FPS Finance - The Power of Social Network Analysis Explained · Tweet your answer: Example: @spicyanalytics 3C Prizes to win: 1st prize: a ticket for Analytics 2015 2nd prize: a book

Copyright © 2014, SAS Institute Inc. All rights reserved.

Turning Data into Value

The Power of Social Network Analysis ExplainedYannic Hulot – Conseiller Général – Directeur ISI SPF FinanceJulie Coyette – Senior Consultant in Analytics -SAS

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Copyright © 2014, SAS Institute Inc. All rights reserved.Copyright © 2014, SAS Institute Inc. All rights reserved.



http://en.wikipedia.org/wiki/Six_Degrees_of_Kevin_Bacon

Six Degrees of Kevin Bacon

http://en.wikipedia.org/wiki/Six_Degrees_of_Kevin_Bacon


Six degrees of separation

Six degrees of separation

is the theory that everyone

and everything is six or

fewer steps away, by way

of introduction, from any

other person in the world,

so that a chain of "a friend

of a friend" statements can

be made to connect any

two people in a maximum

of six steps.

http://en.wikipedia.org/wiki/Theory

http://en.wikipedia.org/wiki/Friend_of_a_friend


A real Network


From Super Clusters… To Relevant Networks!


1. Why should you care about Network Analysis?

• Improved Fraud Detection (Government, Insurance, Banking)

• Better Churn Model (Telco)

2. What are the key concepts in Network Analysis?

3. How to extract relevant networks from the super cluster?

4. How is it used by FPS Finance to detect complex and organized Tax Fraud?

Agenda = 4 Questions










Social Network Analysis =

What is SNA?

Data Analysis from social network sites like Facebook

and Twitter?

Data mining technique that explores the patterns

between people, companies (or other entities) in a

network or group


Used by google to rank web pages (Google Page Rank)

Applications of Network Analysis


Used by GPS to find the shortest path



Used in Biology



In the News…



Used by Telco to prevent churn



How? Attributes derived from SNA may be used alone or as input to classical

predictive models to help improve their accuracy.

SNA to improve churn models

Fact 2: Network models can detect other

types of churners compared to traditional

models

Fact 1a: Customers are influenced by

friends within the network and by friends of

friends

Fact 1b: Incorporating the impact of higher

order leads to improved predictors and

profits

Fact 3: A customer with canceller in their

network churn at three times the rate

http://blogs.sas.com/content/sascom/2011/10/25/using-social-network-analysis-to-predict-churn-in-telco/

http://blogs.sas.com/content/sascom/2011/10/25/using-social-network-analysis-to-predict-churn-in-telco/


Used by Government, Banks, Insurers to detect fraud



Network

score

Automated

Business Rules

Anomaly

Detection

Predictive

Modeling

Text

Mining Database

Searches

Social

Network

Analysis

SNA for Fraud Detection – Hybrid Approach


More prioritized Fraud, Waste and Abuse cases identified

• Including both previously undetected entities and networks and extensions to already identified cases

Reduction in false positive rates

• Hybrid approach reduces false positives by up to 10+ times over traditional rules-based approaches

Improved analyst / investigation efficiency

• Each alert takes 1/2 – 1/3 of the time to investigate due to data aggregation and visualization

• Provides alert logic and suggested path to initiate investigation

Significant increase in ROI per analyst / investigator

Why SAS SNA for Fraud Detection?










Key Concepts in Network Analysis


What are Networks?

NODES

LINKS


What are Networks? More than nodes and links…

Individuals

Fuzzy Match

Address

Link

Transaction

High riskLow risk

Foreign Company

Local Company

Link

Ownership










SAS® Social

Network

Analysis

NetworkAnalytics

NetworkScoring

Business

Rules

Analytics

AnomalyDetection

PredictiveModeling

How to extract relevant networks from the super cluster?


One of the most important objectives in network analysis is the detection of cohesive, self-

contained structures known as Communities. These are defined intuitively as groups of nodes

that are more tightly connected to each other than they are to the rest of the network.

Network Analytics - Community Detection


Claim Network

• Claims

• Insured

• Address

• Employer

• Account Number

Network Analytics – Community Detection



Louvain Algorithm for Community Detection


The centrality of a node or link in a graph gives some indication of its relative importance within a graph.

Many different types of centrality metrics are used to better understand levels of prominence.

Great measures to improve your marketing campaigns!

Target customer with high centrality scores and let them speak about it via word of mouth…

Prioritize who to contact (strong influencers, followers)

Network Analytics – Centrality Scores

Source: http://www.forteconsultancy.wordpress.com



Counts the number of times a

particular node (or link) occurs on

shortest paths between other nodes.

Number of links connected to it or

number of direct relationships that an

entity has

Reciprocal of the average of the

shortest path to all other nodes.

Extension of degree centrality in which

centrality points are awarded for each

neighbor. It is equal to the sum of the

scores of all nodes connected to it

Degree

Betweenness

Closeness

Eigenvectors

(PageRank)

Number of links connected to it or

number of direct relationships that an

entity has

Counts the number of times a

particular node (or link) occurs on

shortest paths between other nodes.

Reciprocal of the average of the

shortest path to all other nodes.

Extension of degree centrality in which

centrality points are awarded for each

neighbor. It is equal to the sum of the

scores of all nodes connected to it



Closeness = ?

Betweenness = ?

Degree = ?

Eigenvectors = ?

Source: http://en.wikipedia.org/wiki/Centrality



Closeness = B

Betweenness = C

Degree = A

Eigenvectors = D

Source: http://en.wikipedia.org/wiki/Centrality


Network Analytics – Centrality Scores (Illustration)









36

“HOW FPS FINANCES PROACTIVELY PREVENTS FRAUD

WITH BIG DATA TOOLS”

SAS FORUM

2014

Yannic HULOT

Inspection Spéciale des Impôts

Director

37

Big Data in the FPS Finance

1. A major challenge

2. Seeing the invisible

3. New data incoming

4. Risk : be sitting on a mountain of gold

5. Time is running

6. No capacity to manage

DATA FRAUD FOOTPRINTS

Data management

Data manipulation

Matching

Cross-checking

…..

Predictive models

Networks

The Fraud Framework

An integrated solution Speedness

Swiftness

Powerness

38

Special Tax Inspectorate

And

SAS tools

1.Making your analysis decisive

2.Giving your inspectors ammunition before the

investigation begins

39

Fra

ud

Pro

pe

ns

ity

Signals

5%

95%

The mass of data was a problem

This becomes a solution

40

SAS® Social

NetworkAnalysis

NetworkAnalytics

NetworkScoring

BusinessRules

Analytics

AnomalyDetection

PredictiveModeling

Fuzzy Matching

Reducing the Super Cluster

Why so efficient inside the STS ?

• Mechanism and schemes = networks

• Criminal organisations = networks

• Not a narrow view

• Taking everything into account without knowing all

• Inter agency

• International contexts

41


Twitter Contest – Tweet to win prizes!SAS Forum

A. 6

B. 3

C. 4,74

5. We live in a very connected world, with the increasing popularity of

Facebook and Twitter, what is the maximum distance between any two

individuals in the world?

Tweet your answer:

Example: @spicyanalytics 3C

Prizes to win:

1st prize: a ticket for Analytics 2015

2nd prize: a book of Prof Bart Baesens: “Analytics in a big

data world”

3rd to 30th prize: chocolates with pepper

Winners will be contacted post-Forum !

Start of your tweet Question # Your answer





Thank You!

See You Next Year!

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FPS Finance - The Power of Social Network Analysis Explained · Tweet your answer: Example: @spicyanalytics 3C Prizes to win: 1st prize: a ticket for Analytics 2015 2nd prize: a book