Anti-Fraud and eDiscovery using Graph Databases and Graph Visualization

Corey Lanum

We are hiring!

Corey Lanum• 10 years with i2 (now IBM), developing

visualization and analytical solutions for large government and enterprise customers– Major insurance companies

• Auto• Health

– Government Agencies• RCMP• FBI• California Department of Justice

FraudFraud consists of misrepresentation

for personal financial gain

– Personal Misrepresentation – Pretending to be

someone else to collect money intended for others

– Transactional Misrepresentation

– Fabricating details of a transaction to avoid scrutiny

– Fabrication or exaggeration of insurance claims

Fraud Detection• Why Graph Databases?

– Almost all fraud cases involve the fabrication of a relationship, so it makes sense to model your data to highlight relationships

• Why Visualization?– Visualization of these relationships helps investigators

and analysts determine what patterns are normal, and which are abnormal, and flag the abnormal patterns for further scrutiny

Fraud Investigation

• Once we have uncovered a fraudulent transaction, how do we determine who is responsibility, and prove misrepresentation?– Who had access?– Who benefited?– Did they work alone?

• 270 public and private sector organizations in the UK are members of CIFAS

• CIFAS maintains two large databases, one of all reported fraud instances and one for reported staff fraud

• CIFAS has contracted to use KeyLines to visualize connections between fraud instances

Neo4j and KeyLines

KeyLines

Visualise and analyse networks in the browser• Communication networks• Social networks• Fraud networks

Features• Pure HTML5• Works on IE6, 7, 8 via Flash• Graph layouts• Graph analytics

– SNA measures, path finding & more• Full event model• Full workflow support

– Image generation for reports, undo stack, etc

• Very quick integration time

• Thorough documentation• Good performance• Great support

KeyLines / Neo Architecture

Credit Card Fraud Scenario

• Employees of a retail merchant swipe customers’ cards and steal data before processing transaction

• Cardholders later notice fraudulent charges on their bill

• How do we walk back to determine who is responsible?

Insurance Fraud

• A claim on an insurance policy that one is not entitled to make– Staged auto accidents– Doctors billing for services they never

performed– Claiming pre-existing damage was

caused by a covered event

• Misrepresentation on the policy application to pay lower premiums

eDiscovery• Similar to Fraud detection• Large volumes of transactional data – need to

understand patterns in the data• Can’t afford to pay lawyers to read every

document• eDiscovery tools help to identify which

documents or communications may be relevant by using a number of algorithms

• Neo4j and Graph Visualization can help!

Costs of Fraud

• Industry estimates are $2.5 Trillion per year

• By making it easier to both detect and investigate fraud, we reduce the incentives to conduct fraud in the first place

• Neo4j and KeyLines are perfect technologies to assist in this endevour

Thanks!

corey@cambridge-intelligence.com

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Roadmap• Larger and larger

networks– Filtering– Combining nodes

together– Improved analytics for

node importance– Faster rendering (long

term)

• Dynamic networks– Filtering– Timeline, time slider

• Location information– Map underlays– Geographic node

layout

• Real time networks– Visual activity

indicators

• Information synthesis – Shapes, boxes,

attributes for annotation

– Snap to grid– Elbows on links