Fighting Financial Crime with Connected Data

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What is Financial Crime?

Financiae

Fraud

Cheque Fraud

Mortgage Fraud

Credit Card Fraud

Medical Fraud

Corporate

Fraud

Securities Fraud

Market Manipulation

Payment Fraud

Theft

Identity Theft

Tax Evasion

Embezzlement

Bribery

Money Laundering

Forgery & Counterfeiting

Cybercrime

Elder Abuse

Violent Crime Financial Crime

Embezzlement

Related to

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

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

Financial Crime & Connected Data

opportunities

2016 – a bumper year for financial crime so far

Market manipulation

Fraud & collusionSanctions evasion, money laundering & tax evasion

Theft

Insurance Fraud

Trade Finance

Minimal ‘Connected Dataness’

Highly connected

Key drivers of solution choice

Requirements - The ‘connectedness’ characteristics of your data

Constraints

3 CD Financial Crime examples > different implementation technology choices

Case study 1: Fraud rings Semantically

unambiguous domain & mainly structured sources

Timeliness & performance requirements

Control over sources & specific questions to ask

Case study 2: Panama Papers

Evolving schema as understanding evolves

Simplicity & time to market key drivers

No control over sources & standards used > reverse engineering

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, Cypher, soft link to Open Corporates

http://data.ontotext.com/ https://offshoreleaks.icij.org

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

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)

Leveraging semantics, linked data & graphs

Sources‘Graphy’ questions

Semantic search &

Processing

Indexing Extraction

Extensibility

Summary

Solution choice considerations – Requirements & Constraints.

Questions?

Come and speak to us (we’re v. friendly…)