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Designing continuous audit analytics and fraud prevention systems using emerging technologies
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Wang, Yunsen. Designing continuous audit analytics and fraud prevention systems using emerging technologies. Retrieved from https://doi.org/doi:10.7282/T3445QWZ

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TitleDesigning continuous audit analytics and fraud prevention systems using emerging technologies
NameWang, Yunsen (author); Kogan, Alexander (chair); Vasarhelyi, Miklos A. (internal member); Issa, Hussein (internal member); Gal, Graham (outside member); Rutgers University; Graduate School - Newark
Date Created2018
Other Date2018-05 (degree)
SubjectManagement, Fraud--Prevention, Auditing
Extent1 online resource (xi, 137 p. : ill.)
DescriptionThis 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.
NotePh.D.
NoteIncludes bibliographical references
Noteby Yunsen Wang
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3445QWZ
Languageeng
CollectionGraduate School - Newark Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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