The document discusses Gartner's concept of the "5 Graphs" that are important for consumer web giants and financial services companies. It describes each of the 5 graphs - Payment, Customer, Entitlement, Asset, and Master Data graphs. For each graph, it provides examples of how the graph can be modeled and queried in a graph database to power use cases in fraud detection, customer 360 views, identity and access management, portfolio analytics, and data governance. It also discusses how graph databases provide faster performance for highly connected data compared to relational databases.

1. The 5 Graphs of
Finance
Philip Rathle
Sr. Director of Products
@prathle
philip@neotechnology.com
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2. The “4 Giants”
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Ref: http://www.gartner.com/id=2081316

3. Gartner’s “5 Graphs”
Consumer Web Giants Depends on Five Graphs
Interest Graph
Intent Graph
Social Graph
Payment Graph
Mobile Graph
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Ref: http://www.gartner.com/id=2081316

4. 5 Graphs of Finance
• Payment Graph
(e.g. Fraud Detection, Credit Risk Analysis, Chargebacks...)
• Customer Graph
(org drillthru, product recommendations, mobile payments, etc.)
• Entitlement Graph
Finance
(identity & access management, authorization)
• Asset Graph
(portfolio analytics, risk management, market & sentiment
analysis, compliance)
• Master Data Graph
(enterprise collaboration, corporate hierarchy, data governance)
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5. #1: Payment Graph
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6. Neo Technology, Inc Confidential

7. #1: Payment Graph Example
The Payment Graph Depends on the Customer Graph (#2)
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8. #1: Payment Graph Example
Capturing B2B & B2C Transactions
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9. #1: Payment Graph Example
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10. #1: Payment Graph Example
Streamlined Management of Chargebacks
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11. #2: Customer Graph
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12. Neo Technology, Inc Confidential

13. #2: Customer Graph
The Corporate Hierarchy is Really a Graph
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14. #2: Customer Graph
Cleansing & Matching for 360 degree master view
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15. #2: Customer Graph
Bank Fraud Example: Ring of False Identities
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16. #2: Customer Graph
Bank Fraud Example: Combinatorial Multiplying of False Persona
3 phony addresses + 3 phony phone #s
= 9 phony customers.... and so on
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17. #3: Entitlement Graph
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18. http://blogs.gartner.com/ian-glazer/2013/02/08/killing-iam-in-order-to-save-it/
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19. Large
Investment
Bank
Industry: Financial Services
Use case: Entitlements/IAM
London
Background
• Top investment bank: over $1T in total assets
• Using a relational database coupled with Gemfire
for managing employee permissions to research
resources (documents and application services)
Business problem
Solution & Benefits
• When a new investment manager was onboarded,
• Organizational model, groups, and entitlements stored
permissions were manually provisioned via a complex
manual process. Traders lost an average of 5 days of
trading, waiting for the permissions to be granted
• Competitor had implemented a project to accelerate
the onboarding process. Needed to respond quickly.
• High stakes: Regulations leave no room for error.
• High complexity: Granular permissions mean each
trader needed access to hundreds of resources.
in Neo4j
• Very happy with the performance of the solution, and
the productivity advantage of a domain fit
• Graph visualization makes it easier for the business to
provision permissions themselves
• Moving to Neo4j meant “fewer compromises” than a
relational data store
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20. #4: Asset Graph
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21. #4: Asset Graph Examples
Portfolio Analytics
IT Asset Management
Risk Analysis
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22. #5: Master Data Graph
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23. Industry: Insurance
Use case: Master Data Management
Background
• German mid-size Insurance company
• Founded in 1858, over 500 employees
Business problem
Solution & Benefits
• Field sales unit needed easy access to policies and
• Enable field sales unit to flexibly search for
customer data, in an increasing variety of ways
• Needed to support a growing business
• Existing IBM DB2 system not able to meet
performance requirements as the system scaled
• 24/7 available system for sales unit outside the
company needed
insurance policies and associated personal data
• Raising the bar with respect to insurance industry
practices
• Suppor the business as it scales, with a high level of
performance
• Easy port of existing metadata into Neo4j
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24. IntercontinentalExchange
Social network for brokers
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25. Technology for
Managing Graphs
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26. Use SQL to Find all Dependencies
(up to 3 levels down)
(continued from previous page...)
(SELECT T.directReportees AS directReportees, sum(T.count) AS count
SELECT depth1Reportees.pid AS directReportees,
FROM (
count(depth2Reportees.directly_manages) AS count
SELECT manager.pid AS directReportees, 0 AS count
FROM person_reportee manager
FROM person_reportee manager
JOIN person_reportee L1Reportees
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
ON manager.directly_manages = L1Reportees.pid
UNION
JOIN person_reportee L2Reportees
SELECT manager.pid AS directReportees, count(manager.directly_manages) AS count
ON L1Reportees.directly_manages = L2Reportees.pid
FROM person_reportee manager
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
GROUP BY directReportees
GROUP BY directReportees
) AS T
UNION
GROUP BY directReportees)
SELECT manager.pid AS directReportees, count(reportee.directly_manages) AS count
UNION
FROM person_reportee manager
(SELECT T.directReportees AS directReportees, sum(T.count) AS count
JOIN person_reportee reportee
FROM(
ON manager.directly_manages = reportee.pid
SELECT reportee.directly_manages AS directReportees, 0 AS count
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
FROM person_reportee manager
GROUP BY directReportees
JOIN person_reportee reportee
UNION
ON manager.directly_manages = reportee.pid
SELECT manager.pid AS directReportees, count(L2Reportees.directly_manages) AS count
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
FROM person_reportee manager
GROUP BY directReportees
JOIN person_reportee L1Reportees
UNION
ON manager.directly_manages = L1Reportees.pid
SELECT L2Reportees.pid AS directReportees,
JOIN person_reportee L2Reportees
count(L2Reportees.directly_manages) AS
ON L1Reportees.directly_manages = L2Reportees.pid
count
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
FROM person_reportee manager
GROUP BY directReportees
JOIN person_reportee L1Reportees
) AS T
ON manager.directly_manages = L1Reportees.pid
GROUP BY directReportees)
JOIN person_reportee L2Reportees
UNION
ON L1Reportees.directly_manages = L2Reportees.pid
(SELECT T.directReportees AS directReportees, sum(T.count) AS count
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
FROM (
GROUP BY directReportees
SELECT manager.directly_manages AS directReportees, 0 AS count
) AS T
FROM person_reportee manager
GROUP BY directReportees)
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
UNION
UNION
(SELECT L2Reportees.directly_manages AS directReportees, 0 AS count
SELECT reportee.pid AS directReportees, count(reportee.directly_manages) AS count
FROM person_reportee manager
FROM person_reportee manager
JOIN person_reportee L1Reportees
JOIN person_reportee reportee
ON manager.directly_manages = L1Reportees.pid
ON manager.directly_manages = reportee.pid
JOIN person_reportee L2Reportees
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
ON L1Reportees.directly_manages = L2Reportees.pid
GROUP BY directReportees
WHERE manager.pid = (SELECT id FROM person WHERE name = "fName lName")
UNION
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)

27. Use Cypher to Find all Dependencies
(up to n levels down)
Using Cypher
MATCH
(fund)-­‐[:INCLUDES*0..n]-­‐>(sub),
(sub)-­‐[:INCLUDES*1..n]-­‐>(asset)
WHERE
fund.ticker
=
“TRLGX”
RETURN
sub.ticker
AS
Asset_Group,
count(asset)
AS
Total
ORDER
BY
Total
DESC
For a given fund, returns all assets that
are made up of other assets,
ordered by the total # of included assets
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28. Connected Data & Query Performance
RDBMS vs. Native Graph Database
Response Time
1000x faster
Degree: Thousands+
Size: Billions+
# Hops: Tens to Hundreds
RDBMS
Degree: < 3
Size: Thousands
# Hops: < 3
Neo4j
Connectedness of Data Set
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29. Top Reasons for Choosing Neo4j
1. Problems with JOIN performance
2. Domain fit for graph
3. Open-ended business requirements
necessitating fast, iterative development
4. Evolving and/or non-uniform data,
avoiding sparse tables and frequent schema
chances
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30. Who’s Using Graph Databases Today?
Finance
Finance
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Accenture

31. Thanks!
Stay Connected
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