Deloitte Dbrief - Visual Analytics: An Enhanced View into Corruption, Fraud, Waste, and Abuse?

Transactions & Business Events presents:

Visual Analytics:

An Enhanced View into Corruption,

Fraud, Waste and Abuse

Anthony DeSantis, Senior Manager, Deloitte Financial Advisory Services LLP

Matt Gentile, Principal, Deloitte Financial Advisory Services LLP

Richard Simon, Senior Manager, Deloitte Financial Advisory Services LLP

David Williams, Chief Executive Officer, Deloitte Financial Advisory Services LLP

May 11, 2011

Copyright © 2011 Deloitte Development LLC. All rights reserved.

Introduction

Visual analytics: It’s all about the data

Enhancing your view into corruption, fraud,

waste and abuse

Applications of visual analytics

Q&A

Agenda


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Release for answers to polling questions


Has your company ever used visual analytics techniques to

identify corruption, fraud, waste and abuse?

• Yes

• No

• NA/Don’t know

Poll question #1

Visual Analytics:It’s all about the data


A new visual lexicon is evolving to communicate

rich information about increasingly complex data

Social networks and

community detection

Word clouds/tag clouds


Visual analytics is the science of analytical reasoning

facilitated by interactive visual interfaces

Visualanalytics

Analyticalreasoning

Data representation

Production and dissemination

Exploration

Interpretation

Decision-making

Statistics

Relationships

Data reduction

Cognition

Visual queries

Interactive feedback

Monitoring

Storytelling

Communicating

Framework adapted from Linköping University, itn.lie.se/mit/research/information-and-geo-visualization/l=en

Visualrepresentation


Exploring transactional data using visual analytic

techniques


What is the most advanced visual analytic tool at your

company?

• MS Excel

• Business intelligence platforms

• Specialized analytic software

• Custom software

• NA/Don’t know

Poll question #2

Enhancing your viewinto corruption, fraud,waste and abuse


Enhancing your view into corruption,

fraud, waste and abuse

1

Source: http://newsmap.jp/#/b,m,n,s,t,w/us/view/all/fraud/


• According to the ACFE, 40% of occupational fraud is

identified via tip 1

• Increased regulatory requirement

• Limited resources (time, budget and people)



Doing More

With Less

Volume of Data

Technology &

Adoption

• Every day, more and more electronic data is created

• Finding a needle in the haystack

• Rules-based approaches may only take you so far

• BI tools and other data mining software include visual

analytic capabilities

• Analysts becoming accustomed to viewing data visually

• Identify complex trends, patterns or anomalies1. ACFE 2010 Report to the Nations on Occupational Fraud and Abuse




V

i

s

u

a

l

A

n

a

l

y

t

i

c

s

Applications of visual analytics


Link analysis

• Unexpected relationships

• Anti-money laundering

• Social Networks

• e-Discovery


Beyond link analysis

Doing More

With Less

Doing More

With Less

Doing More

With Less

Treemaps

• Accommodate hundreds or

thousands of data points in one

screen

• Patterns and exceptions can be

spotted in seconds

• Multiple dimensions can be displayed

at the same time

• Further enhanced with sliders and

drilldown capabilities

-1

US

China

UK

Germany

Brazil

Percent Change


Applying modular algorithms and profiles to detect

candidate high-risk transactions

Suspect transactions

revealed by applying:

• Classifications

• Scoring algorithms

• Complex profiles


Spatial analysis gets its start – proximity & correlation!

In 1854, John Snow depicted a

cholera outbreak in London

using points to represent the

locations of some individual

cases, possibly the earliest use

of the geographic method. His

study of the distribution of

cholera led to the source of the

disease, a contaminated water

pump (the Broad Street Pump,

whose handle he had

disconnected, thus terminating

the outbreak) within the heart of

the cholera outbreak.

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

http://en.wikipedia.org/wiki/John_Snow_(physician)

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

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


Racial breakdown of Chicago.

Red is White (42%)

Blue is Black (36.8%)

Green is Asian (4.4%)

Orange is Hispanic (26%)

Gray is Other

Each dot = 25 people Data from Census 2000 & 2010.

Base map © OpenStreetMap, CC-BY-SA

— Source: Eric Fischer

http://www.radicalcartography.net/index.html?chicagodots

No longer just static pictures –

dynamic data!

http://www.radicalcartography.net/index.html?chicagodots


Geospatial Analytics the collection,

visualization and analysis of data that can be tied to

a geographic location on, above or below the

Earth’s surface.

>80%of all data now has

a location

component

“Everything is related to everything else, but near

things are more related than distant things.”

Source: Tobler’s first law of geography


How do we get there? Where is fraud, waste & abuse most

likely to occur?

Where is the greatest concentration of

risk and opportunity?

Where to deploy my assets, tools and

resources to achieve the greatest

result or reach the greatest number of

customers?

How can I shape policy to influence

geographically localized behaviors?

Financial data

Transactions

Institutions

People

Places

Goods/Materials

Contracts

Addresses/lat/long

Territories

Geospatial

Intelligence

Business

Intelligence &

Financial

Intelligence

Geo

-An

aly

tics

Source: Sean Gorman, GeoIQ


Which is the most important reason to use geospatial

analytics in regards to mitigating corruption, fraud, waste and

abuse at your company?

• To determine where corruption, fraud, waste & abuse are

most likely to occur

• To determine where the greatest concentration of risk and

opportunity lies

• To determine where to deploy assets, tools and resources

to achieve the greatest result or reach the greatest number

of customers

• To determine how to shape policy to influence geographic

consumer behaviors

• NA/Don’t know

Poll question #3

Applied geospatial example: Medicare fraud

© 2011 Deloitte Touche Tohmatsu

A Geographic Information System (GIS)

Transforms tabular data into location information

(25.77981, -80.25141)

Coordinates are assigned to spreadsheet data through a process called geocoding.

This is a preliminary step in the analysis to identify fee-for-service fraud, such as

Medicare fraud. The red points below symbolize the location of clinics in Miami, FL:

HYPOTHETICAL SCENARIOCopyright © 2011 Deloitte Development LLC. All rights reserved.


Features can be symbolized based on underlying data

The same clinics are now represented by location and by the number of Medicare

claims filed and a visual story begins to unfold.

HYPOTHETICAL SCENARIO


43,000 claims

24,000 claims

7,000 claims

12,000 claims

18,000 claims

19,000 claims

10,000 claims

Features can be symbolized based on underlying data

A suspected bad actor is visually identified, but additional data and information is

needed before moving forward.



GIS integrates disparate data by overlaying multiple data sets

The heat map below illustrates the prevalence of people over 65 years old and low

income (highest concentration in red)*. The clinic in question is even more conspicuous

because it does not reflect the socio-demographic profile of a beneficiary.

Multiple data sets can be combined to

create a service beneficiary (patient)

profile using:

•Socio-demographic information

•Travel Distance/Time Data

•Primary Care Service Area Data

•Patient Address Information

*Source: http://factfinder.census.gov/



We can also overlay market segmentation data on the clinic locations and run spatial

queries to determine statistically significant relationships between a clinic location and

the surrounding neighborhoods.

GIS integrates disparate data by overlaying multiple data sets

According to the market

segmentation* data, area #12

characterizes:

•―Up and Coming Families‖

•Average age of 39.1 years

•Upper-middle class

•Predominately White

•Single-family units*Source: http://www.esri.com/data/esri_data/tapestry.html

Market demographics

are incongruous with

billing patterns.



Functionality for conducting robust statistical analysis is

built-In

Where are the outliers? By combining clinic location data ancillary socio-demographic

data we can conduct a cluster/outlier analysis that can help us statistically find

anomalous billing patterns when visual relationships are not visually discernable.

A cluster analysis of patient

addresses (light blue points) reveals:

•Patients are all retirement/nursing

home residents

•Patients are low income senior

citizens

•They are outside of the average

drive-time distance to a clinic

We can test our hypothesis using a

variety of statistical tools built into

GIS, including:

•Nearest Neighbor Analysis

•Spatial Autocorrelation

•Outlier Analysis

•Linear Regression Analysis

•Cluster Analysis



How much of an impact will the use of visual analytics to

discover fraud have on companies in the next 5 years?

• Significant impact

• Somewhat of an impact

• Little to no impact

• NA/Don’t know

Poll question #4


• Exploring, not just visualizing the data

• One size does not fit all

• Structured and unstructured data

Visual Analytics – Many possibilities

Questions and Answers


Are you interested in receiving follow up articles and white

papers surrounding today’s topic from Deloitte?

• Yes

• No

• NA

Poll question #5

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Anthony DeSantis

Senior Manager

Deloitte Financial Advisory Services LLP

+1 212 436 3307

[email protected]

Matt Gentile

Principal


+1 571 882 6880

[email protected]

Richard Simon

Senior Manager


+1 212 436 3438

[email protected]

David Williams

Chief Executive Officer


+1 212 492 2879

[email protected]

Contact info

mailto:[email protected]





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Deloitte Dbrief - Visual Analytics: An Enhanced View into Corruption, Fraud, Waste, and Abuse?

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Transcript of Deloitte Dbrief - Visual Analytics: An Enhanced View into Corruption, Fraud, Waste, and Abuse?