Chapter 1Introduction to Fraud Data Analytics

The world's best auditor using the world's best audit program cannot detect fraud unless their sample includes a fraudulent transaction. This is why fraud data analytics (FDA) is so critical to the auditing profession.

How we use fraud data analytics largely depends on the purpose of the audit project. If the fraud data analytics is used in a whistle blower allegation, then the fraud data analytics plan is designed to refute or corroborate the allegation. If the fraud data analytics plan is used in a control audit, then the fraud data analytics would search for internal control compliance or internal control avoidance. If the fraud data analytics is used for fraud testing, then the fraud data analytics is used to search for a specific fraud scenario that is hidden in your database. This book is written for fraud auditors who want to integrate fraud testing into their audit program. The concepts are the same for fraud investigation and internal control avoidance—what changes is the scope and context of the audit project.

Interestingly, two of the most common questions heard in the profession are, “Which fraud data analytic routines should I use in my audit?” and, “What are the three fraud data analytics tests I should use in payroll or disbursements?” In one sense, there really is no way to answer these questions because they assume the fraud auditor knows what fraud scenario someone might be committing. In reality, we search for patterns ...

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