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.

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…)