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Exceptional exceptions

Descriptive

TitleInfo
Title
Exceptional exceptions
Name (type = personal)
NamePart (type = family)
Issa
NamePart (type = given)
Hussein
NamePart (type = date)
1974-
DisplayForm
Hussein Issa
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Kogan
NamePart (type = given)
Alexander
DisplayForm
Alexander Kogan
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Vasarhelyi
NamePart (type = given)
Miklos
DisplayForm
Miklos Vasarhelyi
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Brown-Liburd
NamePart (type = given)
Helen
DisplayForm
Helen Brown-Liburd
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Dull
NamePart (type = given)
Richard
DisplayForm
Richard Dull
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - Newark
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2013
DateOther (qualifier = exact); (type = degree)
2013-10
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
The increasing utilization of computerized systems in businesses has led to the generation and storage of massive databases. In light of the availability of such big data, auditing is moving from the traditional sample-based approach to audit-by-exception. The literature is abundant with studies that propose various machine learning, statistical, and data mining techniques that have proved to be efficient in identifying exceptions. However, such techniques often inundate auditors and management with large numbers of exceptions. This dissertation, composed of three essays, attempts to help them overcome the human limitations of dealing with information overload by proposing methodologies to detect and subsequently prioritize such exceptions. These prioritization techniques can help auditors and management to direct their investigations towards the more suspicious cases, or exceptional exceptions. The first essay evaluates the quality of auditors’ judgment of business processes’ risk levels using historic data procured from internal controls risk assessments of a multinational company. I identify the exceptions where auditor assessments deviate from the value predicted by an ordered logistic regression model. Subsequently, I propose two metrics to prioritize these exceptions. The results indicate that the prioritization methodology proved effective in helping auditors focus their efforts on the more problematic audits. In the second essay I propose a framework where I use a weighted rule-based expert system to identify exceptions that violate internal controls. These exceptions are then prioritized based on a suspicion score, defined as the sum of the risk weightings of all the internal controls that were violated by that specific record. Finally, the exceptions are ranked by decreasing order of suspicion score. The third essay addresses the problem of data quality from a duplicate records perspective. I present the various techniques used to detect such duplicates, and focus on the issue of duplicate payments. I use two real business datasets as an illustration. Finally I propose a prioritization methodology where each duplicate candidate receives a cumulative score based on multiple criteria. The results show that my prioritization methodology can help the auditors to process duplicate candidates more effectively.
Subject (authority = RUETD)
Topic
Management
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_4972
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
xi, 173 p. : ill.
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = vita)
Includes vita
Note (type = statement of responsibility)
by Hussein Issa
Subject (authority = ETD-LCSH)
Topic
Auditing--Data processing
Subject (authority = ETD-LCSH)
Topic
Corporations--Auditing
RelatedItem (type = host)
TitleInfo
Title
Graduate School - Newark Electronic Theses and Dissertations
Identifier (type = local)
rucore10002600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T32J68V1
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Issa
GivenName
Hussein
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2013-09-01 00:40:47
AssociatedEntity
Name
Hussein Issa
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - Newark
AssociatedObject
Type
License
Name
Author Agreement License
Detail
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
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
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