how to apply graph analytics for bank loan fraud detection?

How to apply graph analytics for bank loan fraud detection?

SAS founded in 2013 in Paris | http://linkurio.us | @linkurious

WHAT IS A GRAPH?

Father Of

Father Of

Siblings

This is a graph

WHAT IS A GRAPH : NODES AND RELATIONSHIPS

Father Of

Father Of

Siblings

A graph is a set of nodes linked by relationships

This is a node

This is a relationship

People, objects, movies, restaurants, music

Antennas, servers, phones, people

Supplier, roads, warehouses, products

Graphs can be used to model many domains

DIFFERENT DOMAINS WHERE GRAPHS ARE IMPORTANT

Supply chains Social networks Communications

But why can graphs can help identify fraud?

GRAPH AND FRAUD DETECTION

AITE Group estimates that first party fraud will cost $28.6 billion in credit card losses a year by 2016.

THE COST OF FRAUD

A look at a common fraud scenario banks face

A COMMON FRAUD SCENARIO

Create a fake identity

Go to the bank, ask for a loan

Disappear with the money

A criminal uses the fake identity to register a bank

account. He acts like a normal customer and tries to

secure a loan

Once the criminal feels he cannot get access to more

money he carefully prepares his exit : in a short amount of

time he empties all of his accounts and disappears

A criminal or a group of criminal mix pieces of

information (addresses, phone numbers, social

security number) to create a “synthetic-identity”

THE PAINS OF WORKING ON CONNECTED DATA WITH RELATIONAL TECHNOLOGIES

Relational databases are not good at handling... relationships

Depth RDBMS execution time (s) Neo4j execution time (s) Records returned

2 0.016 0.01 ~2500

3 30.267 0.168 ~110 000

4 1543.505 1.359 ~600 000

5 Unfinished 2.132 ~800 000

Finding extended friends in a 1M people social network (from the book Graph Databases)

Loan$25k

Home address58, Eisenhower Square

A GRAPH DATA MODEL FOR FRAUD DETECTION

Customer nameJ. Smith

Phone number+33 5 68 98 25 74

The first step to detect fake identity is to use a graph to model customer information

Credit card1 234$

IDJ. Smith

A graph showing a legitimate customer and the information she is linked to

In a fraud ring people share the same information

A LOOK AT A FRAUD RING THROUGH GRAPH VISUALIZATION

58, Eisenhower Square

14, Roses Street

+33 6 75 89 22 14

$7k

P. Martin

$12,5k +331 42 58 66 00

J. Smith

SSN 17873897893

8, Sugar Hill Street

$20k

E. Selmati

SSN 1787576553

$45k

P. Smith

SSN 1787579953SSN 1267576553

8, Coronation Street

HOW TO APPLY IT IN THE REAL WORLD

Graph databases makes it possible to identify the fraud patterns in real-time

Lifecycle events trigger security checks

A new customer opens an account

An existing customer asks for a loan

A customer skips a loan payment

A Neo4j Cypher query runs to detect patterns

The bank can make an informed decision

The fraud teams acts faster and more fraud cases can be

avoided.

WHAT IS THE IMPACT OF LINKURIOUS

If something suspicious comes up, the analysts can use Linkurious to quickly assess the

situation

Linkurious allows the fraud teams to go deep in the data and build cases against fraud

rings.

Treat false positives

Investigate serious cases

Save money

Linkurious allows you to control the alerts and make sure your customers are not

treated like criminals.

DEMO

Go to linkurio.us to try it!

TECHNOLOGY

Cloud ready and open-source based

OTHER USE CASES

Graphs are everywhere, learn to leverage them

CONCLUSION

Contact us to discuss your projects at contact@linkurio.us

Detailed use case on our blog :

● Part 1 : http://linkurio.us/how-to-detect-bank-loan-fraud-with-graphs-part-1/● Part 2 : http://linkurio.us/how-to-detect-bank-loan-fraud-with-graphs-part-2/● Neo4j data set : https://www.dropbox.com/s/wk8k5r23syp6kbx/fraud%20detection.zip

GraphGist by Kenny Bastani : http://gist.neo4j.org/?github-neo4j-contrib%2Fgists%2F%2Fother%2FBankFraudDetection.adoc

Video demonstration : https://vimeo.com/76891393 (around the 12 minutes mark)

ADDITIONAL RESOURCES