Anti-Fraud and eDiscovery usingGraph Databases and GraphVisualizationCorey Lanum
We are hiring!
Corey Lanum• 10 years with i2 (now IBM), developing visualization andanalytical solutions for large government and enterpr...
FraudFraud consists of misrepresentation forpersonal financial gain– Personal Misrepresentation– Pretending to be someonee...
Fraud Detection• Why Graph Databases?– Almost all fraud cases involve the fabrication of a relationship, soit makes sense ...
Fraud Investigation• Once we have uncovered a fraudulenttransaction, how do we determine who isresponsibility, and provemi...
• 270 public and private sector organizations inthe UK are members of CIFAS• CIFAS maintains two large databases, one of a...
Neo4j and KeyLines
KeyLinesVisualise and analyse networks in the browser• Communication networks• Social networks• Fraud networksFeatures• Pu...
KeyLines / Neo Architecture
Credit Card Fraud Scenario• Employees of a retail merchant swipecustomers’ cards and steal data beforeprocessing transacti...
Insurance Fraud• A claim on an insurance policy that one isnot entitled to make– Staged auto accidents– Doctors billing fo...
eDiscovery• Similar to Fraud detection• Large volumes of transactional data – need tounderstand patterns in the data• Can’...
Costs of Fraud• Industry estimates are $2.5 Trillion peryear• By making it easier to both detect andinvestigate fraud, we ...
Thanks!corey@cambridge-intelligence.comAll logos, trademarks, service marks and copyrights used in thispresentation belong...
Roadmap• Larger and largernetworks– Filtering– Combining nodestogether– Improved analytics fornode importance– Faster rend...
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Anti-Fraud and eDiscovery using Graph Databases and Graph Visualization - Corey Lanum @ GraphConnect Boston 2013

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Investigating fraud often involves identifying suspicious patterns among mountains of uninteresting transactional data. A new partnership between Neo Technologies and Cambridge Intelligence allows fraud investigators and data analysts to uncover these patters far more easily. By combining the power of Neo4j's graph database and the visualization capabilities of KeyLines, a web-based graph visualization engine tightly integrated with Neo4j's data model, these investigators and analysts can visually drill down from aggregate data to the individual suspicious data elements quickly and without requiring significant technical expertise in query languages. This presentation will summarize the Neo Technology and Cambridge Intelligence partnership, discuss the technical integration between the two products, and demonstrate a number of different scenarios of uncovering fraud across multiple domains and data types.

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  • Bring up neo demo
  • This slide is to explain the main drivers for the features we are planning.The drivers are: large networks, dynamic, location info, real-time dashboards and a need for users to draw stuff ‘on top’ of the networks
  • Anti-Fraud and eDiscovery using Graph Databases and Graph Visualization - Corey Lanum @ GraphConnect Boston 2013

    1. 1. Anti-Fraud and eDiscovery usingGraph Databases and GraphVisualizationCorey Lanum
    2. 2. We are hiring!
    3. 3. Corey Lanum• 10 years with i2 (now IBM), developing visualization andanalytical solutions for large government and enterprisecustomers– Major insurance companies• Auto• Health– Government Agencies• RCMP• FBI• California Department of Justice
    4. 4. FraudFraud consists of misrepresentation forpersonal financial gain– Personal Misrepresentation– Pretending to be someoneelse to collect moneyintended for others– Transactional Misrepresentation– Fabricating details of atransaction to avoid scrutiny– Fabrication or exaggerationof insurance claims
    5. 5. Fraud Detection• Why Graph Databases?– Almost all fraud cases involve the fabrication of a relationship, soit makes sense to model your data to highlight relationships• Why Visualization?– Visualization of these relationships helps investigators andanalysts determine what patterns are normal, and which areabnormal, and flag the abnormal patterns for further scrutiny
    6. 6. Fraud Investigation• Once we have uncovered a fraudulenttransaction, how do we determine who isresponsibility, and provemisrepresentation?– Who had access?– Who benefited?– Did they work alone?
    7. 7. • 270 public and private sector organizations inthe UK are members of CIFAS• CIFAS maintains two large databases, one of allreported fraud instances and one for reportedstaff fraud• CIFAS has contracted to use KeyLines tovisualize connections between fraud instances
    8. 8. Neo4j and KeyLines
    9. 9. KeyLinesVisualise and analyse networks in the browser• Communication networks• Social networks• Fraud networksFeatures• 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
    10. 10. KeyLines / Neo Architecture
    11. 11. Credit Card Fraud Scenario• Employees of a retail merchant swipecustomers’ cards and steal data beforeprocessing transaction• Cardholders later notice fraudulentcharges on their bill• How do we walk back to determine who isresponsible?
    12. 12. Insurance Fraud• A claim on an insurance policy that one isnot entitled to make– Staged auto accidents– Doctors billing for services they neverperformed– Claiming pre-existing damage was caused bya covered event• Misrepresentation on the policy applicationto pay lower premiums
    13. 13. eDiscovery• Similar to Fraud detection• Large volumes of transactional data – need tounderstand patterns in the data• Can’t afford to pay lawyers to read every document• eDiscovery tools help to identify which documents orcommunications may be relevant by using a number ofalgorithms• Neo4j and Graph Visualization can help!
    14. 14. Costs of Fraud• Industry estimates are $2.5 Trillion peryear• By making it easier to both detect andinvestigate fraud, we reduce the incentivesto conduct fraud in the first place• Neo4j and KeyLines are perfecttechnologies to assist in this endevour
    15. 15. Thanks!corey@cambridge-intelligence.comAll logos, trademarks, service marks and copyrights used in thispresentation belong to their respective owners
    16. 16. Roadmap• Larger and largernetworks– Filtering– Combining nodestogether– Improved analytics fornode importance– Faster rendering (longterm)• 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

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