FPS Finance - The Power of Social Network Analysis Explained · Tweet your answer: Example:...

Turning Data into Value

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

Tweet about it!

#SFBL14

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

Six Degrees of Kevin Bacon

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.

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/

Used by Government, Banks, Insurers to detect fraud

Network

Automated

Business Rules

Anomaly

Detection

Predictive

Modeling

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?

What are Networks? More than nodes and links…

Individuals

Fuzzy Match

Address

Transaction

High riskLow risk

Foreign Company

Local Company

Ownership

SAS® Social

Network

Analysis

NetworkAnalytics

NetworkScoring

Business

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)

“HOW FPS FINANCES PROACTIVELY PREVENTS FRAUD

WITH BIG DATA TOOLS”

SAS FORUM

Yannic HULOT

Inspection Spéciale des Impôts

Director

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

Special Tax Inspectorate

SAS tools

1.Making your analysis decisive

2.Giving your inspectors ammunition before the

investigation begins

Signals

The mass of data was a problem

This becomes a solution

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

Twitter Contest – Tweet to win prizes!SAS Forum

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!

FPS Finance - The Power of Social Network Analysis Explained · Tweet your answer: Example:...

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