The webinar covers the challenges and methodologies of detecting fraud using data analytics, emphasizing that it is not a simple process and requires substantial analysis. Participants must log in individually to receive CPE credits, answer polling questions, and follow specific guidelines to avoid technical issues. The document outlines the importance of understanding data cleaning, anomaly detection, and the necessity of using advanced data analytic tools to identify potential fraud effectively.

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THE TRUTH BEHIND DETECTING FRAUD
USING DATA ANALYTICS
WEBINAR
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PRESENTER
Sunder Gee
Consultant, CPA, CMA, CIDA
AGENDA
• The Myth
• What is Data Analytics
• Leveraging the features of data analytics
• Why You May Need Data Analytic Tools
• The Reality
• Examples

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THE MYTH
• Detecting fraud using data analytics is just a simple mouse
click away
• Simplistic presentation and article titles perpetuate the
myth
PERPETUATING THE MYTH
• Bad title: “Detecting Fraud Using Data Analytic
Tools”
• International Workshop on ICT for Auditing, Lisbon,
Portugal – June 21, 2017
• Better title: “Fraud and Fraud Detection: A Data
Analytics Approach”
• Published by John Wiley & Sons, Inc., Hoboken, New
Jersey, ISBN 978-1-118-77965-1
• Best title: “Detecting Anomalies Using Data
Analytic Tools for Internal Auditors”

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WHAT IS DATA ANALYTICS?
• Data analytics is the process of examining data in order to
formulate conclusions
WHAT DATA ANALYTICS CAN DO
• Data analytic software enable users to obtain quick
overview of data and can drill down to detail information
• Using data analytic software, every transactions can be
involved or touched
• Identify anomalies, trends, patterns and concerns
• Large volumes of data

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POLLING QUESTION 1
The Truth Behind Detecting Fraud Using Data Analytics
DATA OVERVIEW: PAYROLL

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DATA OVERVIEW: FIELD STATISTICS
DATA OVERVIEW: VISUALIZATION

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DATA OVERVIEW: DESCRIPTIVES &
STATISTICS INFORMATION
Descriptives Data
Statistics Data
STANDARD DATA ANALYTIC FEATURES
• Extract
• Sort
• Gaps
• Duplicates
• Aging
• Samples
• Summarize
• Stratify
• Join/Match

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STATISTICAL & ADVANCED FEATURES
• Benford’s Law
• Trend Analysis
• Time Series Analysis
• Correlation
• Z-Score
• Relative Size Factor
• Same-Same-Same
• Same-Same-Different
WHY YOU MAY NEED DATA ANALYTIC
TOOLS
“By using ADAs, auditors are able to discover and analyze
patterns, identify anomalies, and obtain other information
from relevant data populations that may be very useful to an
organization.”
• CPA Canada Audit Client Briefing Audit Data Analytics – May 2017

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WHY YOU MAY NEED DATA ANALYTIC
TOOLS
• Standard 2210.A2 — The internal auditors must consider
the probability of significant errors, fraud, non-compliance,
and other exposures when developing the engagement
objectives
• Data analysis technology enables auditors and other fraud
examiners to analyze transactional data to obtain insights
into the operating effectiveness of internal controls and to
identify indicators of fraud risk or actual fraudulent activities
• GTAG 13 – Global Technology Audit Guide Fraud Prevention and
Detection in an Automated World , IIA – December 2009
WHY YOU MAY NEED DATA ANALYTIC
TOOLS
• Internal control systems have weaknesses
• The need to look at every transaction that takes place and
test them
• Sampling serves well when issues are relatively consistent
throughout the data – but fraud does not occur randomly
• Test specific hypothesis
• Perform risk analysis and quantify fraud if determined

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THE REALITY ABOUT FRAUD
DETECTION
• No magic to finding fraud
• Fraud detection is part of a process
• High number of anomalies from the process
• Few errors
• Fewer fraudulent transactions
THE PROCESS: PREPARING THE DATA
• Define audit objectives
• Obtain the data
• Verify
• Normalize
• Analyze
• Apply rules/criteria

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THE PROCESS: FINDING ANOMALIES
• Resulting anomalies
• Interpret the data, professional judgement
• Reduce anomalies
• Additional criteria
• Cut off
• Sampling
• Investigate
• Validate
• Report
POLLING QUESTION 2
The Truth Behind Detecting Fraud Using Data Analytics

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POLLING QUESTION 3
The Truth Behind Detecting Fraud Using Data Analytics
HIGH RISK ANOMALIES: PAYROLL
DATA

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EXAMPLE: PAYROLL DATA ANOMALIES
• Same-Same-Same Test:
• Same Employee
Number
+
• Same Check Date
+
• Same Account
Description (Payroll
Type)
• 19,854 Records
EXAMPLE: PAYROLL DATA ANOMALIES

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EXAMPLE: PAYROLL DATA ANOMALIES
Same-Same-Same
Regular Pay:
• 11,645 Records
PAYROLL DATA ANOMALIES
Same-Same-Same
Regular Pay:
• Equal or greater than
$5,000
• 312 Records

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PAYROLL DATA ANOMALIES
Same-Same-Same
Regular Pay:
• Summarize by Employee ID
• 454 Records
• Indexed (Descending) by
Hours
• Also index by:
- Amount
- Number of Records
- Average Hours
- Average Amount
POLLING QUESTION 4
The Truth Behind Detecting Fraud Using Data Analytics

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PAYROLL DATA ANOMALIES
Same-Same-Same
Regular Pay:
• Random Sampling of
50 Records
SUMMARY
• By using anomaly detection methods, data analytics can
help in finding fraud
• Unusual patterns needs to be identified
• With fraudulent transactions being rare, one must
understand what is normal

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POLLING QUESTION 5
The Truth Behind Detecting Fraud Using Data Analytics
Questions?

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connect@caseware.com
1-800-265-4332 EXT. 2800
sunder.gee@rtacorp.com
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THE TRUTH BEHIND DETECTING FRAUD
USING DATA ANALYTICS
WEBINAR