This document discusses how graphs and graph databases can be used for financial services applications. It provides three potential use cases: 1. Measuring risk in corporate lending by understanding the relationships between businesses and how they are connected through partnerships, supply chains, etc. 2. Understanding exposure to commodities or industries by analyzing how businesses and customers are related to different commodities and how changes in commodities may impact them. 3. Predicting customer behavior for investment banking by using graph analysis to better understand customer relationships and trading patterns to anticipate future wants and needs.

1. Graphs and Innovative Graph
Solutions for Financial Services
Christopher Upkes
Senior Field Engineer

2. What are we going to do?
• Why graphs?
• What do we use graphs for?
• Graph Theory explained
• Extending Practical Graph Use: The Property Graph Model Revolution
• Use Case 1: Measuring risk in corporate lending
• Use Case 2: Understanding the exposure to commodities or industries
• Use Case 3: Predicting behavior of customers

3. Why graphs?

4. The world is a graph – everything is connected
• people, places, events
• companies, markets
• countries, history, politics
• sciences, art, teaching
• technology, networks, machines,
applications, users
• software, code, dependencies,
architecture, deployments
• criminals, fraudsters and their behavior

5. What Industry Analysts are Communicating
about Graph Database
• “Graph analysis is possibly the single most effective competitive
differentiator for organizations pursuing data-driven operations and
decisions after the design of data capture.”
• “Graph analysis is the true killer app for Big Data.”
• “…(Software) vendors are investing in Graph Databases.”
• “Graph repositories help MDM better connect conceptual master data
models with logical models…that RDBMS repositories often constrain.”
Private & Confidential 5

6. What are people using
graph databases for?

7. Use Cases
Internal Applications
Master Data Management
Network and
IT Operations
Fraud Detection
Customer-Facing Applications
Real-Time Recommendations
Graph-Based Search
Identity and
Access Management

8. How Customers Use Neo4j
Network &
Data Center
Master Data
Management
Social Recom–
mendations
Identity
& Access
Search &
Discovery
GEO

9. Social Network

10. Impact Analysis

11. Logistics & Routing

12. Recommendations

13. Fraud Analysis

14. Access Control

15. Graph Theory

16. The Koningsberg Bridge Problem
The basic idea of graphs were introduced in 18th
century by the great Swiss mathematician Leonhard
Euler.

17. Breaking down the “bridge problem”
• The Euler cycle (or tour) problem: Is it possible to traverse each of
the edges of a graph exactly once, starting and ending at the same
vertex?
• The Hamiltonian cycle problem: Is it possible to traverse each of the
vertices of a graph exactly once, starting and ending at the same
vertex?

18. Essential Graph Types

19. What’s all the fuss?
• Vertex = a point where two lines meet
• Edge = lines that join to form vertices

20. Neo4j Fundamentals

21. What if?
• A vertex could represent something more concrete?
• A customer
• A business
• An asset
• What if edges could provide more context
• Cash flow
• A Transaction (withdraw, deposit)
• A meaningful interaction (borrows from, supplies to)

22. What if I could view all of my business data as a graph?

23. Enter the property graph model

24. The Neo4j Labeled Property Graph
• Nodes
• Relationships
• Properties
• Labels

25. Complex Graph Types
• Directed Graph or Digraph

26. Complex Graph Types
• Property Graph

27. Car
Property Graph Model Components
Nodes
• Represent the objects in the graph
• Can be labeled
Person Person

28. Car
Property Graph Model Components
Nodes
• Represent the objects in the graph
• Can be labeled
Relationships
• Relate nodes by type and direction
LOVES
LOVES
LIVES WITH
Person Person

29. Car
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
Property Graph Model Components
Nodes
• Represent the objects in the graph
• Can be labeled
Relationships
• Relate nodes by type and direction
Properties
• Name-value pairs that can go on
nodes and relationships.
LOVES
LOVES
LIVES WITH
Person Person

30. Commerical Lending
Risk Management

31. 31
• Character - The Borrower’s history of how they pay their bills;
• Capacity - The quantitative measure of whether the Borrower has
sufficient income to pay its debts;
• Capital - Also known as equity. Generally, the higher the equity
contribution, the lower the risk to the Bank;
• Collateral - An alternative source of repayment if cash flow cannot
be relied upon to pay the debt; and
• Conditions - Also known as loan structure. This refers to both the
return built into the investment (the interest rate being charged) as
well as the conditions placed on the accommodation to ensure the
Bank is repaid (hopefully in full!).
Commercial Lending and Risk

32. 32
We need a better understanding of the business “community” to understand
how business relationships are affecting loan risk.
Problem Statement:

33. 33
”Knowing your customers and the markets in which they invest are two of the
simplest and most basic tools of understanding risk. Living, working and playing
in the same community as your Borrower is a surefire way to know their
business environment.”
CNB University
Problem Statement:

34. 34
The Business Ecology
CUST_ID RATING RATEDATE
1 3 011518
2 3 020618
3 2 011218
4 1 020618
PAYID RCPID TX_TY
PE
AMT
1 7 Auto 10000.00
2 12 Auto 5000.00
3 2 Manual 15034.00
4 6 Auto 24000.00

35. 35
The Business Ecology
PAYID RCPID TOTALAMT
1 7 122357.00
2 12 17600.00
3 2 95034.00
4 6 44000.00

36. 36
The Business Risk Community

37. 37
The Business Risk Community

38. 38
The Business Risk Flow

39. 39
The Business Risk Critical Flow

40. Business Banking
Industry / Commodity Impact Analysis

41. Canadian Bank Credit Exposure > $100 Billion

42. 42
We need more ways to determine our credit exposure to commodities and oil
in particular
Problem Statement:

43. 43
Commodities Impact Industries You Might Not Expect
• Do you know how all of your customers are connected, directly or indirectly to the
oil industry?
• How will a precipitous drop in oil prices affect the businesses that you invest in or
provide credit to?
Business Banking and Commodities

44. 44
Businesses function in a “community” and are
“related” in different ways
Business
Community
Graph
Loan
Data
Business
detail
Data
Transaction
Data

45. 45
• Lends to
• Owns part of
• Co-Borrower of
• Subsidiary of
• Supplies to
• Provides Services to
Relationships between business entities

46. 46
• Company A Lends to Company B
• Company B Owns Part of Company C
• Company C Co-Borrower of Company D
• Company D Subsidiary of Company B
• Company D Supplies to Company A
• Company E Provides Services to Company C
Relationships between business entities

47. 47
Relationships between business entities
LENDS_TO
SERVICES
SUPPLIES

48. 48
The Business Community

49. 49
Cross Industry Impact Analysis

50. Investment Banking
Predicting Customer Behavior

51. 51
“Underwriting involves assuming a risk on behalf of an investor selling securities in the
stock market. The company wants an assurance that money is raised for the stock
issued even when some shares have not been sold. Investment bankers place their
capital risk by buying all the securities from the company at a discounted price. Bankers
then mark up the securities to market retail price and sell them to the investing public.
If the sale of securities does not reach the target capital requirement, bankers are
forced to borrow from commercial banks or sell the shares at a loss. Alternatively, they
also invite other investment banks to take part in the underwriting process to share the
risk of loss in form of an underwriting syndicate.”
Joseph Petrick,
Houston Chronicle
Investment Banking / Underwriting and Selling

52. 52
We need to be better prepared to support our customers trades so that we
are less likely to suffer a loss. To do that, we need to better predict when and
what our customers are likely to want to buy or sell.
Problem Statement:

53. 53
Investment Banking / Underwriting and Selling

54. 54
Investment Banking / Underwriting and Selling
External Publicly Available Data

55. 55
Investment Banking / Underwriting and Selling
External Publicly Available Data Internal Application Data

56. Thank you for your time!
Questions?