and [all politicians dataset](https://raw.githubusercontent.com/everypolitician/everypolitician-data/master/countries.json). The scrapers, parsers and README can be found in [sanctions_and_peps](https://github.com/sahanmar/451/tree/main/sanctions_and_peps) directory.
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In the final version of the app, UNandAllpoliticians were used.
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The original UKCH data did not provide pep information. Hence, the data wes enriched with the additional information from the publicly available external datasets. We have scraped [UN sanctions](https://www.un.org/securitycouncil/content/un-sc-consolidated-list), [Russian and Belorussian PEPs](https://rupep.org/en/persons_list/) and [all politicians dataset](https://raw.githubusercontent.com/everypolitician/everypolitician-data/master/countries.json). The scrapers, parsers and README can be found in [sanctions_and_peps](https://github.com/sahanmar/451/tree/main/sanctions_and_peps) directory.
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In the final version of the app, Russianrupep.organdEveryPolitician.org were used.
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### Limitations
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* Limited to cliques of ??? hop distance owing to space limitation
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* If a user wants to refresh the cached data with the latest UKCH datasets, it would need to be downloaded from UKCH company house and formatted as per data_schema/README instructions.
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* Limited to neighbourhood of 2 hop distance, when network is parto of a Giant Ownership component.
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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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* Entity resolution for company/people entities could be improved.Currentlylinkingisdoneonname+yob+mob.
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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.
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* Add an ability to filter based on a custom list of people/companies, explain.......
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* Allow user to input custom lists as additional risk flags.
ElenaDulskyte
committed
with
GitHub
2 years ago