Financial Statements Fraud
University of South Florida
Scholar Commons
Graduate School Theses and Dissertations Graduate School
6-1-2008
Detecting financial statement fraud: Three essays on fraud predictors, multi-classifier combination and fraud detection using data mining
Johan L. Perols
University of South Florida
Follow this and additional works at: http://scholarcommons.usf.edu/etd Part of the American Studies Commons Scholar Commons Citation
Perols, Johan L., "Detecting financial statement fraud: Three essays on fraud predictors, multi-classifier combination and fraud detection using data mining" (2008). Graduate School Theses and Dissertations. http://scholarcommons.usf.edu/etd/449
This Dissertation is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate School Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact scholarcommons@usf.edu.
Detecting Financial Statement Fraud: Three Essays on Fraud Predictors, Multi-Classifier Combination and Fraud Detection Using Data Mining
by
Johan L. Perols
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Information Systems and Decision Sciences Department of Accountancy College of Business University of South Florida
Co-Major Professor: Kaushal Chari, Ph.D. Co-Major Professor: Jacqueline L. Reck, Ph.D. Uday S. Murthy, Ph.D. Manish Agrawal, Ph.D. Date of Approval: April 10, 2008
Keywords: Earnings Management, Discretionary Accruals, Unexpected Productivity, Information Markets, Combiner Methods, Machine Learning © Copyright 2008, Johan L. Perols
Dedication To Becca who provided support (in many ways), encouragement and motivation, helped me with my ideas, and believed in me more than I sometimes did; and to family and friends for providing the motivation for completing this dissertation.
Acknowledgments To the faculty,
Scholar Commons
Graduate School Theses and Dissertations Graduate School
6-1-2008
Detecting financial statement fraud: Three essays on fraud predictors, multi-classifier combination and fraud detection using data mining
Johan L. Perols
University of South Florida
Follow this and additional works at: http://scholarcommons.usf.edu/etd Part of the American Studies Commons Scholar Commons Citation
Perols, Johan L., "Detecting financial statement fraud: Three essays on fraud predictors, multi-classifier combination and fraud detection using data mining" (2008). Graduate School Theses and Dissertations. http://scholarcommons.usf.edu/etd/449
This Dissertation is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate School Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact scholarcommons@usf.edu.
Detecting Financial Statement Fraud: Three Essays on Fraud Predictors, Multi-Classifier Combination and Fraud Detection Using Data Mining
by
Johan L. Perols
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Information Systems and Decision Sciences Department of Accountancy College of Business University of South Florida
Co-Major Professor: Kaushal Chari, Ph.D. Co-Major Professor: Jacqueline L. Reck, Ph.D. Uday S. Murthy, Ph.D. Manish Agrawal, Ph.D. Date of Approval: April 10, 2008
Keywords: Earnings Management, Discretionary Accruals, Unexpected Productivity, Information Markets, Combiner Methods, Machine Learning © Copyright 2008, Johan L. Perols
Dedication To Becca who provided support (in many ways), encouragement and motivation, helped me with my ideas, and believed in me more than I sometimes did; and to family and friends for providing the motivation for completing this dissertation.
Acknowledgments To the faculty,