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Your Enemies Use
GenAI Too!
Staying Ahead of Fraud with Neo4j
2
Fraud Losses
are
Skyrocketing
Fraud is expected to
surge 20% annually in
coming years1
$42B Lost to fraud globally
in 20222
Neo4j Inc. All rights reserved 2024
1. Experian Research: Identity Theft Is on the Rise, Both in Incidents and Losses 2. PwC’s Global Economic Crime and Fraud
Survey 3. BioCatch: 2023 APAC Digital Banking Fraud Trends Report 4. WalletHub: Credit Card Fraud Statistics
Additional costs for each dollar
of credit card fraud losses4
$3.75
200% Surge in voice scams
from 2022 to 20233
3
A.I. is About to Supercharge Financial Fraud
and make it much harder to spot
Beware Of FraudGPT,
The Rogue AI Chatbot
Deepfake Imposter Scams
Are Driving a
New Wave of Fraud
Financial firms must
boost protections
against AI scams, UK
regulator to warn
I Cloned Myself With AI.
She Fooled My Bank
and My Family
Criminals Have
Created Their Own
ChatGPT Clones
Neo4j Inc. All rights reserved 2024
Neo4j Inc. All rights reserved 2024
4
Fraud Frontiers: Examples
Push
Payment
Fraud
Credit
Fraud
Insurance Claims Fraud
Digital Wallet or
Retail Fraud
Supply Chain Fraud,
Procurement Fraud
Internal
Fraud
Checks
Pandemic Fraud/Medical
Insurance Fraud
Tax Fraud/ Criminal
Investigation
Many
Others
5 Neo4j Inc. All rights reserved 2024
Criminals exploit vulnerabilities
in real-time by rapidly adopting
new technologies like AI, while
enterprises struggle to adapt
due to legacy systems and data
silos, taking weeks or months to
detect new fraud schemes.
Why Fraudsters
Have the
Upper Hand
6
Existing Systems Fall Short
Inability to
Uncover
Complex Fraud
Patterns
Difficult to
Adapt to
Evolving
Threats
Neo4j Inc. All rights reserved 2024
Can’t Scale to
Deliver Rapid
Analysis
Analyzing data without
relationships doesn’t work
Pathfinding and entity
resolution need
specific algorithms
Identifying complex, cross-
entity fraud becomes
difficult or impossible
Finding recursive fraud
patterns doesn’t scale
Fraud evades detection
because queries take too long
Hard to connect disparate
data sets without refactoring
the whole database
Coding fraud patterns in SQL
is time-consuming and results
in brittle queries
7 Neo4j Inc. All rights reserved 2024
Neo4j Graph Database and Analytics
8 Neo4j Inc. All rights reserved 2024
Data to Knowledge
Connecting data
adds context and
improves outcomes.
Identify suspicious activity
quickly and accurately with
pattern matching
Scale with any cloud and
speed up fraud detection
and investigation
9
Build Powerful
Fraud
Applications
to Uncover
Hidden Patterns
Improve and evolve fraud
applications with a developer-
friendly schema
10
Identify Suspicious Activity Quickly and Accurately
Discover and investigate hard-to-find fraud
quickly with pattern matching
Find fraud patterns 1000x faster
than relational databases
Expose intermediaries and find fake profiles
through pathfinding and entity resolution
Neo4j Inc. All rights reserved 2024
11
Improve and Evolve Fraud Applications
Reduce exposure to fraud by linking
disparate and disconnected data sets
Stay ahead of fraudsters by detecting
new or emerging fraud patterns quickly
Enable wider and easier use of data by connecting
siloed data into an intuitive data model
Neo4j Inc. All rights reserved 2024
12
Scale with any Cloud and Speed up Fraud
Detection and Investigation
Deploy your applications anywhere
Find fraud in ever-larger data sets and scale up number
of users and concurrent transactions
Integrate with your existing enterprise ecosystem
Neo4j Inc. All rights reserved 2024
Flexible Cloud Deployment Models
Fully-managed SaaS
Consumption-
based pricing
For private
and hybrid
cloud, or on-prem
White-glove
managed service by
Neo4j experts
13 Neo4j Inc. All rights reserved 2024
Self-Hosted
Cloud Managed Services
Graph-as-a-Service
Cloud-native
Self-service deployment
Fully customizable deployment
model and service levels
Operate In own data centers
or Virtual Private Cloud
Bring your own license
Full control of your environment
Run in any cloud, in your account
Neo4j Inc. All rights reserved 2024
14
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
15
Banking &
Financial Services
Technology Telecommunications Energy
E-Commerce
Health & Life
Sciences
1,700+ Organizations Use Neo4j
Neo4j Inc. All rights reserved 2024
16
Summary
Identify suspicious activity
quickly and accurately with
pattern matching
Scale with any cloud and
speed up fraud detection
and investigation
Improve and evolve fraud
applications with a developer-
friendly schema
name.name@neotechnology.com
Neo4j Inc. All rights reserved 2024
17
Thank you!
Come learn more at booth #324
John “Steggy” Stegeman | Product Specialist, Neo4j

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Your enemies use GenAI too - staying ahead of fraud with Neo4j

  • 1. Your Enemies Use GenAI Too! Staying Ahead of Fraud with Neo4j
  • 2. 2 Fraud Losses are Skyrocketing Fraud is expected to surge 20% annually in coming years1 $42B Lost to fraud globally in 20222 Neo4j Inc. All rights reserved 2024 1. Experian Research: Identity Theft Is on the Rise, Both in Incidents and Losses 2. PwC’s Global Economic Crime and Fraud Survey 3. BioCatch: 2023 APAC Digital Banking Fraud Trends Report 4. WalletHub: Credit Card Fraud Statistics Additional costs for each dollar of credit card fraud losses4 $3.75 200% Surge in voice scams from 2022 to 20233
  • 3. 3 A.I. is About to Supercharge Financial Fraud and make it much harder to spot Beware Of FraudGPT, The Rogue AI Chatbot Deepfake Imposter Scams Are Driving a New Wave of Fraud Financial firms must boost protections against AI scams, UK regulator to warn I Cloned Myself With AI. She Fooled My Bank and My Family Criminals Have Created Their Own ChatGPT Clones Neo4j Inc. All rights reserved 2024
  • 4. Neo4j Inc. All rights reserved 2024 4 Fraud Frontiers: Examples Push Payment Fraud Credit Fraud Insurance Claims Fraud Digital Wallet or Retail Fraud Supply Chain Fraud, Procurement Fraud Internal Fraud Checks Pandemic Fraud/Medical Insurance Fraud Tax Fraud/ Criminal Investigation Many Others
  • 5. 5 Neo4j Inc. All rights reserved 2024 Criminals exploit vulnerabilities in real-time by rapidly adopting new technologies like AI, while enterprises struggle to adapt due to legacy systems and data silos, taking weeks or months to detect new fraud schemes. Why Fraudsters Have the Upper Hand
  • 6. 6 Existing Systems Fall Short Inability to Uncover Complex Fraud Patterns Difficult to Adapt to Evolving Threats Neo4j Inc. All rights reserved 2024 Can’t Scale to Deliver Rapid Analysis Analyzing data without relationships doesn’t work Pathfinding and entity resolution need specific algorithms Identifying complex, cross- entity fraud becomes difficult or impossible Finding recursive fraud patterns doesn’t scale Fraud evades detection because queries take too long Hard to connect disparate data sets without refactoring the whole database Coding fraud patterns in SQL is time-consuming and results in brittle queries
  • 7. 7 Neo4j Inc. All rights reserved 2024 Neo4j Graph Database and Analytics
  • 8. 8 Neo4j Inc. All rights reserved 2024 Data to Knowledge Connecting data adds context and improves outcomes.
  • 9. Identify suspicious activity quickly and accurately with pattern matching Scale with any cloud and speed up fraud detection and investigation 9 Build Powerful Fraud Applications to Uncover Hidden Patterns Improve and evolve fraud applications with a developer- friendly schema
  • 10. 10 Identify Suspicious Activity Quickly and Accurately Discover and investigate hard-to-find fraud quickly with pattern matching Find fraud patterns 1000x faster than relational databases Expose intermediaries and find fake profiles through pathfinding and entity resolution Neo4j Inc. All rights reserved 2024
  • 11. 11 Improve and Evolve Fraud Applications Reduce exposure to fraud by linking disparate and disconnected data sets Stay ahead of fraudsters by detecting new or emerging fraud patterns quickly Enable wider and easier use of data by connecting siloed data into an intuitive data model Neo4j Inc. All rights reserved 2024
  • 12. 12 Scale with any Cloud and Speed up Fraud Detection and Investigation Deploy your applications anywhere Find fraud in ever-larger data sets and scale up number of users and concurrent transactions Integrate with your existing enterprise ecosystem Neo4j Inc. All rights reserved 2024
  • 13. Flexible Cloud Deployment Models Fully-managed SaaS Consumption- based pricing For private and hybrid cloud, or on-prem White-glove managed service by Neo4j experts 13 Neo4j Inc. All rights reserved 2024 Self-Hosted Cloud Managed Services Graph-as-a-Service Cloud-native Self-service deployment Fully customizable deployment model and service levels Operate In own data centers or Virtual Private Cloud Bring your own license Full control of your environment Run in any cloud, in your account
  • 14. Neo4j Inc. All rights reserved 2024 14 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
  • 15. Neo4j Inc. All rights reserved 2024 15 Banking & Financial Services Technology Telecommunications Energy E-Commerce Health & Life Sciences 1,700+ Organizations Use Neo4j
  • 16. Neo4j Inc. All rights reserved 2024 16 Summary Identify suspicious activity quickly and accurately with pattern matching Scale with any cloud and speed up fraud detection and investigation Improve and evolve fraud applications with a developer- friendly schema
  • 17. name.name@neotechnology.com Neo4j Inc. All rights reserved 2024 17 Thank you! Come learn more at booth #324 John “Steggy” Stegeman | Product Specialist, Neo4j

Editor's Notes

  1. Background. Fraud is pervasive worldwide and is growing. Experian Research: Identity Theft Is on the Rise, Both in Incidents and Losses PwC’s Global Economic Crime and Fraud Survey 2022 BioCatch: 2023 APAC Digital Banking Fraud Trends Report WalletHub: Credit Card Fraud Statistics
  2. Key Takeaway: The Generative AI revolution isn’t just being used for good; fraudsters are using technology to make their fraud harder to spot.
  3. Key Takeaway: Fraud is everywhere (this slide is a build) Often we think of fraud as something that only pertains to Banking, Financial Services, and Insurance (BFSI) and retail companies. <click to do the first build> Indeed, these kinds of fraud overwhelmingly affect those kinds of organizations. <click to add the second build> However, there are other types of fraud that affect us much more widely <mention one or two if they are particularly relevant to your prospect>. <click to do the final build>...and of course these are only just examples.
  4. Key Takeaway: Fraudsters are taking advantage of new technologies and techniques faster than we can. Hard-to-change legacy systems and disparate, disconnected data sources mean that it takes us a long time to discover and adapt to new fraud schemes.
  5. Relational DBMS systems fall short for three broad reasons: They cannot uncover complex fraud patterns. They do not store relationships natively, and need to use joins and ever-complex queries, and these break down when they go beyond the small and the simple. RDBMSs also dont have algorithms for pathfinding, which are needed to find connections between entities and fraudulent actors. THey cannot scale to deliver rapid analysis. What happens is that when RDBMSs use joins and SQL queries to attack recursive patterns, they do not return results quickly enough. Instead of millisecond response, they take minutes or hours. Customers won’t wait for that. RDBMSs are difficult to evolve over time. Bringing new datasets typically requires the existing database schema to be refactored, which also means that queries and application code need to be refactored as well. SQL queries are also “brittle” meaning that when (for example) fraud rings get larger because fraudsters are using more complex techniques to evade detection, the SQL queries need to be rewritten to detect those larger rings - this takes time, both developer time to re-code the queries, and run-time when the queries take longer and longer because of the extra joins.
  6. Key takeaways: Applications built on Neo4j can identify suspicious activity quickly and accurately through pattern matching, fast join-free relationship traversals, exposing intermediaries, and resolving entities to find fake or synthetic profiles.
  7. Key takeaways: Applications built on Neo4j can identify suspicious activity quickly and accurately through pattern matching, fast join-free relationship traversals, exposing intermediaries, and resolving entities to find fake or synthetic profiles.
  8. How does Neo4j help deal with these issues? Neo4j is a native graph database for building fraud applications that can: 1. Identify suspicious activity quickly and accurately 2. Improve and evolve over time 3. Be deployed on any cloud and scale up Let’s look at how…
  9. Key takeaways: Applications built on Neo4j can identify suspicious activity quickly and accurately through pattern matching, fast join-free relationship traversals, exposing intermediaries, and resolving entities to find fake or synthetic profiles.
  10. Key takeaway: Applications built using Neo4j are flexible, allowing them to evolve over time as needs change and as fraudsters get smarter <the next slide has an animation that plays only once. Make sure to leave some space in your talking points for the audience to absorb the animation before you start talking about it. It only lasts for about 4-5 seconds>
  11. Key takeaway: With Neo4j, you can deploy on a fully managed cloud database on any of the major clouds. That database will grow and scale with you as you scale up, and it has connectors and APIs that work with your existing data ecosystems.
  12. Key takeaway: Deploy in the cloud of your choice with “as-a-Service,” “White glove managed services tailored to your needs,” or “self-hosted” options
  13. Key takeaway: Customers across an array of industries use Neo4j for fraud detection as well as other use cases
  14. Summarize the value proposition again and tie back to your customer’s specific needs.