Today’s audit leader struggles with creating an integrated, efficient approach to data mining that maximizes the impact and value the audit department delivers. The objective of the project is to research and design a data analytics framework ranging across a wide spectrum of concepts (such as financial risk, compliance, and fraud) to help internal audit functions to broaden risk coverage and to enhance audit efficiency.
You will learn how to:
- Develop a data analytics framework and use it to accomplish multiple audit objectives.
- Enhance internal audit efficiency through the use of data mining and analytics.
- Eliminate duplicated data mining and analysis efforts across audit and other functions.
- Determine the optimal effort needed to maximize the framework.
- This partnership project between The IIA Research Foundation and Grant Thornton aided in conducting research, providing subject matter experts, and editorial resources to produce the report.
About the Authors
Warren W. Stippich Jr., CIA, CPA, CRMA. Partner, U.S. Governance, Risk and Compliance Practice Leader, Grant Thornton
Bradley J. Preber, CPA, CFF, CFE. National Managing Partner, Forensic and Valuation Services, Grant Thornton.