Webinar: Stop Complex Fraud in its Tracks with Neo4j
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Stop Complex Fraud in its Tracks with Neo4j
Neo4j Webinar, March 29, 2017
Ryan BoydDeveloper Relations @ Neo4j
Nav Mathur Sr. Director Global Solutions @ Neo4j
Alessandro SvenssonSolutions Marketing @ Neo4j
Agenda• Who are Today’s Fraudsters • How to Fight Fraud Rings with Graphs • Different Types of Credit Card Fraud & Neo4j Demo • How Neo4j Fits in a Typical Architecture • Summary • Q&A
Who Are Today’s Fraudsters?
Organized in groups Synthetic Identities Stolen Identities Hijacked Devices
Who Are Today’s Fraudsters?
Types of Fraud• Credit Card Fraud• Rogue Merchants• Fraud Rings• Insurance Fraud• eCommerce Fraud• Fraud we don’t know about yet…
Digitized and Analog
World of Fraud
Constantly Evolving Few and Many Players
“One Step Ahead”
Simple and Complex
Fraud Detection(From a data-modeling perspective)
Raw Data
Anomalies
Anomalies hidden in “normal behavior”
Patterns
Patterns
1) Detect 2) Respond
Fraud Prevention is About Reacting to Patterns(And doing it fast!)
Relational Database
Choosing Underlying Technology
Data Modelled as a Graph!
Graph Database
Examples of Prevalent Fraud Types
Fraud Rings
“Don’t consider traditional technology adequate to keep
up with criminal trends”
Market Guide for Online Fraud Detection, April 27, 2015
Endpoint-CentricAnalysis of users and their end-points
1.
Navigation CentricAnalysis of navigation behavior and suspect patterns
2.
Account-CentricAnalysis of anomaly behavior by channel
3.
PC:sMobile Phones
IP-addressesUser ID:s
Comparing TransactionIdentity Vetting
Traditional Fraud Detection Methods
Unable to detect • Fraud rings • Fake IP-adresses • Hijacked devices • Synthetic Identities • Stolen Identities • And more…
Weaknesses
DISCRETE ANALYSIS
Endpoint-CentricAnalysis of users and their end-points
1.
Navigation CentricAnalysis of navigation behavior and suspect patterns
2.
Account-CentricAnalysis of anomaly behavior by channel
3.
Traditional Fraud Detection Methods
INVESTIGATE
Revolving Debt
Number of Accounts
INVESTIGATE
Normal behavior
Fraud Detection with Discrete Analysis
Revolving Debt
Number of Accounts
Normal behavior
Fraudulent pattern
Fraud Detection with Connected Analysis
CONNECTED ANALYSIS
Endpoint-CentricAnalysis of users and their end-points
Navigation CentricAnalysis of navigation behavior and suspect patterns
Account-CentricAnalysis of anomaly behavior by channel
DISCRETE ANALYSIS
1. 2. 3.
Cross ChannelAnalysis of anomaly behavior correlated across channels
4.
Entity LinkingAnalysis of relationships to detect organized crime and collusion
5.
Augmented Fraud Detection
ACCOUNT HOLDER 2
Modeling a fraud ring as a graph
ACCOUNT HOLDER 1
ACCOUNT HOLDER 3
ACCOUNT HOLDER 2
ACCOUNT HOLDER 1
ACCOUNT HOLDER 3
CREDIT CARD
BANKACCOUNT
BANKACCOUNT
BANKACCOUNT
PHONE NUMBER
UNSECURED LOAN
SSN 2
UNSECURED LOAN
Modeling a fraud ring as a graph
ACCOUNT HOLDER 2
ACCOUNT HOLDER 1
ACCOUNT HOLDER 3
CREDIT CARD
BANKACCOUNT
BANKACCOUNT
BANKACCOUNT
ADDRESS
PHONE NUMBER
PHONE NUMBER
SSN 2
UNSECURED LOAN
SSN 2
UNSECURED LOAN
Modeling a fraud ring as a graph
Credit Card Fraud
Ryan BoydDeveloper Relations @ Neo4j
Nav Mathur Sr. Director Global Solutions @ Neo4j
Example #1 “Credit Card Testing”
Manual skimming of an ATM
Sophisticated Data Breaches
Retrieval of Credit Card Information
Rogue Merchant
USE
ISSUES
Terminal ATM-skimming
Data Breach
Card Holder
Card Issuer
Fraudster
USE $5MAKES
$10
MAKES
$2MAKES
MAKES $4000
ATTesting
Merchants
ATMAKES Tx
Example #2 “Fraud Origination and
Assessing Loss Magnitude”
TxTx Tx TxTx Tx Tx TxTxTx TxJohn
Tx
$2000
TxTx Tx Tx TxTxTxTx Tx TxComputer
StoreJohn
Tx
$2000
Tx Tx
$25$10$4
TxTx Tx Tx TxTxTxComputer
StoreJohn
Gas Station
Tx
Tx
$2000
Tx Tx
$25$10$4
TxTx Tx Tx TxTxTxComputer
StoreJohn
Gas Station
Sheila Tx
$2
TxTxSheila TxTxTx Tx Tx TxTx
$3000
TxJewelry
StoreTx
$3
Tx
Tx
$2000
Tx Tx
$25$10$4
TxTx Tx Tx TxTxTxComputer
StoreJohn
Gas Station
Sheila Tx
$2
TxTxSheila TxTxTx Tx Tx TxTx
$3000
TxJewelry
StoreTx
$3
Robert TxTxTx Tx TxTx TxTxTx Tx Tx
TxTx
$2
TxTx
Tx
$2000
Tx Tx
$25$10$4
TxTx Tx Tx TxTxTxComputer
StoreJohn
Gas Station
Sheila
Robert
$3
Karen
TxTxTx Tx Tx TxTx
$3000
TxJewelry
StoreTx
$3
TxTxTx Tx Tx TxTx TxTx
TxTx TxTx Tx Tx TxTx
$8 $12
Tx
$1500
Furniture Store
Tx Tx Tx
How Neo4j fits in
Money Transferring
Purchases Bank Services Relational
database
Develop PatternsData Science-team
+ Good for Discrete Analysis– No Holistic View of Data-Relationships– Slow query speed for connections
Money Transferring
Purchases Bank Services Relational
database
Data Lake
+ Good for Map Reduce+ Good for Analytical Workloads– No holistic view– Non-operational workloads– Weeks-to-months processes Develop Patterns
Data Science-team
Merchant Data
Credit Score Data
Other 3rd Party Data
Money Transferring
Purchases Bank Services
Neo4j powers360° view of
transactions in real-time
Neo4j Cluster
SENSETransaction
stream
RESPONDAlerts & notification
LOAD RELEVANT DATA
Relational database
Data Lake
Visualization UI Fine Tune Patterns
Develop PatternsData Science-team
Merchant Data
Credit Score Data
Other 3rd Party Data
Money Transferring
Purchases Bank Services
Neo4j powers360° view of
transactions in real-time
Neo4j Cluster
SENSETransaction
stream
RESPONDAlerts & notification
LOAD RELEVANT DATA
Relational database
Data Lake
Visualization UI Fine Tune Patterns
Develop PatternsData Science-team
Merchant Data
Credit Score Data
Other 3rd Party Data
Data-set used to explore
new insights
Summary
We talked about…Today’s Fraudsters
Examples of different types of Fraud:Fraud Rings
Credit Card Testing Fraud Origination
How Neo4j Fits in an Architecture
Detect & prevent fraud in real-time Faster credit risk analysis and transactions Reduce chargebacks Quickly adapt to new methods of fraud
Why Neo4j? Who’s using it?Financial institutions use Neo4j to:
FINANCE Government Online Retail
Valuable Resources!
neo4jsandbox.com https://neo4j.com/use-cases/fraud-detection/ neo4j.com/product
Sandbox Fraud Detection Product
Q&A
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