14. 2.6 TB
11.5 million documents
Emails, Scanned Documents,
Bank Statements etc… Person
B
Bank US
Account
123
Person
A
Acme
Inc
Bank
Bahama
s
Address
XNODE
RELATIONSHIP
15. 2.6 TB
11.5 million documents
Emails, Scanned Documents,
Bank Statements etc…
29. Category Defining Use Cases
airbnb
Fraud
Detection
Real-Time
Recommendations
Network & IT
Operations
Master Data
Management
Knowledge
Graph
Identity & Access
Management
30. 10M+
Downloads
3M+ from Neo4j Distribution
7M+ from Docker
Events
400+
Approximate Number of
Neo4j Events per Year
50k+
Meetups
Number of Meetup
Members Globally
Largest pool of graph technologists
50k+
Trained/certified Neo4j
professionals
Trained Developers
33. of enterprises are using
graph databases
As of today
Source: Forrester Vendor Landscape:
Graph Databases, October 6, 2017
34. "Neo4j continues to
dominate the graph
database market.”
“69% of enterprises
have, or are planning
to implement graphs
over next 12
months”
October, 2017
“The most widely stated
reason in the survey for
selecting Neo4j was
to drive innovation”
February, 2018
Critical Capabilities for
DBMSA
“In fact, the rapid rise of
Neo4j and other graph
technologies may signal
that data connectedness
is indeed a separate
paradigm from the model
consolidation happening
across the rest of the
NoSQL landscape.”
March, 2018
Graph is a Unique Paradigm
38. Retail
7 of top 10
Finance
20 of top 25 7 of top 10
Software
Hospitality
3 of top 5
Telco
4 of top 5
Airlines
3 of top 5
Logistics
3 of top 5
76%
FORTUNE 100
have adopted or
piloted Neo4j
40. 40
• Record “Cyber Monday” sales
• About 35M daily transactions
• Each transaction is 3-22 hops
• Queries executed in 4ms or less
• Replaced IBM Websphere commerce
• 300M pricing operations per day
• 10x transaction throughput on half the
hardware compared to Oracle
• Replaced Oracle database
• Large postal service with over 500k
employees
• Neo4j routes 7M+ packages daily at peak,
with peaks of 5,000+ routing operations per
second.
Handling Large Graph Work Loads for Enterprises
Real-time promotion
recommendations
Marriott’s Real-time
Pricing Engine
Handling Package
Routing in Real-Time
43. Data Network Effect
“A product, generally powered by machine learning, becomes smarter
as it gets more data from your users. The more users use your product,
the more data they contribute; the more data they contribute, the
smarter your product becomes.”
— Matt Turck
61. “Increasingly we're learning that you can make
better predictions about people by getting
all the information from their friends and
their friends’ friends than you can from the
information you have about the person
themselves”
— Dr. James Fowler
Relationships Are Often the Strongest
Predictors of Behavior
66. $
Better Decisions
Machine Learning Pipeline
• Engineered features
when you know what
you’re looking for
• Feature extraction and
selection using graph
algorithms
• Graph embeddings to
feed into DL
Graphs add highly predictive
features to models; adding accuracy
without altering current workflows
Graphs can also infer
relationships and add data
where sparse
68. talks on
Graph-Enhanced AI & ML
recorded at
GraphConnect
talks on
Graph-Enhanced AI & ML
during the
Spring GraphTour
69. Four Pillars of Graph-Enhanced AI
1. Knowledge
Graphs
Context for Decisions
2. Connected Feature
Extraction
Context for Credibility
4. AI Explainability3. Graph
Accelerated AI
Context for Efficiency
Context for Accuracy
75. • Transaction Fraud
• Anti-money laundering (AML)
• Claims Fraud
• Credit Fraud
• Compliance and investigation
75
Improve the Predictive Power of ML in Fighting Financial Crimes
Machine Learning Pipeline
Data
Machine Learning can help uncover & learn common
traits so we can build
more predictive models
Unfortunately many machine learning
methods rely on flat data structures and
tables
76. Engineering connected features improves Machine Learning by
calculating relationship metrics when you know what’s predictive
For example, adding
how many fraudsters are
in someone’s network
is faster and simpler
using connections
Combat Financial Crimes using Connected Features
ACCOUNT
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SSN/ ID NUMBER
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BANK
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PHONE NUMBER
CREDIT CARD
SSN/ ID NUMBER
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ACCOUNT
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ACCOUNT
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ADDRESS
PHONE NUMBER
$
APPLICATION
77. ACCOUNT
HOLDER
ACCOUNT
HOLDER
ACCOUNT
HOLDER
ACCOUNT
HOLDER
ACCOUNT
HOLDER
3 Fraudsters – 4 Hops Out
4 Fraudsters – 2 Hops Out
BANK
ACCOUNT
SSN/ ID
NUMBER
UNSECURED
LOAN
BANK
ACCOUNT
BANK
ACCOUNT
UNSECURED
LOAN
PHONE
NUMBER
CREDIT
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SSN/ ID
NUMBER
PHONE
NUMBER
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HOLDER
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HOLDER
ADDRESS
PHONE
NUMBER
$
APPLICATION
Combat Financial Crimes using
Connected Feature Engineering
78. Decisions
$
Better Decisions
Graphs add highly predictive
features to models; adding accuracy
without altering current workflows
Machine Learning Pipeline Machine Learning Pipeline
Traditional methods based on ”flat data”
simplify, or leave out entirely, predictive
relationship and network data