© 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
The Path To Success With Graph
Database and Data Science
Michael Down
1

© 2023 Neo4j, Inc. All rights reserved.
© 2022 Neo4j, Inc. All rights reserved.
Neo4j Graph Data Platform
2
BUSINESS
USERS
DEVELOPERS
DATA
SCIENTISTS
DATA
ANALYSTS
Enterprise Ready
Data Science & MLOps
Graph Data Science
OLAP
Data Science and Analytics
Tools, algorithms, and Integrated ML framework
AutoML
Integrations
Discovery & Visualization
Low-code querying, data modeling and exploration tools
Neo4j
Bloom
BI
Connectors
Neo4j
Browser
Language
interfaces
Application Development Tools & Frameworks
Tools and APIs for rapid prototyping and development
Graph Query Language
Cypher and GQL as the lingua franca for graphs
Transactions Analytics
Graph Database
Data Consolidation
Contextualization
OLTP
Native Graph Database
The core component of Neo4j platform
Runs Anywhere
Run by yourself or as DBaaS by Neo4j, in the cloud or on premises
Data Connectors
Ecosystem & Integrations
Rich set of connectors to plug into existing data ecosystems
Data Sources

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Engineering Expertise
>1000 person-years investment
First mover advantage
Maturity, Most enterprise deployments
Largest graph community
Growing at 80%+ annually
Neo4j Graph Database Capabilities
Hybrid
Workloads
Native Graph
Architecture
Powers Graph
Data Science
Rich
Toolset
Enterprise
Trust
Runs
Anywhere
3

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4
Native Graph Architecture
Native Graph
Storage
Native Graph
Processing
• No mismatch
• Data integrity / ACID
• Schema flexible
• 1000x faster than relational
• K-Hop now 10-1000x faster
than version 4
Fabric
• Federation of scaled
out shards
• Instant composite
database
Composite DB
Autonomous
Clustering
• Elastic scale-out for
high throughput
• 100s of machines
across clusters
Data integrity and high speed also true in scaled out situations

© 2023 Neo4j, Inc. All rights reserved.
Developer Productivity: Rich tooling and easy onramp
ops manager
7
data importer
Visualize and explore your data
Query editor and results visualizer
Code-free data loader and modeler
AuraWorkspace
Unified Workspace

© 2023 Neo4j, Inc. All rights reserved.
Enterprise-Grade: Security and Trust Built In
Single Sign-On Secure Development
Practices
Dedicated VPC Role- & Schema-Based
Access Control
Encryption
(At-Rest, In-Transit,
and Intra Cluster)
SOC 2 Type 1
9

© 2023 Neo4j, Inc. All rights reserved.
Forward looking investments
Developer Experience Complete multi-cloud availability AuraDB on Azure in addition to GCP, AWS
Making graph ubiquitous with GQL compliance
Programmatic management and monitoring with APIs for AuraDB
Solidifying Neo4j as the data store of record: CDC + next-gen Kafka connector
Theme: the first-choice and primary database that graph-powers any application
Performance at Scale Analytic step-up performance with Parallel Cypher Queries
Improved mem-to-storage ratio / Lower TCO with Freki next-gen storage
Even more autonomous clustering with declarative server management
Operational Trust Better monitoring and tuning with query analyzer in Neo4j Ops Manager
Integrated observability with AuraDB metrics and log streaming
Customer managed security in AuraDB with customer managed encryption keys and
customer managed RBAC

© 2023 Neo4j, Inc. All rights reserved.
© 2023 Neo4j, Inc. All rights reserved.
Neo4j Graph Data Science

© 2023 Neo4j, Inc. All rights reserved.
What’s important?
Prioritization
Who has the most connections?
Who has the highest page rank?
Who is an influencer?
What’s unusual?
Anomaly & Fraud Detection
Where is a community forming?
What are the group dynamics?
What’s unusual about this data?
What’s next?
Predictions
What’s the most common path?
Who is in the same community?
What relationship will form?
13
Graph Structure Improves Data Science Outcomes

© 2023 Neo4j, Inc. All rights reserved.
And created Neo4j Graph Data Science:
Eliminate Pain & Optimize Data Science Workflows with the data you already
have
Eliminates Pain Optimizes Data Science
Flows
Complex joins operations
Mining Multiple Tables
Tedious Manual
Approximations
Brute Force Comparisons
Fractured Data
95% reduction in computation
time
500x faster than open source
libraries
Improves Customer
Outcomes
20-30% improvement in model
performance
600% improvement in traffic
$5 Million of additional fraud
detected
3x better churn predictability
5x reduction in factory
production lead time
14

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15
Data
Scientists
> Native Python Client
> Apache Arrow integration
> Unified ML pipelines
We invest in four key areas
Built by data scientists,
for data scientists
Better
Predictions
> 65+ Graph algorithms &
embeddings
> Graph native ML Pipelines
> Vertex AI & SageMaker
Integrations
The best graph data
science and ML engine
Ecosystem
> Apache Spark & Kafka
Connectors
> Native BI Connector
> Data Warehouse Connector
> GNN library support
Seamlessly works with
your data stack and
pipeline
Production
Ready
> Compatible with all major
clouds
> Enterprise Scale & Security
> Deploy anywhere
Go to production with
speed, scale, and
security

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16
With The Largest Catalog of Graph Algorithms
Pathfinding &
Search
Centrality &
Importance
Community
Detection
Supervised
Machine Learning
Heuristic Link
Prediction
Similarity Graph
Embeddings
…and more
Graph algorithms are a set of instructions that visit the nodes of a graph to
analyze the relationships in connected data.

© 2023 Neo4j, Inc. All rights reserved.
And Full Support Across the entire ML Lifecycle
Feature
Engineering
Model Training
& Tuning
Model
Deployment
Data Collection
& Preparation
Exploratory
Data Analysis
Model
Evaluation &
Selection
Drivers,
Connectors, Fast
Import/Export
Graph Queries,
Algorithms, and
Visualization
Graph
Embeddings &
Algorithms
Predict APIs,
Model/Graph
Catalog
Operations,
Connectors
Graph Native ML Pipelines
Unsupervised Graph Algorithms
Graph Features -> External ML Pipelines
17

© 2023 Neo4j, Inc. All rights reserved.
And made it seamless for all ecosystems and pipelines
Graph Data Science
BI & VISUALIZATIONS
INGEST
STORE
PROCESS
Apache
Kafka
MACHINE LEARNING
Cloud
Functions
Neo4j
Bloom
PubSub
DataProc
Analytics
Feature
Engineering
Data
Exploration
Graph
Data
Science
Business
Applications &
Existing Systems
Files (unstructured,
structured)
TensorFlow
KNIME Python
Cloud Storage
AWS
Lambda
18
Graph Database

© 2023 Neo4j, Inc. All rights reserved.
View the most well connected and influential nodes
Recommendations from shared user interactions and associations
Our Visualizations Make analysis easy to understand
19

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20
What’s in it for you:
● Improve model accuracy by 30%
● Simplify processes and remove
headaches
● More projects into production
without additional hiring
Neo4j Graph Data Science
Analytics
Feature
Engineering
Data
Exploration
Graph
Data
Science
Queries & Search
Machine Learning Visualization

© 2023 Neo4j, Inc. All rights reserved.
21
Customer Case Study:
Fraud Detection
Correctly identify account holders
committing fraud
Results:
● 300% increase in fraud detection
● 10% true positive escalations
(industry standard < 1%)
● Reduced false positive escalations
● 150% increase in payment flow

© 2023 Neo4j, Inc. All rights reserved.
22
How to get started…
3. Graph Native
Machine Learning
Learn features in your graph
that you don’t even know are
important yet using
embeddings.
Predict links, labels, and
missing data with in-graph
supervised ML models.
Identify associations,
anomalies, and trends using
unsupervised machine
learning.
2. Graph Algorithms
1. Knowledge Graphs
Find the patterns you’re looking
for in connected data

© 2023 Neo4j, Inc. All rights reserved.
23
What’s New in Graph Data
Science

© 2023 Neo4j, Inc. All rights reserved.
Algos & Embeddings
HashGNN Embedding: Faster
approach than GNNs for knowledge
graphs
KMeans Cluster data based on
properties like graph embeddings
Leiden Algorithm: Fast and scalable
modularity based community detection
Image courtesy of: Traag, V.A., Waltman, L. & van Eck, N.J.
Image courtesy of: javatpoint.com
Leiden Algorithm:
K-means Clustering:
24

© 2023 Neo4j, Inc. All rights reserved.
ML Pipelines
Autotuning: Find optimal
hyperparameters to
improve model
performance
Multilayer Perceptrons
(MLPs): Fully connected
neural networks now
available for Link Prediction
and Node Classification
25

© 2023 Neo4j, Inc. All rights reserved.
GNN Support
Graph Sampling: sample a
representative subgraph
from a larger graph for
training complex models
Graph Export: use our
projections in other graph
ML libraries like Deep Graph
Library (DGL), PyG, and
Tensorflow GNN
Image courtesy of Google Cloud
26

© 2023 Neo4j, Inc. All rights reserved.
27
Other
Data Stores
Transactions Analytics
Graph Database Graph Data Science
Integrated AI/Machine
Learning
Data
Integrations
&
Connectors
Admin
Cypher
Drivers
&
APIs
Dev
Tools
Application Layer: Digital Twin, Recommendation, Fraud Detection, Cybersecurity, …
Query
Browser
GraphQL
Analytics & AI/Machine Learning Pipelines
The Neo4j Graph Data Platform
Flexible Graph Schema
Performance, Reliability &
Integrity
Scale-Up & Scale-Out Architecture
Development Tools Breadth
Enterprise Ecosystem

© 2023 Neo4j, Inc. All rights reserved.
Continue your graph journey
Connect with passionate graphistas
Free online training and
certification
• dev.neo4j.com/learn
• dev.neo4j.com/datasets
Graph expert group - The
Ninjas
• dev.neo4j.com/ninjas
Connect with the
community:
• dev.neo4j.com/chat
• dev.neo4j.com/forum
• dev.neo4j.com/newsletter
Next developer events
• Live Streams - Weekly & Online
• Local Meetups neo4j.com/events

© 2023 Neo4j, Inc. All rights reserved.
Meet the Neo4j Ninjas
Masters of Graphs
Ninjas are:
Active graph bloggers, presenters, GitHub contributors, professors,
user group leaders, and researchers - all sharing their graph expertise
Benefits:
Ninjas benefit from exclusive access to Neo4j experts, VIP event
experience, special giveaways and much more
Interested? For more information visit:

© 2023 Neo4j, Inc. All rights reserved.
APOC Documentation
Other Neo4j Resources
Neo4j Graph Data Science Documentation
Neo4j Cypher Manual
Neo4j Driver Manual
Cypher Style Guide
Arrows App
• APOC is a great plugin to level up your
cypher
• This documentation outlines different
commands one could use
• Link to APOC documentation
• The Cypher manual can be used to get
more information about Cypher commands
• Link to cypher manual
• Neo4j Graph Data Science documentation
is a great reference to see which algorithms
to use
• Show how to use different algorithms
• Link to Graph Data Science documentation
• The driver manual provides the official
drivers that are supported by Neo4j
• Link to Neo4j driver manual
• The cypher style guide provide
recommendations for building clean, easy
to read Cypher queries
• Link to Cypher style guide
• The Arrows app allows one to design a
graph without using Cypher
• Link to Arrows app
Cypher Cheat Sheet
• This page gives quick examples of how to
write different queries within Cypher
• Link to Cypher cheat sheet
GraphGists
• GraphGists has many different use cases
and examples for specific industries
• Link to GraphGists
Neo4j Sandbox
• The Neo4j sandbox provides a quick
deployment of a Neo4j server
• It does not require a download
• Comes with example projects
• Link to Neo4j Sandbox

Why graph technology makes sense
for fraud detection and customer 360
projects in Insurance
March 2023
Why graph technology makes sense
for fraud detection and customer
360 projects in insurance

Introduction
Why graph technology makes sense for fraud detection and customer 360 projects in insurance
► 13+ years of experience in data science diversely spread across consulting, industry,
and start-up
► 3+ years in graph technology building data products
► Currently leading the tech for wavespace AI Labs and Data Science for Utilities at
EY Ireland
► A data scientists & a professional accountant who bridges the gap between
technology and business value
► Active contribution to Data Science community and a vocal supporter of DE&I in
Data Science

EY has a large and growing graph
practice, with over 200 consultants
globally.
We see a wide range of graph use
cases across all sectors and have
delivered several compelling graph
solutions to help our clients drive
greater insight, efficiency and value.
EY and Graph Technology
Why graph technology makes sense for fraud detection and customer 360 projects in insurance
Plasma
Donor
360
Retail
Customer
360
Customer
Identity
Enterprise
Org
Design
FinServ
Know Your
Customer
Regulatory
Reporting
Data
Lineage
Anti-Money
Laundering
GCN
Cruiseline
Activity
NBA
Batch
Geneaology
B2B
Event
NBA
Capital
Projects
Cost
Visibility
COVID-19
Risk
Tracking
Fuels
Tradiing
Forecasting
Global
Compliance
Monitoring
Active
Directory
Access
Controls
Financial
Ledger
Transaction
Lineage
FINANCIAL
SERVICES
SALES &
MARKETING
ENERGY
ASSET
MANAGEMENT
LIFE
SCIENCES
RISK
EY SOLUTIONS

Inability to
recommend Next
Best Action (NBA)
Non-optimized fraud
identification and actioning
capabilities
Lack of full view of
customers and
agents
Insurers today are struggling with identity resolution which impacts
growth
Why graph technology makes sense for fraud detection and customer 360 projects in insurance
Silo-ed legacy systems
Fast changing
Customers needs
Broker / Aggregator
mediated market
New fraud trends like
Deepfakes
Reactive & rule-based
policies
Operations at Scale
Leads to:

Many companies today utilize Customer
Graphs:
To support the demands of the digital
business, enterprise architects must
consider how best to link large volumes of
complex, siloed data... Graph
databases are a powerful
optimized technology that link
billions of pieces of connected data to help
create new sources of value for
customers and increase
operational agility for customer
service.
–
Forrester
Zurich
Large online
shopping site
These challenges have been successfully solved using graph databases
Why graph technology makes sense for fraud detection and customer 360 projects in insurance

Customer 360° View in an Insurance Company
Market-ing
Sales Policy
Claims
Contact
Centre
Broker
External
Data
Demographics
UNIFIED VIEW
OF THE CUSTOMER
Why graph technology makes sense for fraud detection and customer 360 projects in insurance
A unified Customer 360° view enables
• Data-driven, customer-centric experiences
• Efficient and automated sales & marketing
• Improved compliance and better underwriting
through fraud detection
• Consistent view of operational metrics across
business segments
• Improved decision-making based on more
reliable reporting

Why graph database is better than SQL
Why graph technology makes sense for fraud detection and customer 360 projects in insurance
Graph can add value in any environment where:
Data is interconnected and
relationships matter
Data needs to be read and queried
with optimal performance
Data is evolving and data model is
not always fixed and pre-defined
Before
Name Address Policy Claims Broker
Phone
Customer Golden Record
LOB - System 1
LOB - System 2
LOB - System 3
Agent Quote
Source Systems EDW Schema Slow Execution

Why graph database is better than SQL
Why graph technology makes sense for fraud detection and customer 360 projects in insurance
Graph can add value in any environment where:
Data is interconnected and
relationships matter
Data needs to be read and queried
with optimal performance
Data is evolving and data model is
not always fixed and pre-defined
After
LOB - System 1
LOB - System 2
LOB - System 3
Agent Quote
Source Systems Graph Schema Faster execution
Customer Golden Profile

Customer Golden Profile will create cross-LOB data assets to help
answer key strategic questions
Integrating data into the graph and building analytics and BI-layers on top to enable rapid extraction of cross-LOB
features on a customer for context-based decision making
Rapidly test and operationalize
new analytical capabilities
• Who are our
customers?
• What drives a
customer to make
a buy decision?
• How to
understand
different customer
behaviours?
• How to get right
and up-to-date
information about
every customer?
• How to create
effective risk
policies?
We want to
understand…
Key components of the Customer Golden Profile
Example Capabilities
• Predict Churn
• Personalise product
bundling
• Optimise discount via agent
effectiveness
• Predict conversion in sales
cycles
• Predict effectiveness of
cross-sell & up-sell schemes
• Predicting fraud triangles
• Effective Chatbot for Contact
Centre activities
Why graph technology makes sense for fraud detection and customer 360 projects in insurance
Identify & ingest
multiple data
sources
Link and
maintain graph
database
Create new data
signals and products

Sales / Marketing
• Customers are not always “price sensitive” but “value sensitive”
• Referral programs are effective along with product bundling
• Agent is the “influencer” but customers validates the information online
• Discount optimisation based on “influence capability” of the agents
Risk & Compliance
• Increased risk exposure due ignoring past performance (linking historical
policies for artificial persons)
• Common elements between claims - like garage, doctor, 3rd party in car &
liability insurance, etc.
• Loss of opportunities from traditional rule-based risk policies – E.g., a young
driver is not always the riskiest driver; add more dimensions for risk validation
Some of the interesting insights were
Why graph technology makes sense for fraud detection and customer 360 projects in insurance

Proving the value of Graph technology
Increase
Cross-Sell and
Upsell
Increase
Retention
Increase Customer
Satisfaction
Reduce Cost to
Acquire and
Service
Reduce Fraud
Why graph technology makes sense for fraud detection and customer 360 projects in insurance
And how to measure them?
Value(€) and Volume(#)
of policies sold to
existing customers in a
year
Measure what matters . . .
Annual customer churn
rate across and within
LOB Straight-Through-
Processing policies
Direct and Indirect
expenses by Customer
Journey milestones like
Quote, Policy,
Operations, etc
Average time-to-resolve
at Contact Centre
Average of CSAT score
and Annual NPS score
Loss and combined ratio

Production Build
Cloud Pilot
Localhost POC
Graphy Problem
Business need, Data sources Data modeling, API, example
queries
Data snapshot, reference
architecture, API suite
Hardening, scheduled & stream
ETL, Live UX
Stakeholder Input
Problem / Scope
What will the graph
solve?
Graph Design
Data Work
APIs / Data Services
Integration
Scale
Validate
What questions can
now be answered?
Connect
Does the data support
the graph model and
semantics?
Mobilize
What data does the
new experience need?
Use Cases
What is the feedback
from the business on
how well the graph
solves the use case?
Deploy
What monitoring,
testing, process needs
to be put in place to
achieve a robust SLA?
Start small and scale
Why graph technology makes sense for fraud detection and customer 360 projects in insurance
Asking better questions

Making Graphs work is not a sprint but a marathon
o Once the data integration phase is complete, the environment is ready for iterating through several 12-16 week use case sprints
o At the end of each sprint, an assessment of the results, in terms of revenue and cost benefits, will guide the decision for additional
Use Cases
o In parallel to each sprint,
o Inform the senior stakeholders on current decision processes to develop more Use Cases for the backlog
o Identify “evangelist” business users for early adoption and acting as voice of influence amongst end-users
Continuous Use Case Development – Sustain and Scale
User
Interview
Sprint Backlog and
Scheduling
Business Use
Case
Business Use
Case
Business Use
Case
Business Use
Case
Model
Development
Industrialisatio
n
BAU
Operations
Strategic
Reporting
Self-service
reporting
Code Config BI / MI
Monitoring Controls Automation
One-off outputs
that cannot be
sustained are
retired after use
Outputs
decommissioned if
not deemed
feasible
Delivery Pod
Delivery Pod
Delivery Pod
Successful use cases, Pod
move into build
Experiment failed, Pod spun
down
Delivery Pod
Delivery Pod
Pod creates a single use
model
Pod output planned for BAU
run
Model
libraries
Maintenance
Sprint Case 1
Sprint Case 2
Sprint Case 3
Sprint Case 4
Sprint Case 5
Sprint Case 6
…
-- W1
-- W4
-- W7
-- W10
-- W13
-- W16
-- …
Allocation
to
Delivery
Pods
Why graph technology makes sense for fraud detection and customer 360 projects in insurance

Thank you! Questions?
Why graph technology makes sense for fraud detection and customer 360 projects in insurance

EY | Building a better working world
EY exists to build a better working world, helping to create
long-term value for clients, people and society and build
trust in the capital markets.
Enabled by data and technology, diverse EY teams in over
150 countries provide trust through assurance and help
clients grow, transform and operate.
Working across assurance, consulting, law, strategy, tax and
transactions, EY teams ask better questions to find new
answers for the complex issues facing our world today.
EY refers to the global organisation, and may refer to one or more, of the member
firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst
& Young Global Limited, a UK company limited by guarantee, does not provide
services to clients. Information about how EY collects and uses personal data and a
description of the rights individuals have under data protection legislation are
available via ey.com/privacy. EY member firms do not practice law where prohibited
by local laws. For more information about our organisation, please visit ey.com.
© 2023 Ernst & Young. All Rights Reserved.
The Irish firm Ernst & Young is a member practice of Ernst & Young Global Limited. It
is authorised by the Institute of Chartered Accountants in Ireland to carry on
investment business in the Republic of Ireland.
Ernst & Young, Harcourt Centre, Harcourt Street, Dublin 2, Ireland.
Information in this publication is intended to provide only a general outline of the
subjects covered. It should neither be regarded as comprehensive nor sufficient for
making decisions, nor should it be used in place of professional advice. Ernst &
Young accepts no responsibility for any loss arising from any action taken or not
taken by anyone using this material.
ey.com

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46
THANK YOU