1. Prevention of money-laundering and fiscal fraud: the BRACCO project David Mitzman, InfoCamere, IT ECRF 2010 - Budapest, Hungary 14 June, 2010 JLS/2007/ISEC/431 30-CE-0220887/00-41

2. contents the BRACCO project the BRACCO software system: what it does and how the next steps

3. Intro to BRACCO BRACCO is a 2-year project, from December 2008 to December 2010 It is co-financed by the EC Directorate General Justice, Liberty and Security Partners: InfoCamere and Metaware (left the project in April)

4. Objectives of the project The project contributes to the fight against fiscal crime by developing a search & investigation system to collect, aggregate, process, analyse and visualise information extracted from different public sources. Such investigation is normally highly labour- and knowledge-intensive, requiring making sense out of a deluge of data.

5. Why should BRs get involved? AML is not (necessarily*) a Business Register service, but it is an important area of collaboration between administrations. * - some Registers are interested in monitoring their territories and all are interested in supporting thorough due diligence procedures.

6. the service context or market The data sources include public registers such as the Business Register, the Insolvency and defaulters databases, yearly accounts statements, as well as newspaper articles. Customer databases can also be integrated to provide additional relations for custom-tailored applications (Fiscal Police, Tax Authority, Stock Exchange Authority, Local Business Regulation Agencies)

7. the service context or market A first (simple) product of this type is being used by the Fiscal Police and was introduced in 2009 to all the Italian Chambers of Commerce (BRs). In 2010 it will be released as part of the standard Business Register access system.

8. A first (simple) product of this type is being used by the Fiscal Police and was introduced in 2009 into the Chamber of Commerce system. In 2010 it will be released as part of the standard Business Register access system. the DeVisu navigator

9. Techniques in fiscal crime detection and prevention Integrate and aggregate different data sources NLP for analysis of free-text information such as newspaper articles Data Visualisation – “social networks” Data Analytics - analyse networks for special features, to detect patterns or series of events Notify relevant authorities of events

10. Relationships Relations between companies and/or people: ownership, company roles, personal and professional relations or family ties, events such as transfer of shares, mergers, involvement in fiscal crimes and the related investigations

11. BRACCO software tools analysis of free-text data normalisation based on a model of the domain (rules and taxonomies governing the relationships - ontology) and based on a unique identification of subjects an inference engine to further analyse the relations and draw conclusions (further relations) graph analysis (“social network analysis”) and statistics on groups or clusters of companies visual navigation of relations

12. three main layers of components presentation layer processing & storage data sources

13. central knowledge data analysis BRACCO Ontology data access presentation layer data sources

14. abstract and concrete knowledge description of real world real-world facts and occurrences data analysis data access presentation layer data sources

15. structured and non-structured data Taxonomies & rules Relations graph data analysis DB adapters presentation layer Business Register Online Newspapers Natural Language Processing Private- client DBs WWW spiders

16. analysing and enriching the knowledge Taxonomies & rules Relations graph DB adapters presentation layer Business Register Online Newspapers Natural Language Processing Private- client DBs WWW spiders graph statistics warehouse inference engine statistical analysis graph analysis

17. outputs: navigation and reports Taxonomies & rules Relations graph (DB) DB connectors BRACCO Navigators (DeVisu, Redada, Soci-N) Business Register Online Newspapers Natural Language Processing Other PA DBs WWW graph statistics warehouse inference engine statistical analysis graph analysis Reports, dossier

18. REDADA navigator

19. investigating a person

20. inspecting relations and events

21. expanded node – F. Corona

22. inspecting a “sentimental relation”

23. Overview of crimes

24. Crimes tree

25. focus on location of activities

26. “Hotspots”

27. focus on recent activity

29. Business navigator

30. investigating InfoCamere

31. identifying InfoCamere group

32. What about Silvio Berlusconi?

33. ... and his group?

34. finding hidden relationships

35. GOTCHA!!

36. Berlusconi “tree” [not the entire “web” of relations]

37. Indirect control – Spadaccini group

38. Group Dossier– Aerofotogrammetrica Nazionale

39. The next steps  Further development of the data analysis techniques and statistics on groups  Application to nearby domains (Naples project on Public Tenders)  Validation with international shareholders data (Infogreffe); presentations and experiments (data exchange) with other BR’s

40. future steps  data reliability measures  unique identification of subjects  new, customer-supplied relations