Using Data Analytics to Detect Fraud
7:30 a.m. - 8:00 a.m.
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Registration - Breakfast Pastries
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8:00 a.m. - 9:20 a.m.
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Introduction to Data Analytics
This session will introduce participants to the uses, benefits, and challenges of data analytics techniques. Participants will also learn common data analysis functions and terminology and will discuss some software options for performing data analysis tests.
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9:20 a.m. - 9:35 a.m.
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Break
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9:35 a.m. - 10:55 a.m.
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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.
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10:55 a.m. - 11:10 a.m.
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Break
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11:10 a.m. - 12:30 p.m.
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Data Analysis Tests for Fraud Detection
This session explores specific tests that participants can use to uncover particular fraud schemes within the payroll, 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.
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12:30 p.m. - 1:30 p.m.
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Group Lunch
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1:30 p.m. - 2:50 p.m.
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Data Analysis Tests for Fraud Detection (continued)
This session focuses on specific tests that participants can use to detect cash receipt theft, inventory fraud, and corruption schemes. 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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2:50 p.m. - 3:05 p.m.
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Break
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3:05 p.m. - 4:25 p.m.
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Data Analysis Tests for Fraud Detection (continued)
This session continues the previous two, focusing on targeted data analysis techniques to identify financial statement fraud schemes. Participants will also learn about non-traditional data analysis tests that identify the red flags of fraud, such as textual and timeline analytics.
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