Graph databases can help insurance companies address challenges like siloed data systems, identity resolution issues, and an inability to gain a full view of customers. They allow for a unified customer 360 view across different business units. Graph databases perform better than SQL for data that is interconnected, requires optimal querying of relationships, and has an evolving data model. Specifically for insurance, graphs can increase cross-sell/upsell opportunities, retention rates, and customer satisfaction while reducing costs and fraud. EY has experience implementing graph solutions for use cases like fraud detection and customer 360 projects.

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

2. 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

3. 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
►Obsolescence of EDW
►Fast changing customer needs
►Primarily broker-mediated market
►Recent fraud trends - Deepfakes
►Increased manual processing
►Reporting than recommending
►Reactive rule-based policies
►Operations at scale
Caused by

4. 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

5. Customer 360° View in an Insurance Company
UNIFIED VIEW
OF THE CUSTOMER
Market-
ing
Sales Policy
Claims
Contact
Centre
Broker
External
Data
Demogra
phics
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

6. 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

7. 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

8. 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…
Identify & ingest
multiple data sources
All data is
aggregated and
linked together in
the graph that
provides an entity
centric view of all
customers,
products, and
merchants
Link and maintain
graph database
Create new data
assets and signals
Key components of the Customer Golden Profile
Customer
Agent
Product
Master Data
Quote
Policy
Claims
Customer Journey Data
Risk & Compliance
Insured Asset
Third-party
External Data
Build a complete view
of customers’
relationship with
businesses
Identify key data
elements and customer
behaviors within and
across Lines of
Business
Develop customer 360
level attributes that are
predictive of customer
behaviour
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

9. How Graphs add value to the insurance business
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
Average of CSAT
score and Annual
NPS score
Average time-to-
resolve at Contact
Centre
Direct and Indirect
expenses by
Customer Journey
milestones
Loss ratio and
combined ratio
Straight-Through-
Processing
policies

10. 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 always
validate the information online
• Discount optimisation based on “influence
capability” of the agents
Risk & Compliance
• Increased risk exposure due to “serial”
entrepreneurs (a.k.a habitual offenders)
• 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
Some of the interesting insights were
Why graph technology makes sense for fraud detection and customer 360 projects in insurance

11. Team:
• Data Product Owner
• Graph Architect
• Data Engineer
• Full-stack Developer
• Data Scientist
• Report Developer
Problem / Scope
What will the graph
solve?
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
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?
Asking better questions
Start small and scale
Why graph technology makes sense for fraud detection and customer 360 projects in insurance

12. 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 3-4 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
Industrialisation
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
Key learnings
Envision end
product at data
modelling phase
Fail Fast. Learn
Faster. (Agile)
Shipping beats
perfection (Product)
Dependency on IT
Infrastructure &
Security
Do not
underestimate Data
Quality
Improvement
Early business
adopters becomes
product evangelist
Why graph technology makes sense for fraud detection and customer 360 projects in insurance

13. 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 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
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