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!
All logos, trademarks, service marks and copyrights used in this presentation belong to their respective owners
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
Recommended
