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Designing continuous audit analytics and fraud prevention systems using emerging technologies
Descriptive
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Title
Designing continuous audit analytics and fraud prevention systems using emerging technologies
Name
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Wang
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Yunsen
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Yunsen Wang
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Kogan
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Alexander
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Alexander Kogan
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Advisory Committee
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chair
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Vasarhelyi
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Miklos A.
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Miklos A. Vasarhelyi
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Advisory Committee
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internal member
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Issa
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Hussein
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Hussein Issa
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Advisory Committee
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internal member
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Gal
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Graham
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Graham Gal
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Advisory Committee
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Rutgers University
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degree grantor
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Graduate School - Newark
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theses
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2018
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2018-05
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2018
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xx
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eng
Abstract
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This dissertation consists of three essays that design and evaluate the continuous audit analytics and fraud prevention systems using three emerging technologies (i.e., the blockchain, in-memory cloud computing, and deep learning). The first essay designs a framework of Blockchain-based Transaction Processing System using the homomorphic encryption and zero-knowledge proof mechanisms. Furthermore, this study develops a prototype of the designed system to demonstrate its applications in real-time accounting, continuous monitoring, and fraud prevention. Although the simulation tests show the Blockchain-based Transaction Processing System consumes more computational overhead than the conventional database-based ERP system, the blockchain should be considered as a promising technology for future accounting and auditing practice. The second essay introduces the database architecture that manages data in main physical memory and columnar format. This essay proposes a conceptual framework for applying the in-memory columnar database system to support high-speed continuous audit analytics. Moreover, this study develops a prototype and conducts the simulation tests to evaluate the proposed framework. The test results show the high efficiency and effectiveness of the in-memory columnar database relative to the conventional ERP system regarding the computational time and the storage volume. Furthermore, the deployment of the in-memory columnar database to the cloud shows great promise of applying the in-memory columnar database for continuous audit analytics. The third essay designs a continuous fraud detection system based on modified deep learning technology. Specifically, this essay builds an accounting layer on top of the deep learning architecture to process financial data for predicting the fraudulent financial statements. A prototype is developed to evaluate the prediction accuracy of the proposed design. The test results show the deep learning-based continuous fraud detection system provides high prediction accuracy relative to the existing studies of financial statement fraud detection.
Subject
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Topic
Management
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Rutgers University Electronic Theses and Dissertations
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ETD_8912
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electronic resource
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application/pdf
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text/xml
Extent
1 online resource (xi, 137 p. : ill.)
Note
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Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Subject
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Topic
Fraud--Prevention
Subject
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Topic
Auditing
Note
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by Yunsen Wang
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Graduate School - Newark Electronic Theses and Dissertations
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rucore10002600001
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Identifier
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doi:10.7282/T3445QWZ
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(authority = ExL-Esploro)
ETD doctoral
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Rights
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The author owns the copyright to this work.
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Wang
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Yunsen
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Permission or license
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2018-04-17 16:55:09
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Yunsen Wang
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Rutgers University. Graduate School - Newark
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
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Copyright protected
Availability
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Open
Reason
Permission or license
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2018-04-17T20:52:03
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2018-04-17T20:52:03
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