RUcore: Rutgers University Community Repository
Anomaly detection and predictive analytics for financial risk management
Descriptive
TitleInfo
Title
Anomaly detection and predictive analytics for financial risk management
Name
(type = personal)
NamePart
(type = family)
Li
NamePart
(type = given)
Zhongmou
NamePart
(type = date)
1986-
DisplayForm
Zhongmou Li
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Xiong
NamePart
(type = given)
Hui
DisplayForm
Hui Xiong
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
chair
Name
(type = personal)
NamePart
(type = family)
Lin
NamePart
(type = given)
Xiaodong
DisplayForm
Xiaodong Lin
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Papadimitriou
NamePart
(type = given)
Spiros
DisplayForm
Spiros Papadimitriou
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Zhou
NamePart
(type = given)
Mengchu
DisplayForm
Mengchu Zhou
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
(encoding = w3cdtf);
(qualifier = exact)
2016
DateOther
(qualifier = exact);
(type = degree)
2016-01
CopyrightDate
(encoding = w3cdtf);
(qualifier = exact)
2016
Place
PlaceTerm
(type = code)
xx
Language
LanguageTerm
(authority = ISO639-2b);
(type = code)
eng
Abstract
(type = abstract)
In the big data era, the digital revolution has driven the entire financial industry to collect, store and analyze massive volumes of data nowadays than it ever has in history. With the overwhelming scale of data, new technologies are needed to derive competitive advantage and unlock the power of the data, including the approaches people use for financial risk management. In this dissertation, we study how advanced data mining techniques can play essential roles in financial risk management. Specifically, we provide case studies to apply data mining techniques in three application scenarios for financial risk management. The first study exploits a special type of fraudulent trading ring pattern in the financial market, and defines the so-called blackhole and volcano patterns to identify the fraud. A blackhole mining framework consisting of two pruning schemes is developed. The first pruning scheme is to exploit the concept of combination dominance to reduce the exponential growth search space. The second pruning scheme is an approximate approach, which can strike a balance between the efficiency and the completeness of blackhole mining. The second study exploits the problem of contract risk management. In particular, how IT service providers can leverage the experiences and lessons learnt from historical contracts to prevent similar issues from reoccurring in the future, in order to mitigate the project risks, ensure smooth delivery and continuous profitability. Along this line, we investigate how to predict potential risks for new contracts based on their similarities with existing ones, and develop a new approach as an extension of the Mahalanobis distance metric learning framework to solve the problem. The third study examines the application of cluster analysis in bankruptcy pattern learning and financial statement fraud detection. By leveraging the domain knowledge in accounting area, valuable features from financial statement can be extracted. Clustering technique is then applied to identify the clustering effect of bankrupt companies in different business sectors. Finally, the most indicative financial features for the bankrupt companies in the business sector can be uncovered from the hidden data and validated by significant tests.
Subject
(authority = RUETD)
Topic
Management
Subject
(authority = ETD-LCSH)
Topic
Data mining
Subject
(authority = ETD-LCSH)
Topic
Risk management
RelatedItem
(type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
Identifier
ETD_6967
PhysicalDescription
Form
(authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (x, 120 p. : ill.)
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = statement of responsibility)
by Zhongmou Li
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/T35B04MT
Genre
(authority = ExL-Esploro)
ETD doctoral
Back to the top
Rights
RightsDeclaration
(ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Li
GivenName
Zhongmou
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
(encoding = w3cdtf);
(qualifier = exact);
(point = start)
2016-01-05 01:00:32
AssociatedEntity
Name
Zhongmou Li
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
Back to the top
Technical
RULTechMD
(ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem
(VERSION = 5.1)
windows xp
Back to the top
Statistical Profile