graphday stockholm - fraud prevention
TRANSCRIPT
FraudPreventionApracticalexampleofgraphdatabasesinaction
A Graph Is Connected Data
Neo4j solves challenges for some of the most powerful companies in the world
Adidas uses Neo4j to combine content and product data into a single, searchable graph database which is used to create a personalized customer experience
“We have many different silos, many different data domains, and in order to make sense out of our data, we needed to bring those together and make them useful for us,” – Sokratis Kartelias, Adidas
eBay Now Tackles eCommerce Delivery Service Routing with Neo4j
“We needed to rebuild when growth and new features made our slowest query longer than our fastest delivery - 15 minutes! Neo4j gave us best solution” – Volker Pacher, eBay
Walmart uses Neo4j to give customer best web experience through relevant and personal recommendations
“As the current market leader in graph databases, and with enterprise features for scalability and availability, Neo4j is the right choice to meet our demands”. - Marcos Vada, Walmart
This shift is happening! Why?
Operational Databases
Applications
X
Pre-computed Queries (Data Warehouse / RDBMS)
Real-time & Dynamic Queries (Graph Database)
Why Graphs Now?
We need something that is:
• Flexible to change • Scalable to many problems • Intuitive to understand • Instantly responsive
Main Strengths
Main Risks
• Widespread competence and familiarity • Integration with wide range of tools
• “Analytical questions in transactional time” • Flexible and scalable to future needs
• New tools require new competence • Misunderstandings can result in tool
being wrongly applied
• Incapable of meeting flexibility and performance demands
• Lacks game changer success stories — status quo is risky in disruptive times
Ways of Thinking About DataPre-computed Queries
(Data Warehouse / RDBMS)Real-time & Dynamic Queries
(Graph Database)
A Graph Example: Fraud Prevention
FraudDetection&Prevention
TypesofFraud• RetailBankingFraud• InsuranceFraud
Identitytypes• Stolen• Fake• Synthetic
TypesofAnalysis• Discrete• Connected
RetailBankingFirst-PartyFraud
Openingmanylinesofcreditwithnointentionofpayingthemback
• TensofbillionsofdollarslosteveryyearbyU.S.Banks.(1)
• 25%oftotalconsumercreditcharge-offsintheUnitedStates.(2)
• 10%to20%ofunsecuredbaddebtatleadingU.S.andEuropeanbanksismisclassified,andisactuallyfirst-partyfraud.(3)
First-PartyFraudImpact
(1)Experian: http://www.experian.com/assets/decision-analytics/white-papers/first-partyfraud-wp.pdf(2)Experian: http://www.experian.com/assets/decision-analytics/white-papers/first-partyfraud-wp.pdf(3)BusinessInsider:http://www.businessinsider.com/how-to-use-social-networks-in-the-fight-against-first-party-fraud-2011-3
ThreeKindsofIdentities,FraudRings
145HickoryRd Pasadena,CA
415HickorySt Pasadena,CA
626-407-1234
626-814-6532
Gartner’sLayeredFraudPreventionApproach(4)
(4)http://www.gartner.com/newsroom/id/1695014
TraditionalFraudPrevention
Analysisofusersandtheirendpoints
Analysisof navigation
behaviorandsuspectpatterns
Analysisofanomaly
behaviorbychannel
Analysisofanomalybehavior
correlatedacrosschannels
Analysisofrelationshipstodetect
organizedcrimeandcollusion
Layer1
Endpoint- Centric
Navigation-Centric
Account- Centric
Cross-Channel
Entity Linking
Layer2 Layer3 Layer4 Layer5
DISCRETEDATAANALYSIS CONNECTEDANALYSIS
Pros SimpleStopsrookies
DiscreteDataAnalysis
Revolving Debt
INVESTIGATE
INVESTIGATE
Numberofaccounts
Cons Falsepositives Falsenegatives
ConnectedAnalysis
Revolving Debt
Numberofaccounts
PROSDetectfraudrings
Fewerfalsenegatives
ValueEffectiveindetectingsomeofthemostimpactfulattacks,evenfromorganizedrings
Challenge Extremelydifficultwithtraditionaltechnologies
Forexampleaten-personfraudbust-outis$1.5M,assuming100falseidentitiesand3financialinstrumentsperidentity,eachwitha$5Kcreditlimit
ConnectedAnalysiswithNeo4j
Demo
InsuranceFraud”WhiplashforCash”
PaperCollisionsInsurancescammersinventautomobileaccidentscompletewithfakedrivers,passengersandwitnesses
WhiplashforCashExample
Accidents
Cars
Doctor Attorney
People
DrivesIsPassenger
DriversPassengersWitnesses
Viewoffraudring inagraphdatabase
ModelingInsuranceFraudasaGraph
Accident1
Accident
2
Person
1
Person
2
Person
3
Person
4
Person
5
Person
6
Car
1
Car
2
Car
3
Car
4
INVOLVES
DRIVES
REPRESENTS
WITNESSES
ADJUSTS
HEALS
DoingConnectedAnalysisisChallenging
• Largeamountsofdataandrelationshipsmustbeprocessed
• Newdataandrelationshipsarecontinuallybeingadded
• Fraudringsmustbeuncoveredinreal-timetopreventfraud
Adopting a Pattern-oriented Mindset
Search-oriented
• Good when you know exactly what you’re looking for
• Primarily based on explicit search criteria
Pattern-oriented
• Good when you want to suggest what might fit
• Primarily based on implicit information, often many “hops” away
The Case For Innovation with Graphs
During past 20 years, society has become hyperconnected.
We considered how regular people tend to think and reason, and modeled Neo4j to match that.
Neo4j allows you to naturally map together the data that matters to you in a graph — like a mind map!
…
Graph structure scales to many problems, and is highly flexible to change.
…
Unlock the business value of connections and relationships in data.
…How can we discover insights hidden behind the complexity?
…Traditional technology does not handle complexity well.
…Information-wise, gone from a small town to a metropolis. Complexity has exploded.
…Need for better insights driven by competition and disruption. There is a shift happening now.
The world’s largest companies rely on Neo4j. The competitive advantage is real.
…
In uncertain times, many consider the risks of change, but what are the risks of not adapting?
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Get in the driver’s seat. Be the bringer of innovation.
…
The Problem The Solution The Future
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what a time to be alive!”})-[:FROM]->(you)