On-Site Training Proposed Schedule
Using Data Analytics to Detect Fraud
7:30 a.m. - 8:00 a.m.
Registration - Breakfast Pastries
8:00 a.m. - 9:20 a.m.
Introduction to Data Analytics
This session will introduce participants to the uses, benefits, and challenges of data analytics techniques. Participants will also learn about the types of data that can be analyzed and will discuss some software options for performing data analysis tests.
Data Analysis Tests for Detecting Billing and Check 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.
9:20 a.m. - 9:35 a.m.
9:35 a.m. - 10:55 a.m.
The Data Analysis Process
The results of any data analysis technique are only as good as the underlying data that is examined. Participants will learn how to formulate an overarching methodology for building a data analytics program—from data identification and acquisition through reporting the analysis results—and how to tie the process to the organization’s fraud risk assessment to most effectively detect fraud.
Data Analysis Tests for Detecting Payroll and Expense Reimbursement Schemes
This session highlights data analysis techniques that participants can use to uncover particular fraud schemes within the payroll and expense reimbursement 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.
10:55 a.m. - 11:10 a.m.
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.
Data Analysis Tests for Detecting Theft of Cash Receipts and Inventory
This session explores specific data tests that participants can use to spot red flags of the theft of incoming cash receipts and of inventory in 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.
12:30 p.m. - 1:30 p.m.
Lunch on Your Own
1:30 p.m. - 2:50 p.m.
Advanced 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.
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.
2:50 p.m. - 3:05 p.m.
3:05 p.m. - 4:25 p.m.
Other Data Analysis Techniques
In this session, participants will learn about non-traditional data analysis tests that identify the red flags of fraud, such as textual, visual, and timeline analytics. They will also discuss methods for ranking particular transactions and individuals based on a composite of red flags identified through data analysis techniques.
Data Analysis Tests 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.