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The Impact of Graph Technology
on Financial Services
Neo4j Inc. All rights reserved 2024
2
Fraud is ever
increasing
Fraud is
increasing
Fraud…
Neo4j Inc. All rights reserved 2024
3
Fraud…
Fraud is ever
increasing
Fraud is
increasing
Associated
costs are
increasing
Neo4j Inc. All rights reserved 2024
4
Fraud…
Fraud is ever
increasing
Fraud is
increasing
Associated
costs are
increasing
Complex
Fraud
Patterns
Scale to
Deliver Rapid
Analysis
Adapt to
Evolving
Threats
Detection
systems need
to improve
Neo4j Inc. All rights reserved 2024
5
Graph creates a
connected view of
data that is intuitive
and actionable.
Simple but powerful
Graphs
Neo4j Inc. All rights reserved 2024
6
● Storage
● Graph Transactions
● Querying
Graph Database Graph Analytics
● Graph Analytics
● Machine Learning
● Graph Data Science
You could enter a name and just double
click, and the networks would expand.
Millions of people had gone into it. So in
the Panama Papers investigations, we
knew that we needed graphs to
understand the data better.
Mar Cabra, Data Editor ICIJ
Neo4j Inc. All rights reserved 2024
7
Biggest cross-border journalism projects ever:
● More than 60 million files
● Various formats
● Multiple interconnected datasets datasets
Known for publishing:
● Offshore Leaks (2013)
● Panama Papers (2016)
● Paradise Papers (2017)
● Pandora Papers (2021)
Knowledge graphs
provide deep, dynamic context.
Connecting data adds context and
improves outcomes.
Neo4j Inc. All rights reserved 2024
8
Data to Knowledge
Transactions
graph
Consumer
graph
Parts
graph
Digital Twin
graph
Neo4j Inc. All rights reserved 2024
9
Knowledge Graphs Enable Various Use Cases
Neo4j Inc. All rights reserved 2024
10
Building Knowledge Graphs
Chapters on:
• Pattern Detection
• Dependency
• Identity
• Semantic Search
• Enrichment with GDS
• Graph native ML
E-book-Building Knowledge
graphs : A Practitioner’s Guide
GenAI -Knowledge Graphs with LLM’s
Neo4j Inc. All rights reserved 2024
11
Neo4j Inc. All rights reserved 2024
12
Make Sense of Data Relationships
What’s important? What’s unusual? What’s next?
Transacts
T
r
a
n
s
a
c
t
s
Transacts
Transacts
T
ra
n
s
a
c
ts
Banking &
Financial Services
Health & Life
Sciences
Neo4j Inc. All rights reserved 2024
13
Technology Telecommunications Energy
E-Commerce
1,700+ Organizations Use Neo4j
Uncover Hard-to-Find Fraud Schemes
Discover fraudulent patterns using graph
queries optimized for pattern matching
Traverse the database 1000x faster
than relational databases with
index-free adjacency
Discover new anomalies and patterns
by visualizing complex data and
relationships using Bloom
Neo4j Inc. All rights reserved 2024
14
Match patterns in data and relationships
to find complex, recursive fraud
patterns such as rings, trees, and chains
using Cypher query language
Expose Intermediaries and Find Fake Profiles
Resolve entities using cypher queries and
graph algorithms like Node Similarity and
Weakly Connected Components
Uncover connections between fraudulent
actors and intermediaries using
pathfinding algorithms like Yen’s,
Delta-Stepping Single-Source,
and Dijkstra Source-Target
Make linkage predictions between
identities in the graph, create unified
views of individual identities, and
group nodes using node similarity and
community detection algorithms
Neo4j Inc. All rights reserved 2024
15
Known
Fraudster 1
Registered
Address
Registered
Phone Number
Registered
Business
Known
Fraudster 2
Name: Johan Nordberg
Age: 32
Occupation: Pilot
Employer: SAS
Address 1: Arlanda, Sweden
Address 2: Uppsala, Sweden
Bank Acct: XXXXX934
Name: Johan Nordberg
Age: 32
Occupation: Pilot
Employer: SAS
Address: Uppsala, Sweden
Bank Acct: XXXXX934
Name: Johan Nordberg
Age: 32
Occupation: Pilot
Employer: SAS
Address: Arlanda, Sweden
Bank Acct: XXXXX934
16
TODO1 Increases Fraud Detection by 200% with Neo4j
Impact
Using Neo4j, TODO1's iuviPROFILER
increased fraud detection by
200% while maintaining the
same false positive rate.
Challenge
Latin America's rapidly growing digital
banks must instantly assess transaction
risks to maintain seamless user
experiences, but traditional databases
are inadequate for real-time detection.
Solution
TODO1 created iuviPROFILER, using
Neo4j, to efficiently process data and
assess risk, enabling banks to securely
conduct transactions with minimal
friction, even during peak demand times.
iuviPROFILER’s
Graph by the Numbers
–
250 million nodes
–
2.2 billion relationships
Graph Performance
by the Numbers
–
>500 transactions
per second
–
100 milliseconds
per query
For the same false positive rate,
we’re able to achieve twice the
detection rate.’ he said. ‘And that in
the end is less friction for the
customer, less losses for the bank,
and a better feeling in terms of
protection for their customers.
Edgar Osuna
Chief Data and Analytics Officer
“ “
Read the full customer story on Neo4j.com
Neo4j Inc. All rights reserved 2024
17
Neo4j Transforms Zurich’s Fraud Triage Process
Impact
● Field investigators uncover
connections previously lost in
large data sets.
● Investigators save 5-10 minutes
per case, leading to significant
cost savings.
Challenge
Zurich Switzerland's automated fraud
detection system overwhelmed
investigators with data, making
manual checks time-consuming and
lacking context for cross-referencing
vital information.
Solution
Zurich Switzerland uses Neo4j and
Linkurious for efficient fraud triage,
helping investigators quickly spot
suspicious patterns and connections.
Zurich’s Graph by
the numbers
–
20 million nodes
–
35 million
relationships
50,000 hours
saved each year
Read the full customer story on Neo4j.com
If I were to tell our investigators
today that we are doing away
with Neo4j, there would be
a huge outcry. The solution is
indispensable for their daily work.
Paul Kühne
Head of Fraud Prevention,
Zurich Insurance
“ “
Neo4j Inc. All rights reserved 2024
18
From Rule-Based to Graph ML: Banking Circle's Evolution
Impact
● 300%+ increase in fraud
detection
● 10% true positive alert escalations
(industry is <1%)
● 25% reduction of false positives
Challenge
Must ensure fast, safe, and secure
cross-border transactions for business
clients like e-commerce companies, but
the traditional rules-based system was
slow, manual, and burdened by false
positives.
Solution
Built a knowledge graph linking
accounts to payments and employed
community detection algorithms to
create features for a machine learning
pipeline that identifies and ranks
high-risk clusters.
300% increase in
fraud detection
25% reduction of
False positives
Neo4j Inc. All rights reserved 2024
Knowledge Graphs enable organizations to
reason about their underlying data.
Graph Queries and Graph Algorithms can
be applied to find complex fraud patterns on
scale in milliseconds.
Multiple organizations are using Neo4j and
report an increase in fraud detection, time
savings during fraud investigations and
reduction in false positives alerts.
Neo4j Inc. All rights reserved 2024
19
Impact Radar for 2024
Thank you!
name.name@neotechnology.com
Neo4j Inc. All rights reserved 2024
20
Feel free to reach out to discuss how your organization can use Neo4j.
E-book-Building Knowledge graphs : A Practitioner’s Guide
White Paper: Fraud Detection
Gartner: Impact Radar 2024
Case Studies:
○ The ICIJ-Panama Papers
○ TODO1 Increases Fraud Detection Rates by 200 Percent with Neo4j
○ Faster Fraud Investigations with Neo4j
erik.bijl@neo4j.com
Linkedin

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Neo4j_Exploring the Impact of Graph Technology on Financial Services.pdf

  • 1. The Impact of Graph Technology on Financial Services
  • 2. Neo4j Inc. All rights reserved 2024 2 Fraud is ever increasing Fraud is increasing Fraud…
  • 3. Neo4j Inc. All rights reserved 2024 3 Fraud… Fraud is ever increasing Fraud is increasing Associated costs are increasing
  • 4. Neo4j Inc. All rights reserved 2024 4 Fraud… Fraud is ever increasing Fraud is increasing Associated costs are increasing Complex Fraud Patterns Scale to Deliver Rapid Analysis Adapt to Evolving Threats Detection systems need to improve
  • 5. Neo4j Inc. All rights reserved 2024 5 Graph creates a connected view of data that is intuitive and actionable. Simple but powerful Graphs
  • 6. Neo4j Inc. All rights reserved 2024 6 ● Storage ● Graph Transactions ● Querying Graph Database Graph Analytics ● Graph Analytics ● Machine Learning ● Graph Data Science
  • 7. You could enter a name and just double click, and the networks would expand. Millions of people had gone into it. So in the Panama Papers investigations, we knew that we needed graphs to understand the data better. Mar Cabra, Data Editor ICIJ Neo4j Inc. All rights reserved 2024 7 Biggest cross-border journalism projects ever: ● More than 60 million files ● Various formats ● Multiple interconnected datasets datasets Known for publishing: ● Offshore Leaks (2013) ● Panama Papers (2016) ● Paradise Papers (2017) ● Pandora Papers (2021)
  • 8. Knowledge graphs provide deep, dynamic context. Connecting data adds context and improves outcomes. Neo4j Inc. All rights reserved 2024 8 Data to Knowledge
  • 9. Transactions graph Consumer graph Parts graph Digital Twin graph Neo4j Inc. All rights reserved 2024 9 Knowledge Graphs Enable Various Use Cases
  • 10. Neo4j Inc. All rights reserved 2024 10 Building Knowledge Graphs Chapters on: • Pattern Detection • Dependency • Identity • Semantic Search • Enrichment with GDS • Graph native ML E-book-Building Knowledge graphs : A Practitioner’s Guide
  • 11. GenAI -Knowledge Graphs with LLM’s Neo4j Inc. All rights reserved 2024 11
  • 12. Neo4j Inc. All rights reserved 2024 12 Make Sense of Data Relationships What’s important? What’s unusual? What’s next? Transacts T r a n s a c t s Transacts Transacts T ra n s a c ts
  • 13. Banking & Financial Services Health & Life Sciences Neo4j Inc. All rights reserved 2024 13 Technology Telecommunications Energy E-Commerce 1,700+ Organizations Use Neo4j
  • 14. Uncover Hard-to-Find Fraud Schemes Discover fraudulent patterns using graph queries optimized for pattern matching Traverse the database 1000x faster than relational databases with index-free adjacency Discover new anomalies and patterns by visualizing complex data and relationships using Bloom Neo4j Inc. All rights reserved 2024 14 Match patterns in data and relationships to find complex, recursive fraud patterns such as rings, trees, and chains using Cypher query language
  • 15. Expose Intermediaries and Find Fake Profiles Resolve entities using cypher queries and graph algorithms like Node Similarity and Weakly Connected Components Uncover connections between fraudulent actors and intermediaries using pathfinding algorithms like Yen’s, Delta-Stepping Single-Source, and Dijkstra Source-Target Make linkage predictions between identities in the graph, create unified views of individual identities, and group nodes using node similarity and community detection algorithms Neo4j Inc. All rights reserved 2024 15 Known Fraudster 1 Registered Address Registered Phone Number Registered Business Known Fraudster 2 Name: Johan Nordberg Age: 32 Occupation: Pilot Employer: SAS Address 1: Arlanda, Sweden Address 2: Uppsala, Sweden Bank Acct: XXXXX934 Name: Johan Nordberg Age: 32 Occupation: Pilot Employer: SAS Address: Uppsala, Sweden Bank Acct: XXXXX934 Name: Johan Nordberg Age: 32 Occupation: Pilot Employer: SAS Address: Arlanda, Sweden Bank Acct: XXXXX934
  • 16. 16 TODO1 Increases Fraud Detection by 200% with Neo4j Impact Using Neo4j, TODO1's iuviPROFILER increased fraud detection by 200% while maintaining the same false positive rate. Challenge Latin America's rapidly growing digital banks must instantly assess transaction risks to maintain seamless user experiences, but traditional databases are inadequate for real-time detection. Solution TODO1 created iuviPROFILER, using Neo4j, to efficiently process data and assess risk, enabling banks to securely conduct transactions with minimal friction, even during peak demand times. iuviPROFILER’s Graph by the Numbers – 250 million nodes – 2.2 billion relationships Graph Performance by the Numbers – >500 transactions per second – 100 milliseconds per query For the same false positive rate, we’re able to achieve twice the detection rate.’ he said. ‘And that in the end is less friction for the customer, less losses for the bank, and a better feeling in terms of protection for their customers. Edgar Osuna Chief Data and Analytics Officer “ “ Read the full customer story on Neo4j.com Neo4j Inc. All rights reserved 2024
  • 17. 17 Neo4j Transforms Zurich’s Fraud Triage Process Impact ● Field investigators uncover connections previously lost in large data sets. ● Investigators save 5-10 minutes per case, leading to significant cost savings. Challenge Zurich Switzerland's automated fraud detection system overwhelmed investigators with data, making manual checks time-consuming and lacking context for cross-referencing vital information. Solution Zurich Switzerland uses Neo4j and Linkurious for efficient fraud triage, helping investigators quickly spot suspicious patterns and connections. Zurich’s Graph by the numbers – 20 million nodes – 35 million relationships 50,000 hours saved each year Read the full customer story on Neo4j.com If I were to tell our investigators today that we are doing away with Neo4j, there would be a huge outcry. The solution is indispensable for their daily work. Paul Kühne Head of Fraud Prevention, Zurich Insurance “ “ Neo4j Inc. All rights reserved 2024
  • 18. 18 From Rule-Based to Graph ML: Banking Circle's Evolution Impact ● 300%+ increase in fraud detection ● 10% true positive alert escalations (industry is <1%) ● 25% reduction of false positives Challenge Must ensure fast, safe, and secure cross-border transactions for business clients like e-commerce companies, but the traditional rules-based system was slow, manual, and burdened by false positives. Solution Built a knowledge graph linking accounts to payments and employed community detection algorithms to create features for a machine learning pipeline that identifies and ranks high-risk clusters. 300% increase in fraud detection 25% reduction of False positives Neo4j Inc. All rights reserved 2024
  • 19. Knowledge Graphs enable organizations to reason about their underlying data. Graph Queries and Graph Algorithms can be applied to find complex fraud patterns on scale in milliseconds. Multiple organizations are using Neo4j and report an increase in fraud detection, time savings during fraud investigations and reduction in false positives alerts. Neo4j Inc. All rights reserved 2024 19 Impact Radar for 2024
  • 20. Thank you! name.name@neotechnology.com Neo4j Inc. All rights reserved 2024 20 Feel free to reach out to discuss how your organization can use Neo4j. E-book-Building Knowledge graphs : A Practitioner’s Guide White Paper: Fraud Detection Gartner: Impact Radar 2024 Case Studies: ○ The ICIJ-Panama Papers ○ TODO1 Increases Fraud Detection Rates by 200 Percent with Neo4j ○ Faster Fraud Investigations with Neo4j erik.bijl@neo4j.com Linkedin