Dinuke Abeysekera, Neo4j GraphTalk Copenhagen

1. Next Generation Solutions built on Neo4j
Dinuke Abeysekera,
Pre-Sales / Field Engineer Nordics
Oct 2018

2. Agenda
● Solutions using Neo4j
● Fraud
● Recommendations
● Risk Management & Compliance
● Conclusions

3. Solutions: new mindset required?

4. Solutions: new mindset
Yesterday:
- Static Applications
- Designed to fulfill current
requirements
- Performance Constraints
- Domain experts versus IT experts
Tomorrow:
- Flexible Applications
- Designed to fulfill tomorrows
requirements
- Performance is not limiting
- Domain experts work hand in hand
with IT experts

5. Evolution using Neo4j
Neo4j Platform
G ra ph T ra nsa ctions G ra ph Ana lytics
DataIntegration
D evelopm ent &
Adm in Ana lytics T ooling
D rivers & APIs D iscovery & V isua liza tion
D evelopers
Adm ins
Applica tions B usiness U sers
D a ta Ana lysts
D a ta Scientists
3rd Party Tools
“The Graph Advantage”
Domain know-how Professional Services PS Packages
Graph Based Solution

6. Evolution using Neo4j
Neo4j Platform
GraphTransactions GraphAnalytics
DataIntegration
Development&Admin AnalyticsTooling
Drivers&APIs Discovery&Visualization
Developers
Admins
Applications BusinessUsers
DataAnalysts
DataScientists
3rd Party Tools
“The Graph Advantage”
Domain know-how Professional Services PS Packages
Graph Based Solution
Neo4j enables Graph Based Solutions
with a need for:
- Agility
- Intuitiveness
- High Performance to support
connected data scenarios
- Scalable on traversing through
connected data

7. Look at this data…

8. Swap glasses…

9. …now look at it again, this time as a graph

10. Graph Based Solutions
10
Speed: Real time query enabled Enables Up-Sell / Cross-sell
Key Features Added Value
360 degree view on data
Using data Connections as a value
Intuitive: Supports Business Needs
Finding patterns within the data
Detect anomalies
Prevent rather than detect
Enables conversation across Functions
Comply to regulations
What-if Analysis
Telco
OSS
GDPR
Fraud
Telco BSS
Recomm
endations
MDM
Resource efficient
Flexible: enabled for
Additional requirements

11. Fraud Detection
Building Powerful Solutions to prevent Fraud Based on Neo4j

12. The Impact of Fraud
The payment card fraud alone, constitutes for
over 16 billion dollar in losses for the bank-
sector in the US.
$16Bpayment card fraud in 2014*
Banking
$32Byearly e-commerce fraud**
Fraud in E-commerce is estimated to
cost over 32 billion dollars annually in
the US..
E-commerce
The impact of fraud on the insurance
industry is estimated to be $80 billion
annually in the US.
Insurance
$80Bestimated yearly impact***
*) Business Wire: http://www.businesswire.com/news/home/20150804007054/en/Global-Card-Fraud-Losses-Reach-16.31-Billion#.VcJZlvlVhBc
**) E-commerce expert Andreas Thim, Klarna, 2015
***) Coalition against insurance fraud: http://www.insurancefraud.org/article.htm?RecID=3274#.UnWuZ5E7ROA

13. Who Are Today’s Fraudsters?

14. Organized in groups Synthetic Identities Stolen Identities
Who Are Today’s Fraudsters?
Hijacked Devices

15. “Don’t consider traditional
technology adequate to keep up
with criminal trends”
Market Guide for Online Fraud Detection, April 27, 2015

16. Endpoint-Centric
Analysis of users and their
end-points
1.
Navigation Centric
Analysis of navigation
behavior and suspect
patterns
2.
Account-Centric
Analysis of anomaly
behavior by channel
3.
PC:s
Mobile Phones
IP-addresses
User ID:s
Comparing Transaction
Identity Vetting
Traditional Fraud Detection Methods

17. Unable to detect
• Fraud rings
• Fake IP-addresses
• Hijacked devices
• Synthetic Identities
• Stolen Identities
• And more…
Weaknesses
DISCRETE ANALYSIS
Endpoint-Centric
Analysis of users and their
end-points
1.
Navigation Centric
Analysis of navigation
behavior and suspect
patterns
2.
Account-Centric
Analysis of anomaly
behavior by channel
3.
Traditional Fraud Detection Methods

18. INVESTIGATE
Revolving Debt
Number of Accounts
INVESTIGATE
Normal behavior
Fraud Detection With Discrete Analysis

19. Revolving Debt
Number of Accounts
Normal behavior
Fraud Detection With Connected Analysis
Fraudulent pattern

20. CONNECTED ANALYSIS
Augmented Fraud Detection
Endpoint-Centric
Analysis of users and their
end-points
Navigation Centric
Analysis of navigation
behavior and suspect
patterns
Account-Centric
Analysis of anomaly
behavior by channel
DISCRETE ANALYSIS
1. 2. 3.
Cross Channel
Analysis of anomaly
behavior correlated across
channels
4.
Entity Linking
Analysis of relationships to
detect organized crime and
collusion
5.

21. Preventing Fraud
Networks of People
Has_SSN
Lives_With
Has_SSN
Knows
Processes and Transactions
Shipment_to
Shipment_to
Purchased
Paym
ent
Purchased
Ownership
Director_of
Owns
Officer_of
Lives_with
E.g. e-commerce Fraud, AML
E.g. detecting fraud rings,
finding connections and
shortest paths
E.g. AML, tax fraud, legal
entities
Data connections assist the business by identifying patterns
Subsidary_of
Transfer

22. The Power of Cypher
Fraud Ring:
MATCH ring = (suspect:AccountHolder)-[*]->(contactInformation)<-[*..5]-(:AccountHolder)-[*]->(suspect)
RETURN ring

23. Top Tier Electronic
Payment Services
Case studyApply to AML regulations
Challenge
• Needed to apply to AML regulation
• Inability to provide reports out of RDBMS leading
systems
Transactions fragmented and transferred „from rings to
rings“
• Neo4j is used to store and report on transaction over
previous 24 months
• Business Users / Fraud Analysts are enabled to
investigate data and detect patters
Use of Neo4j
• Complies to Regulations
• Neo4j also enabled the company to detect potential
AML usage early and act against them
“We have been unable to detect AML
fraud patterns in the SQL based
operational systems. Graphs and Graph
visualisation is a key enabler technology.”
– Top Tier Payment Service
Result/Outcome

24. What about Machine Learning?

25. What about Machine Learning?
Neo4j is an enabler technology:
• Automized detection of Fraud patterns via Cypher
• Detecting Paths
• Graph Algorithms (eg Centrality, Community)
• Algorithms as background tasks -> mark corresponding
nodes
• Automatically cancel Business Transactions
• Score identified patterns and weigh
• ….

26. Why Graph is Superior for Fraud Detection
Fraud Requirement Traditional Approaches Neo4j Approach
Find connected data patterns over unlimited
amount of „hops“
Complex queries with hundreds of join
tables
Simple single query traverses all enterprise
systems
Real-time acting on incoming events in ever
changing formats for potential fraud
Limitations inherited from SQL Database
Schema
Schema free database enables to connect
any nodes with each other
Effort required to add new data and systems Days to weeks to rewrite schema and
queries
Draw new data connections on the spot
Time to deployment Months to years Weeks to months
Response time to Fraud requests Minutes to hours per query Milliseconds per query
Form of Fraud Incidents / Investigations Text reports that are not visual and prove
very little
Visuals patterns and the path to follow
through your system
Bottom line Long, ineffective and expensive Easy, fast and affordable

27. How Neo4j fits into your environment

28. Money
Transferring
Purchases Bank
Services Relational
database
Develop Patterns
Data Science-team
+ Good for Discrete Analysis
– No Holistic View of Data-Relationships
– Slow query speed for connections

29. Money
Transferring
Purchases Bank
Services Relational
database
Data Lake
+ Good for Map Reduce
+ Good for Analytical Workloads
– No holistic view
– Non-operational workloads
– Weeks-to-months processes Develop Patterns
Data Science-team
Merchant
Data
Credit
Score
Data
Other 3rd
Party Data

30. Money
Transferring
Purchases Bank
Services
Neo4j powers
360° view of
transactions in
real-time
Neo4j
Cluster
SENSE
Transaction
stream
RESPOND
Alerts &
notification
LOAD RELEVANT DATA
Relational
database
Data Lake
Visualization UI
Fine Tune Patterns
Develop Patterns
Data Science-team
Merchant
Data
Credit
Score
Data
Other 3rd
Party Data

31. Money
Transferring
Purchases Bank
Services
Neo4j powers
360° view of
transactions in
real-time
Neo4j
Cluster
SENSE
Transaction
stream
RESPOND
Alerts &
notification
LOAD RELEVANT DATA
Relational
database
Data Lake
Visualization UI
Fine Tune Patterns
Develop Patterns
Data Science-team
Merchant
Data
Credit
Score
Data
Other 3rd
Party Data
Data-set used to
explore new
insights

32. Example Fraud Solution Architecture

33. Neo4j Database Cluster
Data Visualization
Neo4j APOC Fraud
Detection
Algorithms
Management
Dashboard
Neo4j Bolt Driver
Data Ingest
Mgmt.
…
Customer Data Sources / Systems / Applications
Legend:
Neo4j Provided Components
Custom built Neo4j/Customer
Customer/SI
Fraud Reports
Real Time Alerts
Batch
Data Buffering
(Queue)
Real-Time
Neo4j BrowserAdmin UI
UI for Fraud
Analysis
System Specific Adapters / Scripts / Connecters
Fraud Analysts Admin / SuperuserFraud Analysts Fraud Analysts

34. Neo4j powered Fraud Solution
Characteristic Benefit for Fraud Solution
• Agility • Constant catch up with fraudster techniques supported
• Enabled for Future Requirements
• Solution can be built iteratively
• Fast implementation cycles
• Schema free DB supports “connect anything”
• Intuitiveness • Enable Fraud Analysts to use Technology
• Using visualization to detect pattern
• Drilling into suspicious patterns
• Speed • Unlimited number of traversals to detect complex connections within the data
• Response time enables fraud prevention
• Leverage Data Connections • 360 degree customer view enabled / provided
• Scalability • Hardware efficiency with real-time patterns
• TCO/ROI • Adding on top of existing infrastructure protects investments

35. Recommendation Engines
Building Powerful Recommendation Engines With Neo4j

36. “If you liked this, you might like that…”
Powerful, real-time, recommendations and
personalization engines have become fundamental
for creating superior user experience and
commercial success in retail

37. Creating Relevance in an
Ocean of Possibilities

38. Retail

39. • Lots of data
• Connecting multiple data silos
• Matching in Real-Time
• Complex rules
• Changing Business Priorities
• Promotions
• Contextual Personalization
“You May Also Like”
sounds simple, but there's a lot happening behind the scenes:
…all this in milliseconds before
the user completes transaction

40. Solution aspets on Graph Based Recommendation Engines
• Recommends based on flexible scoring based algorithm
• Traverses complex set of relationships and large data sets at blazing speed
• Takes into account both user and business perspectives
• Incorporates ML for dynamic segmentation of users and similarity computation

42. Hybrid Scoring Based Approach is more contextual

44. Risk Management & Compliance

45. Internal Risk Models Span Investment Data Silos
Graph technology unites discrete silos into a unified data source
that enables banks to trace compliance data lineage

46. Need for a Modern Risk Management Platform
Benefits:

47. Conclusion
(graphs)-[:ARE]-> (everywhere)
and
(Solutions)-[:NEED]-> (graphs)

48. Who can help?
Neo4j Platform
GraphTransactions GraphAnalytics
DataIntegration
Development&Admin AnalyticsTooling
Drivers&APIs Discovery&Visualization
Developers
Admins
Applications BusinessUsers
DataAnalysts
DataScientists
3rd Party Tools
“The Graph Advantage”
Domain know-how Professional Services PS Packages
Graph Based Solution
Professional Services:
- Extend and leverage Domain Expertise
- Best Practices
- Using Building Blocks
- Don’t “re-invent the wheel”
- Speed up development and deployment
- Access to Neo4j infrastructure (Development,
Support, Product management)

49. Connectedness and Size of Data Set
ResponseTime
Relational and
Other NoSQL
Databases
0 to 2 hops
0 to 3 degrees
Thousands of connections
1000x
Advantage
Tens to hundreds of hops
Thousands of degrees
Billions of connections
Neo4j
“Minutes to
milliseconds”
Real-Time Query Performance

50. Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid)
RDBMS
&
Aggregate-
Oriented NoSQL
Hadoop /
MapReduce
|<———————- Graph Database & ———————>|
Graph Compute Engine
A View of the Data Management Portfolio

51. Thank you!