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Fighting Financial Crime with
Connected Data
We help organisations get more value from their
Data
Training
Consulting
Deliver
Deliver
Build
Data
Solutions
Lean
Informa...
What is Financial Crime?
Fin
an
cia
e
Fraud
Cheque
Fraud
Mortgage
Fraud
Credit
Card
Fraud
Medical
Fraud
Corporate
Fraud
Securities
Fraud
Market
Man...
Things that may surprise you about Financial Crime
Its widespread
It’s increasing
Its invisible
Economically costly
Financ...
How have Financial Services firms
typically reacted to Financial Crime?
Several large fines > large
compliance budgets:
HS...
Financial Crime &
Connected Data
opportunities
2016 – a bumper year for financial
crime so far
Market
manipulation
Fraud & collusion
Sanctions evasion, money
laundering ...
Key drivers of solution choice
Requirements - The ‘connectedness’
characteristics of your data
Constraints
3 CD Financial ...
Case study 1:
Fraud rings Semantically
unambiguous
domain & mainly
structured sources
Timeliness &
performance
requirement...
Case study 2:
Panama
Papers
Evolving schema
as
understanding
evolves
Simplicity & time
to market key
drivers
No control ov...
More entities, more attributes, more
relationship types
Extract, Transform,
Load
Two interesting Panama portals
here today
RDF, SPARQL, linked to Geonames &
DBPedia Country resources
Property-graph, Cyph...
Case study 3:
Trade Finance
& paper based
products
Research & self
service more
important (red flag
investigation)
Timelin...
Financial Criminals are rational agents
Credit card
fraud
Trade
Finance
crimes
Examples of TF crimes:
- Dealing with sanct...
Leveraging semantics, linked data &
graphs
Sources
‘Graphy’ questions
Semantic
search &
Processing
Indexing Extraction
Ext...
Summary
Solution choice considerations – Requirements &
Constraints.
Questions?
Come and speak to us (we’re v. friendly…)
Fighting financial crime with connected data
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Fighting financial crime with connected data

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James Phare's slides from his talk at Connected Data London. James is a managing director of Data to Value, he presented what are the best ways to make use of this emerging Connected Data technology to better fight financial crime.

Published in: Technology
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Fighting financial crime with connected data

  1. 1. Fighting Financial Crime with Connected Data
  2. 2. We help organisations get more value from their Data Training Consulting Deliver Deliver Build Data Solutions Lean Information ManagementPowers
  3. 3. What is Financial Crime?
  4. 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. 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. 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. 7. Financial Crime & Connected Data opportunities
  8. 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. 9. Key drivers of solution choice Requirements - The ‘connectedness’ characteristics of your data Constraints 3 CD Financial Crime examples > different implementation technology choices
  10. 10. Case study 1: Fraud rings Semantically unambiguous domain & mainly structured sources Timeliness & performance requirements Control over sources & specific questions to ask
  11. 11. Case study 2: Panama Papers Evolving schema as understanding evolves Simplicity & time to market key drivers No control over sources & standards used > reverse engineering
  12. 12. More entities, more attributes, more relationship types Extract, Transform, Load
  13. 13. 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
  14. 14. 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
  15. 15. 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)
  16. 16. Leveraging semantics, linked data & graphs Sources ‘Graphy’ questions Semantic search & Processing Indexing Extraction Extensibility
  17. 17. Summary Solution choice considerations – Requirements & Constraints. Questions? Come and speak to us (we’re v. friendly…)

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