Data analysis is a powerful tool for fraud examiners. By searching for patterns, anomalies, trends and outliers, you might discover a cleverly hidden scheme. A skillful fraudster is adept at covering his tracks. However, data analytics empowers you to uncover even the most thoroughly shrouded fraud. Furthermore, data analytics can be used to continuously monitor an organization so that red flags of fraud are discovered and investigated as soon as they occur.
This course will introduce you to the basics of using data analysis to uncover fraud. You will work through several interactive examples to illustrate simple analysis techniques. With these examples, you will learn how to perform a variety of tests and interpret the results to identify common red flags of fraud.
You will learn how to:
Describe the four phases of the data analysis process and why it is important to have a
Identify common red flags that appear in fraudulent data
Select the appropriate data analysis tools for a particular investigation
List fundamental data analysis techniques that can be applied to almost any investigation
Determine data analysis techniques to use depending on the type of fraud scheme
Evaluate unstructured data, such as text, for signs of fraud
Field of study: Auditing
Last updated: May 2013
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Please note: To be eligible for CPE credit, you must complete the final exam within one year of purchase date. You may only claim CPE credit for a course once.
Table of Contents:
Lesson 1: Introduction
Lesson 2: What is Data Analysis?
Lesson 3: Why Do We Use Data Analytics to Detect Fraud?
Lesson 4: Challenges of Using Data Analytics to Detect Fraud
Lesson 5: The Planning Phase
Lesson 6: The Preparation Phase
Lesson 7: The Testing and Interpretation Phase
Lesson 8: The Post-Analysis Phase
Lesson 9: Basic Analytical Procedures
Lesson 10: Billing Schemes
Lesson 11: Check Tampering Schemes
Lesson 12: Payroll Schemes
Lesson 13: Expense Reimbursement Schemes
Lesson 14: Theft of Cash Receipts
Lesson 15: Inventory Schemes
Lesson 16: Corruption Schemes
Lesson 17: Financial Statement Fraud Schemes
Lesson 18: Ratio Analysis
Lesson 19: Benford's Law
Lesson 20: Textual Analytics
Lesson 21: Conclusion
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