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According to the ACFE's Occupational Fraud 2022: A 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 47% less costly and detect frauds 2.25 times as quickly as 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, Galvanize, 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.
Working knowledge of basic functions in one or more of the following software programs: Microsoft Excel, Galvanize, IDEA, Tableau.
You Will Learn How To:
Recognize the types of data and available tools that can be used to look for signs of fraud
Identify anomalies and recognize common red flags of fraud that appear in the data
Design and run targeted data analytics tests to detect various types of fraud schemes
Implement numerous traditional and non-traditional data analysis techniques
Apply the full data analysis process, including planning, data preparation, analysis and effectively communicating the results
Use your own laptop and software to perform data analysis techniques designed to detect fraud
Location & Venue
AMA Executive Conference Center
New York, NY 10019
*We do not have an ACFE room block established at any of these hotels.
319 West 48th St.
New York, NY 10036
Phone: +1 (212) 245-7000
This session will introduce participants 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. Participants 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.
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.
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.
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 data analysts can use to take your fraud detection efforts to the next level. Participants 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.
Once the data has been analyzed and conclusions drawn, the fraud examiner 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.
This session explores specific data tests that participants can use to spot red flags of fraud in the customer sales cycle in your organization. Participants will learn to identify red flags of theft of inventory and incoming cash receipts that appear in the data.
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. Attendees will then apply these techniques to data assess whether and how an organization's reported financial results have been manipulated.
This session explores specific tests that participants can use to uncover fraud schemes within the accounts payable and cash disbursements functions of their 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.
This session highlights data analysis techniques that participants can use to uncover particular fraud schemes within the payroll function of their organizations. 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.
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 participants with numerous techniques that can be used to analyze the structured and unstructured data in which warning signs of corruption schemes are often found.
This session focuses on data analysis techniques that participants can use to uncover particular fraud schemes within the expense reimbursement and purchasing card (p-card) functions of their organizations. 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.
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.
Bethmara Kessler, CFE - Speaker
Bethmara Kessler is a global thought leader, lecturer, consultant and advisor to businesses on the topics of fraud, audit, compliance, enterprise risk management, shared services delivery strategies and process transformation. Kessler is an ACFE Regent and is on the ACFE Faculty and Advisory Council. She earned her bachelor’s degree in business administration and accounting from Baruch College in New York.
Kessler is the former head of integrated global services (IGS) for the Campbell Soup Company. Her career spans more than 30 years in positions that include chief compliance officer, chief audit executive and enterprise risk management head. Her extensive experience also includes leadership roles in audit, risk management, information technology and corporate investigations in companies including EY, Avon Products, Nabisco, EMI Group, LBrands, The Fraud and Risk Advisory Group and Warner Music Group.
Kessler is a passionate fraud fighter and frequently speaks on a variety of topics related to fraud prevention and detection, investigations, auditing, compliance and risk. She is a contributing author to the ACFE's Fraud Examiners Manual and "Fraud Casebook: Lessons from the Bad Side of Business" as well as several articles that have appeared in "Internal Auditor Magazine," "The Journal of Accountancy" and other specialty publications.
Payment must be received by August 4, 2023 to receive early registration discount.
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, the ACFE will process a full refund of registration fees within 30 days of such circumstances becoming known. The ACFE will attempt to notify affected customers by phone and email after it determines cancellation is necessary.
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