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 broaden risk coverage and 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.
The Internal Audit Foundation, in partnership with Grant Thornton, conducted research and provided subject matter experts and editorial resources to produce this report.
About the Authors:
Warren W. Stippich Jr., CIA, CPA, CRMA, Partner, National Governance, Risk and Compliance Practice Leader, Global Co-Leader Business Risk Services, Grant Thornton LLP.
Bradley J. Preber, CPA/CFF, CGMA, CFE, CCA, National Managing Partner, Forensic and Valuation Services, Grant Thornton LLP.
Thank you IIA-Dallas Chapter for your sponsorship of this Internal Audit Foundation publication.
Item Number: 10.5074