“Graphs in the Real World”

Developed, deployed and battle-tested graph use-cases

Value from Data RelationshipsCommon Graph Database Use Cases

Internal Applications

Master Data Management

Network and IT Operations

Fraud Detection

Customer-Facing Applications

Real-Time Recommendations

Graph-Based Search

Identity and Access Management

Graphs for Master Data Management

MDM as a Graph

What we *think* MDM is What MDM *really* is

Patient

Agent

G.P.Surgeon Partner

Insurance

Patient

AgentG.P.Surgeon

PartnerInsurance

Common Graphs in Master Data Management

C

C

A AA

U

S S SS S

USER_ACCESS

CONTROLLED_BY

SUBSCRIBED _BY

User

Customers

Accounts

Subscriptions

VP

Staff Staff StaffStaff

DirectorStaffDirector

Manager Manager Manager Manager

FiberLink

FiberLink

FiberLink

Ocean Cable

Switch Switch

Router Router

Service

OrganizationalHierarchy

Product Hierarchy

Network Topology/ CMDB

Social Network

die Bayerische – Master Data Management

Mid-size German insurer

Founded in 1858

More than 500 employees

Project executed by Delvin GmbH,

subsidiary of die BayerischeVersicherung

360° View of the Customer

die Bayerische SOLUTION

• Complete view customer & policy information by Field Sales

• Flexibly policy & customer search

• Overcome scaling limitations of existing IBM DB2 system

• Extend information to sales partners

Classmates – Social network

Online yearbook connecting friends from

school, work and military in US and Canada

Founded as Memory Lane in Seattle

Develop new social networking capabilities to monetize yearbook-related offerings

• Show all the people I know in a yearbook

• Show yearbooks my friends appear in most often

• Show sections of a yearbook that my friends appear most in

• Show me other schools my friends attended

Classmates SOLUTION

Neo4j provides a robust and scalable graph database solution

• 3-instance cluster with cache sharding and disaster-recovery

• 18ms response time for top 4 queries

• 100M nodes and 600M relationships in initial graph—including people, images, schools, yearbooks and pages

• Projected to grow to 1B nodes and 6B relationships

Source:“Growing the Elephant: Tales from an Enterprise Data Model”by Jeremy Posner (Synechron)Enterprise Data World 2015

Graphs for Network and IT Operations Management

Graphs in Networking

The Royal Netherlands Meteorological Institute

Operational Infrastructure to Collect, Record, and Manage Weather Data

Graph Applied to Fraud Detection

Some Examples

Retail First Party Fraud• Opening many lines of credit with no intention of paying back

• Accounts for $10B+ in annual losses at US banks(1)

Synthetic Identities and Fraud Rings• Rings of synthetic identities committing fraud

Insurance – Whiplash for Cash• Insurance scams using fake drivers, passengers and witnesses• Increase network efficiency

eCommerce Fraud• Online payment fraud

(1) Business Insider: http://www.businessinsider.com/how-to-use-social-networks-in-the-fight-against-first-party-fraud-2011-3

ProsSimpleStops rookies

Discrete Data Analysis

RevolvingDebt

INVESTIGATE

INVESTIGATE

Number of accounts

ConsFalse positivesFalse negatives

Connected Analysis

RevolvingDebt

Number of accounts

PROSDetect fraud rings

Fewer false negatives

Graph of First Party Bank Fraud

AccountHolder

1

AccountHolder

2

AccountHolder

3

SSN2

SSN2

PhoneNumbe

r2

CreditCard

Address1

BankAccount

BankAccount

BankAccount

PhoneNumbe

r2

CreditCard

UnsecuredLoan

UnsecuredLoan

Insurance Fraud Example

Gartner’s Layered Fraud Prevention Approach (4)

(4) http://www.gartner.com/newsroom/id/1695014

Traditional Fraud Prevention

Analysis of users

and their endpoints

Analysis ofnavigation

behavior and suspect patterns

Analysis of anomaly

behavior by channel

Analysis of anomaly behavior

correlated across channels

Analysis of relationships

to detect organized crime

and collusion

Layer 1

Endpoint-Centric

Navigation-Centric

Account-Centric

Cross-Channel

Entity Linking

Layer 2 Layer 3 Layer 4 Layer 5

DISCRETE DATA ANALYSIS CONNECTED ANALYSIS

Graphs for Real-time Recommendations

Using Data Relationships for Recommendations

Collaborative filtering

Predict what users like based on the similarity of their behaviors, activities and preferences to others

Content-based filtering

Recommend items based on what users have liked in the past

Movie

Person

Person

Retail Recommendations

“We found Neo4j to be literally thousands of times faster than our prior MySQL solution, with queries that require 10-100 times less code. Today, Neo4j provides eBay with functionality that was previously impossible.”- Volker Pacher, Senior Developer, eBay

eBay – Real-time routing recommendations

• Order from local stores

• Deliveries within 90 minutes

• Leverage local courier services

• Calculate best route in real-time

Graphs for Graph-Based Search

Curaspan – Graph-based Search

Leader in patient management for

discharges and referrals

Manages patient referrals 4600+ health care facilities

Connects providers, payers via web-based patient management platform

Founded in 1999 in Newton, Massachusetts

“Find a skilled nursing facility within 5 miles of the patient’s home, belonging to an eligible health care group, offering speech therapy and cardiac care, and optionally Italian language services”

Curaspan WHERE ARE THE GRAPHS?

• Permissions: Caregivers to Patient Data

• Coverage: Organizational Relationships

• Provider Services & Skills

• Service Areas: Location Graph

Graphs for Identity and Access Management

Identity & Access Management

• Based in Oslo

• #1 in Nordics

• #10 in world

Oslo-based Telco#1 in Nordic countries

#10 in world

Mission-critical system

Availability and responsiveness critical to

customer satisfaction

Telenor – Identity & Access Management

Source:

Using Graph Databases in Real-Time to Solve Resource

Authorization at Telenor -Sebastian Verheughe @

GraphConnect London 2013

Value from Data RelationshipsCommon Graph Database Use Cases

Internal Applications

Master Data Management

Network and IT Operations

Fraud Detection

Customer-Facing Applications

Real-Time Recommendations

Graph-Based Search

Identity and Access Management

Graphs in the Real World

March 2015