Literature Review on Data Mining Methods for Crime Detection

This report presents a literature review focusing on the application of data mining methods in crime detection, particularly in the context of identity and credit card fraud. The research begins with a broad scan of relevant literature, including academic journals and online resources, followed by a focused review to refine the scope and identify key papers. The report details the process of selecting and evaluating sources, creating research and filing journals, and updating bibliographic information. It examines identity crime, data mining methods, and credit card fraud detection techniques. The report explores various data mining approaches to combat fraud and emphasizes the importance of these techniques in protecting sensitive personal and financial data. The findings highlight the evolution of fraud detection and the use of big data techniques vs. data mining techniques. The report also includes a discussion on how to prevent frauds.

Introduction to Research
Literature Review
By : [To be added by student]
Student Id: [To be added by student]

Table of Contents
Chapter 1..........................................................................................................................................3
Literature Review............................................................................................................................3
1.1 Broad Scanning......................................................................................................................4
1.1.1 Research Journal.............................................................................................................4
1.1.2 Filing Journal..................................................................................................................6
1.1.3 Bibliographic Information of chosen papers..................................................................7
1.2 Focused review......................................................................................................................9
1.2.1 Updated filing System....................................................................................................9
1.2.2 Update Bibliographic Information of chosen papers....................................................10
1.3 Data Mining Methods for Crime Detection.........................................................................11
1.3.1 Identity Crime...............................................................................................................12
1.3.2 Overview of Data Mining.............................................................................................13
1.3.3 Data Mining Methods...................................................................................................13
1.4 Analysis on Credit Card Fraud Detection Techniques........................................................14
1.4.1 Credit Card Frauds........................................................................................................15
1.4.2 Detection of Fraud using Big Data techniques vs. Data Mining Techniques...............16
1.4.3 Discussion and Conclusion...........................................................................................17
1.5 Final Outline of the Literature Review Chapter..................................................................17
1.6 Introduction..........................................................................................................................18
References......................................................................................................................................19
1

List of Figures
Figure 1: Identity Crime Examples................................................................................................12
Figure 2: Types of Credit card Frauds...........................................................................................15
Figure 3: Data Mining Approach...................................................................................................16
Figure 4: Big Data Approach.........................................................................................................17
List of Tables
Table 1: Research Journal................................................................................................................3
Table 2: Filing System.....................................................................................................................5
Table 3: Updated Filing system.......................................................................................................8
2

Chapter 1
Literature Review
This assignment is about conducting an analytical research on the chosen topic: Data Mining.
The aim of the assignment is to show the process of how the research was conducted practically
through broad scan and then filtering out the appropriate and relevant information related to the
topic through focused review.
In the later sections of the paper, the literature review about the topic Data Mining has been
covered. Wherein the two most relevant papers filtered out from the whole research was selected
and evaluated to determine the answers to questions like research problem, subject,
instrumentation etc.
The research in this paper is based on ‘Data Mining methods to identify and detect data stealing
and frauds’. A broad scan was conducted wherein I reviewed more than 20 papers and found
more than 10 of them directly related to the topic which contained some of the relevant
information.
The two papers chosen for the research are based on conventional approach and technology
based approach on what kind of frauds exist related to it and also various data mining techniques
on how to overcome detected frauds.
3

1.1 Broad Scanning
I researched about the most common problem related to technology which world is exposed to in
today’s time. I found out about the online frauds related to credit cards, personal data which is
the most sensitive data. I decided my topic for the paper to ‘Data mining in detection of crime
and various data mining methods that are being used to detect identity crime. I researched about
the topic on different platforms such as pdf’s from Google Scholar, papers based on IEEE and so
on. There were a number of papers presented on this topic. All those papers have been referenced
here in the bibliographic section.
1.1.1 Research Journal
Table 1: Research Journal
Date Task Action Comment
28/08/2018 Conducted a search
for the topic
Read about various
problems related to
technology
Decided on two topics
: Data Mining and
Social Media Issues
29/08/2018 Conducted a search to
decide from 2 topics
listed out previous
day
Read a number of
research publications
and papers on both the
topic
Selected Data Mining
as the final topic
30/08/2018 To find relevant
information and
papers related to the
topic Data Mining
Explored Google
explored, Google
scholar, IEEE
reference papers,
some libraries
Approximately 25
papers were selected
which contained some
important and relevant
information related to
the topic
31/08/2018 Literature Reading A quick reading given
to 4 papers and
Got some knowledge
about the topic
4

journals
1/09/2018 Literature reading A quick reading given
to 5 more papers and
journals
Got a more clear
understanding of the
topic and how to write
up a literature review
2/09/2018 Literature reading Read few more papers
on the topic
Understood the topic
thoroughly and
discarded papers
which were not
related or useful
3/09/2018 To Filter out the
papers from the
collection remained
A thorough reading to
each of the paper
filtered out previous
day
Selected two final
papers for the
reference
4/09/2018 A thorough
understanding of how
to write literature
reviews
Researched few
papers and guides on
the proper steps to
write up literature
reviews.
All the information
collected on the topic
and how to start with
the assignment
5/09/2018 Started with the
assignment
Cited the work and
sources
Citations which were
related to my write-
ups included them in
the assignment
6/09/2018 Reviewed both
selected papers
Noted down all topics
and information to be
included in the
literature review
section
Paraphrased them in
my own words
7/09/2018 Written the Literature
review section
Covered all the
requirements
Completed the
assignment before
5

deadline.
1.1.2 Filing Journal
Table 2: Filing System
Source Keywords Used No. of Returned
Literatures
No. of collected
Literatures
Google Most Common
Technology Problems
Current Online Issues
Online Threats
18000
7654
5436
2
1
2
Scholar Data Mining Tools
Magical Thinking in
Data Mining
A Review on Data
Mining Methods
17896
14567
9078
3
3
4
IEEE
Data Stealing Process
Data Mining Tools
432
235
2
6
VU Library Review on Data
Mining
Measured taken to
detect frauds on credit
34000
1176
2
3
6

cards.
1.1.3 Bibliographic Information of chosen papers
Clifton Phua, Kate Smith-Miles, Vincent ChengSiong Lee and Ross Gayler. (2012). Resilient
Identity Crime Detection”, IEEE Transactions on Knowledge and Data Engineering, vol.2,
no.3,pp.533-546.
D. Bhavani. (2016). Data Mining for Malicious Code Detection and Security Applications”,
European Intelligence and Security Informatics Conference.
D. Hand. (2006). Classifier Technology and the Illusion of Progress,” Statistical Science, vol. 21,
no. 1, pp.1-15,doi: 10.1214/ 088342306000000060.
D. Yue, X. Wu, Y. Wang, Y. Li and C. Chu. (2016). A Review of Data iningbased Financial
Fraud Detection Research”, IEEE Conference.
E. Aleskerov, B. Freisleben, and B. Rao. (2015). Cardwatch: a neural network based database
mining system for credit card fraud detection. In Proceedings of Computational Intelligence for
Financial Engineering, pages 173-200.
E, Clkan. (2010). Magical Thinking in Data Mining: Lessons from CoIL Challenge 2000. Proc.
of SIGKDD01, 426-431.
E. Kirkos, C. Spathis and Y. Manolopoulos. (2015). Detection of Fraudulent Financial
Statements through the use of Data Mining Techniques”, 2nd International Conference on
Enterprise Systems and Accounting, Thessaloniki, Greece.
G.Apparao, Dr.Prof Arun Singh, G.S.Rao, B.Lalitha Bhavani, K.Eswar,D.Rajani. (2009).
Financial Statement Fraud Detection by Data Mining”, Int. J. of Advanced Networking and
Applications ,Volume: 01 Issue: 03 Pages: 159-163.
G. Gordon, D. Rebovich, K. Choo, and J.Gordon. (2017). Identity Fraud Trends and
7

Patterns:Building a Data-Based Foundation forProactiveEnforcement,” Center for Identity
Management and Information Protection, UticaCollege.
H. Experian. (2016). Experian Detect: Application Fraud
PreventionSystem,Whitepaper,http://www.experi an.com/products/pdf/experian_detect.pdf.
I. Witten and E. Frank. (2009). Data Mining: Practical Machine Learning Tools and Techniques
with Java. IEEE conference on Credit Scoring and CreditControl. VII. Edinburgh, UK. Sept 5-7.
J. Hollmn and V. Tresp. (2009). Call-based fraud detection in mobile communication networks
using a hierarchical regime- switching model.In Proceedings of the 1998 conference on
Advances in neural information processing systems II, pages 889-895. MIT Press.
K. Hassibi. (2017). Detecting payment card fraud with neural networks. In Business application
of Neural Networks, P.J.G. Lisboa, A. Vellido, B.Edisbury Eds. Singopore: World Scientific.
Lavrac, N., Motoda, H., Fawcett, T., Holte, R.,Langley, P. & Adriaans, P. (2014). Introduction:
LessonsLearned from Data Mining Applications and Collaborative Problem Solving. Machine
Learning 57(1-2): 13-34.
Masoumeh Zareapoor, Seeja.K.R, and M.Afshar.Alam. (2012). Analysis of Credit Card Fraud
Detection Techniques: based on Certain Design Criteria”, International Journal of Computer
Applications (0975 – 8887) Volume 52– No.3.
S. Bhattacharyya, S. Jha, K. Tharakunnel and J. Christopher. (2017). Data mining for credit card
fraud: A comparative study”, Decision Support Systems 50 pp. 602–613.
S Kerly. (2012). A Comparative Assessment Of Supervised Data Mining Techniques for
FraudPrevention”,TIST.Int.J.Sci.Tech.Res.,Vol.,1-6.
S. Wang. (2010). A Comprehensive Survey of Data Mining-based Accounting-Fraud Detection
Research”, International Conference on Intelligent Computation Technology and Automation.
R. Brause, T. Langsdorf, and M. Hepp. (2014). Credit card fraud detection by adaptive neural
data mining. In Proceedings of the11th IEEE International Conference on Tools with Artificial
8

Intelligence, pages 103-106.
T. Minegishi and A. Niimi. (2011). Detection of Fraud Use of Credit Card by Extended
FDT”,IEEE conference.
V.Bhusari ,S.Patil. (2011). Study of Hidden Markov Model in Credit Card Fraudulent Detection
", International Journal of Computer Applications (0975 - 8887) Volume 20- No.5.
1.2 Focused review
After reviewing the papers, documents presented in bibliographic section of broad scan, I
concluded that I had sufficient information to continue my assignment and I did not need to
research more on the topic. Also, I found that few of the papers were not much relevant to the
topic and nothing much useful could be extracted from it so I decided to discard them. Here I am
presenting the updated filing system and bibliography of the papers I chose to consult for further
reading.
1.2.1 Updated filing System
Table 3: Updated Filing system
Source Keywords Used No. of Returned
Literatures
No. of collected
Literatures
Scholar Data Mining Tools
Magical Thinking in
Data Mining
A Review on Data
Mining Methods
17896
14567
9078
2
2
1
IEEE Data Mining Tools 235 2
9

VU Library Review on Data
Mining
Measured taken to
detect frauds on credit
cards.
34000
1176
2
1
1.2.2 Update Bibliographic Information of chosen papers
D. Bhavani. (2016). Data Mining for Malicious Code Detection and Security Applications”,
European Intelligence and Security Informatics Conference.
E, Clkan. (2010). Magical Thinking in Data Mining: Lessons from CoIL Challenge 2000. Proc.
of SIGKDD01, 426-431.
E. Kirkos, C. Spathis and Y. Manolopoulos. (2015). Detection of Fraudulent Financial
Statements through the use of Data Mining Techniques”, 2nd International Conference on
Enterprise Systems and Accounting, Thessaloniki, Greece.
I. Witten and E. Frank. (2009). Data Mining: Practical Machine Learning Tools and Techniques
with Java. IEEE conference on Credit Scoring and CreditControl. VII. Edinburgh, UK. Sept 5-7.
Lavrac, N., Motoda, H., Fawcett, T., Holte, R.,Langley, P. & Adriaans, P. (2014). Introduction:
LessonsLearned from Data Mining Applications and Collaborative Problem Solving. Machine
Learning 57(1-2): 13-34.
R. Brause, T. Langsdorf, and M. Hepp. (2014). Credit card fraud detection by adaptive neural
data mining. In Proceedings of the11th IEEE International Conference on Tools with Artificial
Intelligence, pages 103-106.
S. Bhattacharyya, S. Jha, K. Tharakunnel and J. Christopher. (2017). Data mining for credit card
fraud: A comparative study”, Decision Support Systems 50 pp. 602–613.
10

S Kerly. (2012). A Comparative Assessment Of Supervised Data Mining Techniques for
FraudPrevention”,TIST.Int.J.Sci.Tech.Res.,Vol.,1-6.
T. Minegishi and A. Niimi. (2011). Detection of Fraud Use of Credit Card by Extended
FDT”,IEEE conference.
V.Bhusari ,S.Patil. (2011). Study of Hidden Markov Model in Credit Card Fraudulent Detection
", International Journal of Computer Applications (0975 - 8887) Volume 20- No.5.
1.3 Data Mining Methods for Crime Detection
Any kind of fraud or crime related to someone’s personal data which has been obtained by
wrongful deeds is termed as Identity crime. The crime is mainly attempted for economic gain.
Using deceitful identity document and falsification are major enablers related to Identity Fraud.
E-commerce is badly affected because of these crimes. The section 1.3.1 gives an introduction to
Identity crime: how the crime is committed and what are the major areas where this type of
crime is prominent. The next section 1.3.2 gives an overview of Data mining and the purpose of
data mining tools. In section 1.3.3 we will talk about various data mining methods to identify and
detect identity crime and in section 1.3.4 we will give a general discussion. In section 1.4 we will
discuss about the different credit-cards related frauds and different mining techniques to detect
credit card related frauds. Also predefined precautions to prevent these frauds.
1.3.1 Identity Crime
An identity crime takes place when a person’s personal information is stolen and some kind of
unlawful activity is observed using that information. For example: using a person’s credit card’s
account information to charge products. Commission of this crime can be done in two ways: one
is mock identity crime that denotes believable but made-up identity and other is illegally using
someone’s original identity details. The first kind is easy to obtain but difficult to use while the
second kind is easy to use but difficult to obtain.
Figure 1: Identity Crime Examples
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