Graphs in Action

Driving Digital Transformation With Neo4jGRAPHS IN ACTION

Atlanta, Sept 8, 2016

Social networks RetailHR & Recruiting

Manufacturing & Logistics

Health Care Telco

Today we see graph-projects in virtually every industry

Finance

Retail

NEO4j solves retail-related challenges for some of the largest 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

End ConsumersComponent Manufacturers

Logistics

Traditional Retail Value ChainRetailersWholesalersAssembly

Plants

PAYMENTSSALES- CHANNELS

SUPPLY CHAIN

PRODUCTS MARKETING

CRM

CUSTOMER EXPERIENCE

THE ONLINE RETAIL VALUE

CHAIN

PAYMENTSSALES-CHANNELS

SUPPLY CHAIN

PRODUCTS MARKETING

CRM

CUSTOMER EXPERIENCEStore

Mobile

Webstore


SUPPLY CHAIN

PRODUCTS MARKETING

CRM


Mobile

Shipping

Inventory

Express goods

Home delivery

Webstore


SUPPLY CHAIN

PRODUCTS MARKETING

CRM


Mobile

Shipping

Inventory

Express goods

Home delivery RatingsPrice-range

Category

Webstore


SUPPLY CHAIN

PRODUCTS MARKETING

CRM


Mobile

Shipping

Inventory

Express goods


Category ContentPromotions

Online advertising

Webstore


SUPPLY CHAIN

PRODUCTS MARKETING

CRM


Mobile

Shipping

Inventory

Express goods



Online advertising

Loyalty Programs

Returns

Feedback

reviews

Tweets

Emails

Customer support

Webstore


SUPPLY CHAIN

PRODUCTS MARKETING

CRM


Mobile

Shipping

Inventory

Express goods



Online advertising

Loyalty Programs

Returns

Feedback

reviews

Tweets

Emails

Customer support

Credit Card

Cash

Mobile Pay

Purchase History

PAYMENTS

Webstore

Digital transformation in retail today requires to put all this data into good use

SHOPPING EXPERIENCE

Related products

People who bought X also bought Y

Recommendations (In Real-Time)

The main product

LOOKS_AT

KITCHEN AID SERIES

LOOKS_AT

Complaints

reviews

TweetsEmails

KITCHEN AID SERIES

LOOKS_AT

Returns

Complaints

reviews

TweetsEmails

KITCHEN AID SERIES

LOOKS_AT

Returns

Inventory

Complaints

reviews

TweetsEmails

KITCHEN AID SERIES

LOOKS_AT

Returns

Home delivery

Inventory

Express goods

Complaints

reviews

TweetsEmails

Location/

KITCHEN AID SERIES

Promotions

Bundling

LOOKS_AT

Returns

Purchase History

Price-range

Home delivery

Inventory

Express goods

Complaints

reviews

TweetsEmails

Category

Promotions

Bundling

Location/

KITCHEN AID SERIES

LOOKS_AT

Returns

Purchase History

Price-range

Home delivery

Inventory

Express goods

Complaints

reviews

TweetsEmails

Category

Promotions

Bundling

Location

KITCHEN AID SERIES

Data stored as a graph

TECHNICAL LEGACY

Product

RDBMS

CRM

RDBMS

Payment

RDBMS

Marketing

RDBMS

Logistics

RDBMS

TECHNICAL LEGACY

Product

RDBMS

CRM

RDBMS

Payment

RDBMS

Marketing

RDBMS

Logistics

RDBMS

Pre-computedPurpose has to pre-determinedLimited ContextStatic

Non-graph approachRDBMS

Real-Time RecommendationsDynamicHighly contextualFlexible and Scalable

Graph approach

To get results, in real time, from a dataset that is highly interconnected – you need a

graph database!

THANK YOU!

Social networks RetailHR & Recruiting

Manufacturing & Logistics

Health Care Telco

Today we see graph-projects in virtually every industry

Finance

Finance

LEVERAGING GRAPHS TO FIGHT ECONOMIC FRAUD

The Impact of Fraud

The payment card fraud alone, constitutes for over 16 billion dollar in losses for the bank-sector in the US.

$16B payment card fraud in 2014*

Banking

$32B yearly e-commerce fraud**

Fraud in E-commerce is estimated to cost over 32 billion dollars annually is the US..

E-commerceThe impact of fraud on the insurance industry is estimated to be $80 billion annually in the US.

Insurance

$80B estimated yearly impact***

*) Business Wire: http://www.businesswire.com/news/home/20150804007054/en/Global-Card-Fraud-Losses-Reach-16.31-Billion#.VcJZlvlVhBc

**) E-commerce expert Andreas Thim, Klarna, 2015

***) Coalition against insurance fraud: http://www.insurancefraud.org/article.htm?RecID=3274#.UnWuZ5E7ROA

http://www.businesswire.com/news/home/20150804007054/en/Global-Card-Fraud-Losses-Reach-16.31-Billion#.VcJZlvlVhBc

http://www.insurancefraud.org/article.htm?RecID=3274#.UnWuZ5E7ROA

Who Are Today’s Fraudsters?

Organized in groups Synthetic Identities Stolen Identities

Who Are Today’s Fraudsters?

Hijacked Devices

“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


1.


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

Fraud Detection With Connected Analysis

Fraudulent pattern

CONNECTED ANALYSIS

Augmented Fraud Detection




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.

Modeling a fraud ring as a graph

ACCOUNT HOLDER 2

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

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

FRAUD DEMO

USING NEO4j FOR REAL-TIME CONNECTED ANALYSIS

Account-CentricAnalysis of anomaly behavior correlated across channels

4.

Entity LinkingAnalysis of relationships to detect organized crime and collusion

5.

CONNECTED ANALYSIS




DISCRETE ANALYSIS

1. 2. 3.

Augment Fraud Detection with Neo4j

Traditional Vendors

ACCEPT / DECLINE

MANUAL

User/TransactionCONNECTED ANALYSIS

User/TransactionACCEPT / DECLINE(DISCRETE ANALYSIS) +

User/Transaction (sub-second performance to any data size and connection)

ACCEPT / DECLINE

REAL TIME

TRADITIONAL VENDORS (DISCRETE ANALYSIS)

(DISCRETE ANALYSIS)

ACCEPT / DECLINE

How Neo4j fits in

• Today’s fraudsters are organized and highly sophisticated • Legacy technology does not detect fraud sufficiently and in real-time • Graph-databases enable you to discover fraudulent patterns in real-

time • Augment your current fraud detection infrastructure with connected

analysis

KEY TAKE AWAYS

THANK YOU!

Graphs in Action

Technology

Transcript of Graphs in Action