deloitte dbrief - visual analytics: an enhanced view into corruption, fraud, waste, and abuse?
DESCRIPTION
This is the slide deck from the 5/11/11 Deloitte Dbrief entitled, Visual Analytics: An Enhanced View into Corruption, Fraud, Waste, and Abuse? More companies are employing various data-driven analytic techniques and methods, including visual analytics, for business intelligence purposes. How can visualization be employed to analyze potential corruption, fraud, waste, and abuse? The panelists discussed using visual analytics to identify business risks; visual analytics in the analysis of unstructured data, such as emails and instant messages; and enhancing continuous monitoring capabilities with visual analytics, and providing clarity to predictive and advanced analytics through visualization.TRANSCRIPT
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
Copyright © 2011 Deloitte Development LLC. All rights reserved.
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
Copyright © 2011 Deloitte Development LLC. All rights reserved.
A new visual lexicon is evolving to communicate
rich information about increasingly complex data
Social networks and
community detection
Word clouds/tag clouds
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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
Copyright © 2011 Deloitte Development LLC. All rights reserved.
Exploring transactional data using visual analytic
techniques
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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
Copyright © 2011 Deloitte Development LLC. All rights reserved.
Enhancing your view into corruption,
fraud, waste and abuse
1
Source: http://newsmap.jp/#/b,m,n,s,t,w/us/view/all/fraud/
Copyright © 2011 Deloitte Development LLC. All rights reserved.
• According to the ACFE, 40% of occupational fraud is
identified via tip 1
• Increased regulatory requirement
• Limited resources (time, budget and people)
Enhancing your view into corruption,
fraud, waste and abuse
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
Copyright © 2011 Deloitte Development LLC. All rights reserved.
Enhancing your view into corruption,
fraud, waste and abuse
V
i
s
u
a
l
A
n
a
l
y
t
i
c
s
Applications of visual analytics
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Link analysis
• Unexpected relationships
• Anti-money laundering
• Social Networks
• e-Discovery
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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
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Applying modular algorithms and profiles to detect
candidate high-risk transactions
Suspect transactions
revealed by applying:
• Classifications
• Scoring algorithms
• Complex profiles
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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
Copyright © 2011 Deloitte Development LLC. All rights reserved.
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!
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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
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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
Copyright © 2011 Deloitte Development LLC. All rights reserved.
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.
Copyright © 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
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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.
HYPOTHETICAL SCENARIO
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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/
HYPOTHETICAL SCENARIO
Copyright © 2011 Deloitte Development LLC. All rights reserved.
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.
HYPOTHETICAL SCENARIO
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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
HYPOTHETICAL SCENARIO
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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
Copyright © 2011 Deloitte Development LLC. All rights reserved.
• Exploring, not just visualizing the data
• One size does not fit all
• Structured and unstructured data
Visual Analytics – Many possibilities
Questions and Answers
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Are you interested in receiving follow up articles and white
papers surrounding today’s topic from Deloitte?
• Yes
• No
• NA
Poll question #5
Join us June 8th at 2 PM ET as our Transactions & Business Events series presents:
Delivering Value through M&A: Improving Corporate Development Effectiveness
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CPE certificates are now available
for immediate download.
Click the Request CPE link in the
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Anthony DeSantis
Senior Manager
Deloitte Financial Advisory Services LLP
+1 212 436 3307
Matt Gentile
Principal
Deloitte Financial Advisory Services LLP
+1 571 882 6880
Richard Simon
Senior Manager
Deloitte Financial Advisory Services LLP
+1 212 436 3438
David Williams
Chief Executive Officer
Deloitte Financial Advisory Services LLP
+1 212 492 2879
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