Fraud in Detection using neo4j

SAIYAM KOHLI

2669077

Agenda

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•

•

•

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• 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 HijackedDevices

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…

World of Fraud

Constantly Evolving Simple and Complex Few and Many Players

Digitized and Analog “One Step Ahead”

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 UnderlyingTechnology

Data Modelled as a Graph!

Graph

Database

Examples of Prevalent

Fraud Types

Fraud Rings

Endpoint-CentricAnalysis of users and

their end-points

1.

Navigation CentricAnalysis of

navigation behavior

and suspect

patterns

Account-CentricAnalysis of anomaly

behavior by channel

2. 3.

PC:s

Mobile Phones

IP-addresses

User ID:s

Comparing Transaction

Identity Vetting

Traditional Fraud Detection Methods

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•

•

•

•

•

Fraud rings

Fake IP-adresses

Hijacked devices

Synthetic Identities

Stolen Identities

And more…

Weaknesses

Unable to detect

DISCRETEANALYSIS

Endpoint-CentricAnalysis of users and

their end-points

1.

Navigation CentricAnalysis of

navigation behavior

and suspect

patterns

Account-CentricAnalysis of anomaly

behavior by channel

2. 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

CONNECTEDANALYSIS

Endpoint-CentricAnalysis of users and

their end-points

Navigation CentricAnalysis of

navigation behavior

and suspect

patterns

Account-CentricAnalysis of anomaly

behavior by channel

DISCRETEANALYSIS

1.

CrossChannel

Analysis of anomaly

behavior correlated

across channels

Entity Linking

Analysis of relationships

to detect organized

crime and collusion

2. 3. 4. 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

BANK

ACCOUNT

BANK

ACCOUNT

BANK

ACCOUNT

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

BANK

ACCOUNT

BANK

ACCOUNT

BANK

ACCOUNT

ADDRESS

PHONE

NUMBER

PHONE

NUMBER

SSN 2

UNSECURED

LOAN

SSN 2

UNSECURED

LOAN

Modeling a fraud ring as a graph

Credit Card Fraud

Example #1

“Credit Card Testing”

Manual skimming

of an ATM

Sophisticated Data Breaches

Retrieval of Credit Card Information

Rogue Merchant

USE

Terminal ATM-

skimming

Data Breach

Card Holder

Card Issuer

Fraudster

USE MAKES

$2

$5

$10

MAKES $4000

ATTesting

Merchants

ATMAKES Tx

Example #2

“Fraud Origination and

Assessing Loss Magnitude”

TxTx Tx TxTx Tx TxTxTx TxJohn

Tx

$2000

TxTx Tx TxTxTxTx Tx TxComputer

StoreJohn

Tx

$2000

Tx Tx

$25$10$4

TxTx Tx TxTxTxComputer

StoreJohn

Gas Station

Tx

$2000

Tx Tx

$25$10$4

TxTx Tx TxTxTxComputer

StoreJohn

Gas Station

Tx

$2

TxSheila TxTxTx Tx Tx Tx

$3000

TxJewelry

StoreTx

$3

Tx

$2000

Tx Tx

$25$10$4

TxTx Tx TxTxTxComputer

StoreJohn

Gas Station

Tx

$2

TxSheila TxTxTx Tx Tx Tx

$3000

TxJewelry

StoreTx

$3

Robert TxTxTx TxTx TxTxTx Tx Tx

TxTx

$2

Tx

Tx

$2000

Tx Tx

$25$10$4

TxTx Tx TxTxTxComputer

StoreJohn

Gas Station

Sheila

Robert

$3

Karen

TxTxTx Tx Tx Tx

$3000

TxJewelry

StoreTx

$3

TxTxTx TxTx TxTx

TxTx TxTx Tx TxTx

$8 $12

Tx

$1500

Furniture

Store

Tx Tx Tx

How Neo4j fits in

NEO4j

Money

Transferring

Purchases Bank

Services Relational

database

Data Science-teamDevelop Patterns

+ 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 MapReduce

+ Good for AnalyticalWorkloads

– No holistic view

– Non-operational workloads

– Weeks-to-months processes

Data Science-teamDevelop Patterns

Merchant Data

CreditScoreData

Other 3rd Party Data

Money

Transferring

Purchases Bank

Services

Neo4j powers

360° view of

transactions in

real-time

Neo4j

Cluster

SENSETransaction

stream

RESPONDAlerts & notification

LOAD RELEVANT DATA

Relational

database

Data Lake

Visualization UI

Fine Tune Patterns

Data Science-teamDevelop Patterns

Merchant Data

CreditScoreData

Other 3rd Party Data

Money

Transferring

Purchases Bank

Services

Neo4j powers

360° view of

transactions in

real-time

Neo4j

Cluster

SENSETransaction

stream

RESPONDAlerts & notification

LOAD RELEVANT DATA

Relational

database

Data Lake

Visualization UI

Fine Tune Patterns

Data Science-teamDevelop Patterns

Merchant Data

CreditScoreData

Other 3rd Party Data

Data-set used

to explore

new insights

THANK

YOU