According to the ACFE's 2018 Report to the Nations, proactive data monitoring and analysis is among the most effective anti-fraud controls. Organizations that undertake proactive data analysis techniques experience frauds that are
52% less costly and 58% shorter than organizations that do not monitor and analyze data for signs of fraud.
The Detecting Fraud
with Data Analytics Workshop will teach you how to plan, design, and apply numerous data analytics tests in order to detect various fraud schemes. You will also discover how to examine and interpret the results of those tests to identify the red flags
of fraud.
The first day of the course will provide a solid foundation on which to build your data analytics initiatives, from understanding the types of tests that can be used to tying your tests to the fraud risk assessment, properly preparing and
normalizing your data for testing, and effectively communicating the results of your analysis. On days two and three, you will link your new knowledge of analytical tests, data sources, and fraud schemes while applying data analysis techniques
to real data sets and scenarios.
Bring your own laptop and the software you are most comfortable with,* and practice using data analytics to uncover the red flags of fraud. You will work through cases covering a variety of fraud schemes, including purchasing fraud,
payroll manipulation, and financial statement fraud.
* Practical exercises can be conducted in Microsoft Excel, ACL, IDEA, or Tableau. In order to participate in this workshop, attendees will need to bring one or more of these software programs fully installed and ready to use on their own laptop.
Technical support for installing and running these programs will NOT be provided.
Upcoming Courses
You Will Learn How To:
Recognize the types of data and available tools that can be used to look for signs of fraud
Implement numerous traditional and non-traditional data analysis techniques
Identify anomalies and recognize common red flags of fraud that appear in the data
Apply the full data analysis process, including planning, data preparation, analysis and effectively communicating the results
Design and run targeted data analytics test to detect various types of fraud schemes
Use your own laptop and software* to perform data analysis techniques designed to detect fraud
Fees*
Members: $1195
Non-Members: $1495
*Pricing listed is for U.S. events. International event pricing may vary by location. Please view the individual event page for International pricing.
CPE Credit
24
Field of Study
Auditing
Course Level
Overview
Prerequisite
Working knowledge of basic functions in one or more of the following software programs: Microsoft Excel, ACL, IDEA, Tableau
Advanced Preparation
None
Delivery Method
Group-Live
*Please note: Schedule listed is for U.S. and Canadian events. All events outside of the U.S. and Canada are pushed back 30 minutes with registration beginning at 8:00 a.m. and the last session ending at 4:55 p.m.
| Detecting Fraud wiht Data Analytics Workshop |
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DAY ONE |
DAY TWO |
DAY THREE |
7:30 a.m. -
8:00 a.m. |
Registration |
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8:00 a.m. -
9:20 a.m.
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Introduction to Data Analysis and the Data Analysis Process
This session will introduce you to the uses, benefits and challenges of data analytics techniques, as well as the types of data that can be analyzed and some software options for performing data analysis tests. You will also learn how to formulate and
apply an overarching methodology for building a data analytics program and how to tie the organization's fraud risk assessment to most effectively detect fraud. |
Data Analysis Tests for Detecting Theft of Inventory and Cash Receipts
This session explores specific data tests that you can use to spot red flags of fraud in the customer sales cycle in your organization. You will learn to identify red flags of theft of inventory and incoming cash receipts fraud.
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Data Analysis Tests for Detecting Payroll Schemes
This session highlights data analysis techniques that you can use to uncover particular fraud schemes within the payroll function of your organization. Using discussion scenarios to walk through data analytics techniques, participants will learn to identify red flags of these types of fraud that appear in the data.
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9:20 a.m. -
9:35 a.m.
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Break
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Break
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Break
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9:35 a.m. -
10:55 a.m.
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Prepping, Normalizing and Harmonizing Data
The results of any data analysis technique are only as good as the underlying data that is examined. One of the most important steps — and often one of the biggest challenges — in data analytics initiatives involves preparing and normalizing
the data for analysis. In this session, participants will explore common issues and effective solutions for ensuring the data they have obtained is properly prepared, normalized and harmonized before they begin their testing.
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Case Study — Cash Receipts Fraud
You will apply the data analytics techniques discussed in the previous section by working on a hands-on case study involving cash receipts fraud.
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Case Study — Payroll Fraud
You will apply the data analytics techniques discussed in the previous section by working on a hands-on case study involving payroll fraud.
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10:55 a.m. -
11:10 a.m.
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Break
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Break
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Break
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11:10 a.m. -
12:30 p.m.
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Fundamental Data Analysis Techniques
In this session, participants will discuss many of the most common data analysis techniques — such as duplicate testing, matching, gap testing and compliance verification — that can be used to comb through the data and identify anomalies and red flags of
fraud. |
Data Analysis Test for Detecting Financial Statement Fraud
This session focuses on targeted data analysis tests to identify various financial statement fraud schemes. By using these techniques to analyze the financial statements, the related disclosures and the underlying data, fraud examiners
can identify anomalies and uncover financial statement manipulation. You will then apply these techniques to data assess whether and how an organization's reported financial results have been manipulated.
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Data Analysis Tests for Detecting Corruption
Corruption schemes can be particularly difficult to detect, as so many clues for these schemes fall outside the company's financial records — and thus outside the traditional realm of data analytics. This session provides you with numerous
techniques that can be used to analyze the structured and unstructured data in which warning signs of corruption schemes are often found.
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12:30 p.m. -
1:30 p.m.
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Group Lunch
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Group Lunch
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Group Lunch
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1:30 p.m. -
2:50 p.m.
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Other Data Analysis Techniques
There are numerous sophisticated analysis techniques that can be particularly useful in analyzing datasets for symptoms of fraud. This session includes discussions of Benford's Law analysis, regression analysis, reasonableness testing
and several other tools that you can use to take your fraud detection efforts to the next level. You will also learn about non-traditional data analysis tests that identify the red flags of fraud, such as textual, visual, and timeline
analytics, as well as transactional risk-ranking techniques.
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Data Analysis Tests for Detecting Billing and Check Tampering Schemes
This session explores specific tests that you can use to uncover fraud schemes within the accounts payable and cash disbursements functions of your organization. Using discussion scenarios to walk through data analytics techniques, you
will learn to identify red flags of these types of fraud that appear in the data.
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Data Analysis Tests for Detecting Expense Reimbursement and P-Card Schemes
This session focuses on data analysis techniques that you can use to uncover particular fraud schemes within the expense reimbursement and purchasing card (or p-card) functions of your organization. Using discussion scenarios to walk through data analytics techniques, you will learn to identify red flags of these types of fraud that appear in the data.
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2:50 p.m. -
3:05 p.m.
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Break
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Break
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Break
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3:05 p.m. -
4:25 p.m.
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Effectively Communicating the Results of the Data Analytics
Once the data has been analyzed and conclusions drawn, you often must convey what has been learned. However, effectively communicating those results can be a challenge for many examiners, both experienced and novice. This session will discuss how to effectively
communicate the results of data analytics to both internal and external audiences.
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Case Study — Billing Fraud
Attendees will apply the data analytics techniques discussed in the previous section by working on a hands-on case study involving billing fraud.
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Case Study — Expense Reimbursement and P-Card Fraud
Attendees will apply the data analytics techniques discussed in the previous section by working on a hands-on case study involving expense reimbursement and p-card fraud.
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Event Details
Event Cancellation Policy
Our
cancellation policy is intended to keep costs low for attendees. Due to financial obligations incurred by ACFE, Inc. you must
cancel your registration prior to the start of the event. Cancellations received less than 14 calendar days prior to an event start date are subject to a $100 administrative fee. No refunds or credits will be given for cancellations
received on or after the start date of the event. Those who do not cancel and do not attend are responsible for the full registration fee. Should an event be cancelled or postponed by the ACFE due to unforeseen circumstances, ACFE
will process a full refund of registration fees within 30 days of such circumstances becoming known. ACFE will attempt to notify affected customers by phone and email after it determines cancellation is necessary.
Satisfaction Guarantee
ACFE seminars are unmatched in scope and effectiveness and backed by our unconditional satisfaction guarantee. If you attend an ACFE event and are not completely satisfied, please contact an ACFE Member Services Representative at
MemberServices@ACFE.com or call (800) 245-3321 / +1 (512) 478-9000.
The
Association of Certified Fraud Examiners, Inc. is registered with the National Association of State Boards of Accountancy (NASBA) as a sponsor of continuing professional education on the National Registry of CPE Sponsors. State boards
of accountancy have final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website:
www.nasbaregistry.org.
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