Detecting Fraud with Data Analytics Workshop

Location & Venue

Event Venue

Hotel Information

Hotel Phone: 602-388-4888
Room Rate: $229 single/double
Hotel Cut-Off: October 13, 2024

*To make a reservation, please contact the hotel directly or click here.

Registration & Fees

Registration Fee:
Members: $1269
Non-Members: $1569
Early Registration Deadline: October 14, 2024
Register by the Early Registration Deadline to SAVE $150!

Course Outline

Day 1

• 7:30 – 8:00 a.m. Registration and Continental Breakfast

  Registration and Continental Breakfast

• 8:00 – 9:20 a.m. Introduction to Data Analytics and the Data Analysis Process

  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 process to the organization’s fraud risk assessment to most effectively detect fraud.

• 9:35 – 10:55 a.m. 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.

• 11:10 a.m. – 12:30 p.m. 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.

• 12:30 – 1:30 p.m. Group Lunch

  Group Lunch

• 1:30 – 2:50 p.m. 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 data analysts can use to take their fraud detection efforts to the next level. Participants will also earn 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.

• 3:05 – 4:25 p.m. Effectively Communicating the Results of the Data Analytics

  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.

Day 2

• 7:30 – 8:00 a.m. Breakfast

  Breakfast

• 8:00 – 9:20 a.m. Data Analysis Tests for Detecting Theft of Inventory and Cash Receipts

  This session explores specific data tests that participants can use to spot red flags of fraud in the customer sales cycle in their organizations. Participants will learn to identify red flags of theft of inventory and incoming cash receipts that appear in the data.

• 9:35 – 10:55 a.m. Case Study — Cash Receipts Fraud

  Attendees will apply the data analytics techniques discussed in the previous section by working on a hands-on case study involving cash receipts fraud.

• 11:10 a.m. – 12:30 p.m. 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. Attendees will then apply these techniques to data to assess whether and how an organization’s reported financial results have been manipulated.

• 12:30 – 1:30 p.m. Group Lunch

  Group Lunch

• 1:30 – 2:50 p.m. Data Analysis Tests to Detect Billing and Payment Tampering Schemes

  This session explores specific tests that participants can use to uncover fraud schemes within the accounts payable and cash disbursements 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.

• 3:05 – 4:25 p.m. 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.

Day 3

• 7:30 – 8:00 a.m. Breakfast

  Breakfast

• 8:00 – 9:20 a.m. Data Analysis Tests for Detecting Payroll Schemes

  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.

• 9:35 – 10:55 a.m. Case Study — Payroll Fraud

  Attendees will apply the data analytics techniques discussed in the previous section by working on a hands-on case study involving payroll fraud.

• 11:10 a.m. – 12:30 p.m. 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 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.

• 12:30 – 1:30 p.m. Group Lunch

  Group Lunch

• 1:30 – 2:50 p.m. Data Analysis Tests for Detecting Expense Reimbursement and P-Card Schemes

  This session focuses on data analysis techniques that participants can use to uncover particular fraud schemes within the expense reimbursement and purchasing card (or 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.

• 3:05 – 4:25 p.m. 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.

Presenter

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