This presentation was delivered by Michael Down, Neo4j, at FIMA 2023.

1. Entity Resolution
How to expand your customer base whilst managing risks

2. Agenda
1. What we do at Neo4j
2.Entity Resolution with Graphs
3.Example use cases
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3. What we do
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4. The Graph Database
& Analytics Leader
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5. Neo4j Inc. All rights reserved 2023
150+
Global Financial
Firms
~40%
Proportion of Neo4j
revenue from Financial
Services
Fraud
Common Entry Point
For Neo4j
Graph Database Leader
Creator of the Property Graph and Cypher language at the core of the GQL ISO project
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6. Data Graph Knowledge Graph
Uncovering relationships in data
enables insights and knowledge
Dynamic Context Deep Dynamic Context
+ +
Relationships Semantics

7. Neo4j Inc. All rights reserved 2023
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Relationships can have
properties (name/value pairs)
:HAS_ACCOUNT
opened_date: 2008-01-20
Nodes can have properties
(name/value pairs)
name: Amy Peters
date_of_birth: 1984-03-01
account_no: 1 Nodes represent
objects (nouns)
Relationships are directional
:HAS_TRANSACTION
S
The property graph for advanced analytics
A more human way to create data relationships
Account
Person Transaction

8. Have you ever considered using a
graph database for entity
resolution?
Poll Question
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9. Entity Resolution with
Graphs
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10. Network-Centric Data Orientation
Graph Advantage
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Graph Algorithms
(Community Detection & Centrality)
Traverse Deep Connections
Pattern Matching
(Fraud)

11. Now show me
the (graph) magic…
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12. The graph magic…
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id first_name surname dob address
1 Micheal Down 02/02/1988 2 The Sycamores
2 Michael Downs 02/02/1988 2 The Sycamores

13. The graph magic…
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id first_name surname dob address
1 Micheal Down 02/02/1988 2 The Sycamores
2 Michael Downs 02/02/1988 2 The Sycamores

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The graph magic…

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The graph magic…
MATCH path=(p1:Person)-[:HAS_ADDRESS]->(address)<-[:HAS_ADDRESS]-(p2:Person)
RETURN path

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The graph magic…
MATCH path=(p1:Person)-[:HAS_ADDRESS|HAS_DOB]->(info)<-[:HAS_ADDRESS|HAS_DOB]-(p2:Person)
RETURN path

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The graph magic…
MATCH path=(p1:Person)-[:HAS_ADDRESS]->(address)<-[:HAS_ADDRESS]-(p2:Person)
WITH apoc.text.levenshteinSimilarity(p1.first_name, p2.first_name) AS firstNameSimilarity,
apoc.text.levenshteinSimilarity(p1.surname, p2.surname) AS surnameSimilarity,
path
WHERE firstNameSimilarity > 0.8 AND surnameSimilarity > 0.8
RETURN path

18. Hold on! Where is the
RISK PART?
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19. Neo4j Inc. All rights reserved 2023
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Risk Mitigation
Via Explainability

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Risk Mitigation
Via Explainability

21. Neo4j Inc. All rights reserved 2023
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Risk Mitigation
Via Explainability

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Risk Mitigation
Via Explainability

23. Entity Resolution
Use Case
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24. Address
Additional Entity Resolution
Use Cases
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Vehicles
People
Data Lineage
Doctors
Fraud
Quotes / Policies
Core IT
Alert Triage
Insurance

25. Can you see the value of using a
graph database for entity
resolution?
Poll Question
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26. Thank you!
michael.down@neo4j.com
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