Fighting financial crime with connected data
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The document discusses the growing issue of financial crime and the need for organizations to leverage connected data for better solutions. Various types of financial crimes are highlighted, along with examples of how financial services have responded, often through increased compliance budgets and technology investments. It also presents case studies demonstrating the importance of data connectedness and technology choices in tackling financial crime effectively.
Fighting financial crime with connected data
- 1. Fighting Financial Crime with Connected Data
- 2. We help organisations get more value from their Data Training Consulting Deliver Deliver Build Data Solutions Lean Information ManagementPowers
- 3. What is Financial Crime?
- 4. Fin an cia e Fraud Cheque Fraud Mortgage Fraud Credit Card Fraud Medical Fraud Corporate Fraud Securities Fraud Market Manipulation Payment Fraud Theft Identity Theft Tax Evasion Embezzle ment Bribery Money Laundering Forgery & Counterfeiting Cybercrime Elder Abuse Violent Crime Financial Crime Embezzlement Related to
- 5. Things that may surprise you about Financial Crime Its widespread It’s increasing Its invisible Economically costly Financial criminals are transnational, rational, organised and highly data & IT literate Its industrial in terms of economies of scale
- 6. How have Financial Services firms typically reacted to Financial Crime? Several large fines > large compliance budgets: HSBC $1.9bn Standard Chartered $1bn BNP Paribas $8.8bn Typical responses: Hired more compliance people Initiated large regulatory change initiatives Data & Technology investments
- 7. Financial Crime & Connected Data opportunities
- 8. 2016 – a bumper year for financial crime so far Market manipulation Fraud & collusion Sanctions evasion, money laundering & tax evasion Theft Insurance Fraud Trade Finance Minimal ‘Connected Dataness’ Highly connected
- 9. Key drivers of solution choice Requirements - The ‘connectedness’ characteristics of your data Constraints 3 CD Financial Crime examples > different implementation technology choices
- 10. Case study 1: Fraud rings Semantically unambiguous domain & mainly structured sources Timeliness & performance requirements Control over sources & specific questions to ask
- 12. Case study 2: Panama Papers Evolving schema as understanding evolves Simplicity & time to market key drivers No control over sources & standards used > reverse engineering
- 13. More entities, more attributes, more relationship types Extract, Transform, Load
- 14. Two interesting Panama portals here today RDF, SPARQL, linked to Geonames & DBPedia Country resources Property-graph, Cypher, soft link to Open Corporates http://data.ontotext.com/ https://offshoreleaks.icij.org
- 15. Case study 3: Trade Finance & paper based products Research & self service more important (red flag investigation) Timeliness less important than interoperability & standards Control over sources > forward engineering & opportunities for standards & schemas
- 16. Financial Criminals are rational agents Credit card fraud Trade Finance crimes Examples of TF crimes: - Dealing with sanctioned countries– e.g. Russia - Dealing with sanctioned companies, ships or shipping agents - Dealing with sanctioned individuals e.g. terrorists - Money Laundering - Dealing in Dual Use goods (nuclear, military, chemical)
- 17. Leveraging semantics, linked data & graphs Sources ‘Graphy’ questions Semantic search & Processing Indexing Extraction Extensibility
- 18. Summary Solution choice considerations – Requirements & Constraints. Questions? Come and speak to us (we’re v. friendly…)