Using Data Analytics to Detect Fraud€¦ · © 2018 Association of Certified Fraud Examiners, Inc....

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© 2018 Association of Certified Fraud Examiners, Inc. Using Data Analytics to Detect Fraud Effectively Communicating the Results of Data Analytics

Transcript of Using Data Analytics to Detect Fraud€¦ · © 2018 Association of Certified Fraud Examiners, Inc....

© 2018 Association of Certified Fraud Examiners, Inc.

Using Data Analytics to

Detect Fraud

Effectively Communicating the

Results of Data Analytics

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Introduction

▪ Graphical representation of data, results, or

other information

▪ Blend of art and science

▪ Requires communication skills and design

skills

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Objectives

▪ Provide clarity

▪ Provide context

Source: www.thisisindexed.com

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COMMUNICATION

PRINCIPLES

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Principle 1: Focus on the Purpose

▪ Common purposes:

• Analyze

• Educate

• Persuade

• Entertain

▪ What is the analyst trying to accomplish?

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Principle 2: The Audience Is

More Important Than Data

▪ Audience considerations

• Internal versus external

• Emotional versus rational

• Agreeable versus confrontational

• Novice versus expert

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Principle 3: Be Selective—Not

All Data Is Equal

▪ No rule requiring that everything be presented

▪ What is relevant?

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Sunday Monday Tuesday Wednesday Thursday Friday Saturday

459

7,515

5,295

8,1508,648

6,662

1,204

Transaction Date9K

8K

7K

6K

5K

4K

3K

2K

1K

0K

Nu

mb

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of

Reco

rds

Example 1

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Example 2

Sunday Saturday

459

1,204

Transaction Date

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Principle 4: Think Visually

▪ Visuals can complement other elements of a

report.

▪ Narratives can be supplemented with a

visual.

▪ Data tables can be replaced with a visual.

▪ Each situation is unique.

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Principle 5: Determine the Data’s Role

▪ Detailed data is not required.

▪ Ease of use is a consideration.

▪ Data can be requested later for deeper

analysis.

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DESIGN ELEMENTS

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Element 1: Message

▪ Determine the overall message the data viz

is trying to communicate.

▪ Data itself is neutral.

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Element 2: Simplicity

▪ Carefully consider all elements of the design.

▪ Remove unnecessary chart junk.

▪ Ask: “Is there anything else that can be

removed?”

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Element 3: Accessories

▪ Includes all the extras on a visualization:

• Shapes

• Position

• Axis titles

• Data tables

• Trend lines

▪ Not always necessary

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Element 4: Color Selection

▪ Key considerations:

• Color blindness

• Printing of visualization (color or black and white)

• Predefined meaning

• Complementary or divergent

▪ Color directly impacts readability of visualization.

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Element 5: Visual Type

▪ Specific types have specific purposes.

▪ Choosing a chart because it is “fun” might

detract from the message.

▪ Be intentional about the visual type.

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Application to Fraud Examiners

▪ Visualizations are used throughout a fraud

examination:

• Planning

• The examination itself

• Reporting

• Trial and testimony

▪ All uses require slightly different approaches.