This document discusses why graph technology is ideal for customer 360 and fraud detection projects in the insurance industry. It provides an overview of graph use cases in banking, insurance, and capital markets including for customer 360, fraud detection, and knowledge graphs. It then discusses challenges insurers face with siloed data and lack of a unified customer view. Implementing a customer graph allows linking diverse data sources to create a complete view of customers and their relationships to enable context-based decision making and analytics.
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EY: Why graph technology makes sense for fraud detection and customer 360 projects
1. Page 1
Why graph technology is ideal
for Customer360 and fraud
detection
2. Page 2
1. Introduction & Background
2. Graph Use Cases in Banking, Insurance and Capital
Markets
3. Customer360 and Fraud in Insurance
4. How to get up and running
AGENDA
Page 2
3. Page 3
Introduction
Kenneth Nielsen, PhD
Director
Kenneth.Nielsen@dk.ey.com
+45 25 29 32 61
Kasper Müller
Manager
kasper.mueller@dk.ey.com
+45 25294765
Ivana Hybenova
Senior Consultant
ivana.hybenova@dk.ey.com
+45 25295065
Sebastian S. Bennetzen
Consultant
sebastian.bennetzen@dk.ey.com
+45 25296373
• Kenneth is a Director in FS Technology Consulting where he heads up the Data and Analytics area. Prior to joining EY in
2017, he worked 3.5 years as a senior quant in Nordea Markets with a high focus on optimizing the use of data across
teams.
• His team focuses on big data analytics, network analysis and streamlining/automating processes using a combination of
process mining, machine learning and programming. Kenneth has been delivering projects revolving around these topics
in both global banks and most recently in Danske Bank.
• Kasper is a Manager in FS Technology Consulting and in practice, he is working as a data scientist and programmer,
designing and building mathematical and programmatical models and solutions.
• His experience counts DevOps, Machine Learning, Cloud Architecture, and implementing scalable solutions using
Docker and Kubernetes.
• Kasper infuses graphs on the back of EY-developed solutions such as the GDPR Scanner and holds both the Certified
Developer and Graph Data Science certification
• Ivana has background in statistics and has been working as a data analyst and scientist for over 4 years.
• She currently leads the organization of the EY Graphathons as well as two internal graphs projects on customer analytics
and AML
• Ivana is Neo4j Certified Professional and has also Neo4j Graph Data Science certification.
• Sebastian is a consultant in FS Technology Consulting. He has obtained a masters degree in Mathematics and
Economics with a focus on graph theoretic studies from SDU.
• At EY, Sebastian has been working on fostering Graph Analytics by hosting multiple Graphathons and contributing to
internal graph-based projects.
• Sebastian has completed the Neo4j Certified Professional and Neo4j Graph Data Science certification.
Why graph technology is ideal for Customer 360 and fraud detection
4. Page 4
Background …
Why graph technology is ideal for Customer 360 and fraud detection
5
4
3
2
1
5.5 years ago
• First encounter
3.5 years ago
• Decided to have a
second look
1 year ago
• Graph Community
(Nordic) setup
2.5 years ago
• Established the first small
in-house EY group
• Initial relationship with
Neo4j
TODAY
• EY Graphatons
• Part of Global Graph
community
5. Page 5
…. and a warning
Why graph technology is ideal for Customer 360 and fraud detection
7. Page 7
Banking, Insurance and Capital Markets
Why graph technology is ideal for Customer 360 and fraud detection
Enterprise Knowledge Graph
Customer 360
Financial Crime
Identity Access Management
8. Page 8
A B C D E
A B C D E
One-to-Many
Relationships
Across Many
Entities
Wide Data Complex Data Hierarchical & Recursive Data
Many-to-Many
Relationships
Nested Tree
Structures
Recursion (Self-
Joins)
Deep
Hierarchies
Link Inference
(If C relates to A and A relates to E,
then C must relate to E)
Node Similarity
Hidden Data
Legacy Data Frozen Data
Legacy SQL Systems Data Lake Fact Tables Graph Data Science - Machine Reasoning
A
C
E
If your data looks like this, you should be thinking graph
Why graph technology is ideal for Customer 360 and fraud detection
9. Page 9
A B C D E
A B C D E
One-to-
Many
Relationship
s Across
Many
Entities
Wide Data Complex Data Hierarchical & Recursive Data
Many-to-Many
Relationships
Nested Tree
Structures
Recursion (Self-
Joins)
Deep
Hierarchies
Link Inference
(If C relates to A and A relates to E,
then C must relate to E)
Node Similarity
Hidden Data
Legacy Data Frozen Data
Legacy SQL Systems Data Lake Fact Tables Graph Data Science - Machine Reasoning
A
C
E
Enough with the abstract
Customer Product Basic form Tegningsgrundlag Conditions Status Risk savings Payment 1 Payment 2
D10006 Traditional 211 D5 1 3 S PU P
D10007 Traditional 810 D5 1 4 R PU P
D10008 Traditional 810 D3 1 4 R PU P
D10008 Traditional 810 D5 1 4 R PU P
D10009 Traditional 810 D5 1 4 R PU P
D10009 Traditional 840 D5 1 4 R PU P
D10010 Traditional 810 D2 1 4 R PU P
D10010 Traditional 840 D2 1 4 R PU P
D10010 Traditional 810 D3 1 4 R PU P
D10010 Traditional 840 D3 1 4 R PU P
D10010 Traditional 810 D5 1 4 R PU P
D10010 Traditional 840 D5 1 4 R PU P
D10011 Traditional 115 D1 1 1 R PP E
D10011 Traditional 185 D1 1 1 S PP E
Why graph technology is ideal for Customer 360 and fraud detection
11. Page 11
Inability to
recommend Next
Best Action (NBA)
Non-optimized fraud
identification and
actioning capabilities
Lack of full view of
customers and
agents
Silo-ed legacy systems
Obsolescence of Enterprise Data
Warehouse
Fast changing customer needs
Primarily broker-mediated market
Recent fraud trends - Deepfakes
Increased manual processing
Reporting vs recommending
Reactive rule-based policies
Operations at scale
Caused by
Insurers are facing some key challenges which impacts growth
!
Why graph technology is ideal for Customer 360 and fraud detection
12. Page 12
UNIFIED VIEW
OF THE CUSTOMER
Marketin
g
Sales Policy
Claims
Contact
Centre
Broker
External
Data
Demogra
phics
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
Customer 360° View in an Insurance Company
Why graph technology is ideal for Customer 360 and fraud detection
13. Page 13
Example Schema: Insurance Agent 360°
Marketing
UNIFIED VIEW
OF THE CUSTOMER
Demo
graphic
Policy
Claims
Contact
Center
External Data
Broker
Sales
Why graph technology is ideal for Customer 360 and fraud detection
14. Page 14
Why graph technology makes sense for fraud detection and customer 360 projects in insurance
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
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
Customer Golden Profile will create cross-LOB data assets to help answer key strategic
questions
15. Page 15
Increase Cross-
Sell and Upsell
Increase
Retention
Increase Customer
Satisfaction
Reduce Cost to
Acquire and
Service
Reduce Fraud
And how to measure them?
Value(DKK) 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
How Graphs add value to the insurance business
Why graph technology is ideal for Customer 360 and fraud detection
16. Page 16
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 is ideal for Customer 360 and fraud detection
17. Page 17
Can’t we just do this in an RDBMS?
Customer Golden Record
• Slow execution
• Faster execution
EDW Schema
Graph schema Customer Golden Profile
Before
After
Source systems
LOB - System 1
LOB - System 2
LOB - System 3
Agent Quote
Name Address Phone Policy Claims Broker
Why graph technology is ideal for Customer 360 and fraud detection
18. Page 18
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 is ideal for Customer 360 and fraud detection
”
19. Sub-header slide
Why graph technology makes sense for fraud detection and customer 360 projects in insurance
How to get up and running
20. Page 20
Neo4j development generally begins with a graph model designed to answer a set of
questions, and then data is imported to populate the model. The model is tested and
adjusted as needed.
Iterative, Agile Solution Development Approach
Adjust if data is missing
?
Start with a
question
Build or extend
model using
domain expertise
Source data to
populate model
Import data Build query Analytics
Adjust if needed
to answer question
Why graph technology is ideal for Customer 360 and fraud detection
21. 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 organization, 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 organization, please visit ey.com.
2023 EYGM Limited.
All Rights Reserved.
ED None
This material has been prepared for general informational purposes only and is not intended to be relied upon as
accounting, tax, legal or other professional advice. Please refer to your advisors for specific advice.
ey.com
Hinweis der Redaktion
2) Use cases, data structures that spell graph
3) Based on work done at an insurance company in Belgium with global reach – what are the data points you want in there? What could a schema look like in insurance? Couldn’t we just do this in RDBMS? Outcome of project and how to measure
Not a very technical talk, but happy to discuss technicalities afterwards.
Who am I?
Team – 25
Three with me today – Kasper, Ivana and Sebastian.
Spent past three years going down the rabbit hole of graphs
Cross-Nordic graph community
We host graphathons
Ask Kasper, Ivana and Sebastian
Who am I?
Team – 25
Spent past three years going down the rabbit hole of graphs
Cross-Nordic graph community
Three with me today – Kasper, Ivana and Sebastian
We host graphathons
Takeaway – you don’t start big, you start with a specific problem!
X360°:
Customer 360° / KYC
Agent / Financial Professional 360°
Product Recommendations / Next Best Action
EDF:
Data Lineage / Dependency
Data Exposure
Process:
Customer Journey
Origination / Underwriting
Portfolio Management
Risk:
AML/Fraud Detection
Risk / Compliance
Identity / Access Management
System Dependency / Points of Failure
It is often a mix between the data structures
Requires many entities (e.g., many SQL tables, 360° views)
Involves recursion (e.g., SQL self joins)
Has complex, potentially colliding, hierarchies (e.g., SQL 1 to many, many-to-many)
Based on informatics of the relationships themselves (e.g., collaborative filtering shared relationship counts, shortest path segment summations for wayfinding, cost/time minimization for supply chain, money flows for finance)
Requires mapping, direct or indirect across data sources (e.g., data lake unification)
Demands fast query results (e.g., digital applications, search)
Another example is when you have holes (i.e. sparse) in your dataset
Inorganic growth => 5 ERP systems. Graph for resolution of entities and products
Deep fakes – document falsification and identity theft
Reactive: bundling not marketed pro-actively. After the graph project became Amazon of insurance
Talk around the circle from Broker to External Data
Questions related to policy provide further demographic data
Why did they want 360 view?
What kinds of data do you put in there? It is not rocket science!
Internal database
Customer demographics (master)
Quotations generated
Policy
Claims
Payments data
Broker
Call centre interactions (enquiry, complaints, requests)
Web portal interactions (online transactions – policy renewals, FNOL, claims, etc.)
Social media interactions (enquiry, complaints, requests, feedback)
External database
Companies database (non-individual customers)
Address database – entity resolution
Point of interest – OSM
GIS data
Weather data
Property registration, Marine vessels, Car registration data
A unified view of the customer is foundational to a successful digital transformation. The customer 360° view is derived from customer, product, sales, marketing, support and web data. Data is ingested & cleaned in a data lake, unified & analyzed in a Knowledge Graph, and mobilized via API microservices.
Provides the agent a 360° view of customer activity
Connects historical data (policies, claims, quotes) with real-time interactions (customer support, web events, mobile)
Handles householding complexity
Supports customer analytics and next-best-action product recommendations
X-LOB can’t be stressed enough!
Leading your graph-projects with tangible questions makes things easier, but there will often also be a lot of value (and questions generated) from just starting to view your data in a graph!
Some customers may be both private customers and have their company be a client – not all insurance companies pick up on this , at least not realtime. What if the ownership structure around a company changes – someone enters the ownership structure who also owns other companies that are not currently customers. Is that a threat or an opportunity?
Example: automation of investigations in fraud and money laundering => save time, save money, catch more bad guys. The best foundation is a graph database.
Have an optimized webpage (customer journey on webpage is also a graph)
CVR data => are the owners of your company customers involved in other companies? Could they be influencers?
Garage or garages. Incorporating e.g. CVR data you can start to see if there are patterns in who submits claims and/or where claims are “fixed”. You can – in principle – start to do adverse media screening.
SO, why is graph tech ideal for Customer360 and Fraud?
When is Graph superior to Relational DB – it depends…
Flexibility in adding third party data, e.g. CVR, weather data, BBR data, etc.
Cypher>SQL – story from DB
At the end of the day it is about picking the right tool for the task!
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
The question: how many customers do I have that are n steps away from e.g. a known fraudster? How do different autoshops rank in terms of usage for specific cases? Do we have private customers who are company owners? What about the people they co-own with?
Gather the data necessary. Start right, not small – don’t go overboards! Do PoC and communicate expected value to the business. Work in cross-functional teams.