Fraud Analysis Approaches in the Age of Big Data-A Review of State of the Art

Sara Makki; Rafiqul Haque; Yehia Taher; Zainab Assaghir; Gregory Ditzler; Mohand Said Hacid; Hassan Zeineddine

doi:10.1109/FAS-W.2017.154

Fraud Analysis Approaches in the Age of Big Data-A Review of State of the Art

Sara Makki, Rafiqul Haque, Yehia Taher, Zainab Assaghir, Gregory Ditzler, Mohand Said Hacid, Hassan Zeineddine

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

Fraud is a criminal practice for illegitimate gain of wealth or tampering information. Fraudulent activities are of critical concern because of their severe impact on organizations, communities as well as individuals. Over the last few years, various techniques from different areas such as data mining, machine learning, and statistics have been proposed to deal with fraudulent activities. Unfortunately, the conventional approaches display several limitations, which were addressed largely by advanced solutions proposed in the advent of Big Data. In this paper, we present fraud analysis approaches in the context of Big Data. Then, we study the approaches rigorously and identify their limits by exploiting Big Data analytics.

Original language	English (US)
Title of host publication	Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	243-250
Number of pages	8
ISBN (Electronic)	9781509065585
DOIs	https://doi.org/10.1109/FAS-W.2017.154
State	Published - Oct 9 2017
Event	2nd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017 - Tucson, United States Duration: Sep 18 2017 → Sep 22 2017

Publication series

Name	Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017

Other

Other	2nd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
Country	United States
City	Tucson
Period	9/18/17 → 9/22/17

Keywords

Big Data
Data Mining
Fraud Analysis
Machine Learning
Statistical Modeling

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Computational Mechanics

Access to Document

10.1109/FAS-W.2017.154

Fingerprint Dive into the research topics of 'Fraud Analysis Approaches in the Age of Big Data-A Review of State of the Art'. Together they form a unique fingerprint.

Cite this

Makki, S., Haque, R., Taher, Y., Assaghir, Z., Ditzler, G., Hacid, M. S., & Zeineddine, H. (2017). Fraud Analysis Approaches in the Age of Big Data-A Review of State of the Art. In Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017 (pp. 243-250). [8064130] (Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FAS-W.2017.154

Fraud Analysis Approaches in the Age of Big Data-A Review of State of the Art. / Makki, Sara; Haque, Rafiqul; Taher, Yehia; Assaghir, Zainab; Ditzler, Gregory; Hacid, Mohand Said; Zeineddine, Hassan.

Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 243-250 8064130 (Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Makki, S, Haque, R, Taher, Y, Assaghir, Z, Ditzler, G, Hacid, MS & Zeineddine, H 2017, Fraud Analysis Approaches in the Age of Big Data-A Review of State of the Art. in Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017., 8064130, Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017, Institute of Electrical and Electronics Engineers Inc., pp. 243-250, 2nd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017, Tucson, United States, 9/18/17. https://doi.org/10.1109/FAS-W.2017.154

Makki S, Haque R, Taher Y, Assaghir Z, Ditzler G, Hacid MS et al. Fraud Analysis Approaches in the Age of Big Data-A Review of State of the Art. In Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 243-250. 8064130. (Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017). https://doi.org/10.1109/FAS-W.2017.154

Makki, Sara ; Haque, Rafiqul ; Taher, Yehia ; Assaghir, Zainab ; Ditzler, Gregory ; Hacid, Mohand Said ; Zeineddine, Hassan. / Fraud Analysis Approaches in the Age of Big Data-A Review of State of the Art. Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 243-250 (Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017).

@inproceedings{47bd8d08827044c99d1c5282ec8dc9e8,

title = "Fraud Analysis Approaches in the Age of Big Data-A Review of State of the Art",

abstract = "Fraud is a criminal practice for illegitimate gain of wealth or tampering information. Fraudulent activities are of critical concern because of their severe impact on organizations, communities as well as individuals. Over the last few years, various techniques from different areas such as data mining, machine learning, and statistics have been proposed to deal with fraudulent activities. Unfortunately, the conventional approaches display several limitations, which were addressed largely by advanced solutions proposed in the advent of Big Data. In this paper, we present fraud analysis approaches in the context of Big Data. Then, we study the approaches rigorously and identify their limits by exploiting Big Data analytics.",

keywords = "Big Data, Data Mining, Fraud Analysis, Machine Learning, Statistical Modeling",

author = "Sara Makki and Rafiqul Haque and Yehia Taher and Zainab Assaghir and Gregory Ditzler and Hacid, {Mohand Said} and Hassan Zeineddine",

note = "Funding Information: This work partially benefited from the support of the Lebanese National Council for Scientific Research (CNRS-L). Publisher Copyright: {\textcopyright} 2017 IEEE.; 2nd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017 ; Conference date: 18-09-2017 Through 22-09-2017",

year = "2017",

month = oct,

day = "9",

doi = "10.1109/FAS-W.2017.154",

language = "English (US)",

series = "Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "243--250",

booktitle = "Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017",

}

TY - GEN

T1 - Fraud Analysis Approaches in the Age of Big Data-A Review of State of the Art

AU - Makki, Sara

AU - Haque, Rafiqul

AU - Taher, Yehia

AU - Assaghir, Zainab

AU - Ditzler, Gregory

AU - Hacid, Mohand Said

AU - Zeineddine, Hassan

PY - 2017/10/9

Y1 - 2017/10/9

N2 - Fraud is a criminal practice for illegitimate gain of wealth or tampering information. Fraudulent activities are of critical concern because of their severe impact on organizations, communities as well as individuals. Over the last few years, various techniques from different areas such as data mining, machine learning, and statistics have been proposed to deal with fraudulent activities. Unfortunately, the conventional approaches display several limitations, which were addressed largely by advanced solutions proposed in the advent of Big Data. In this paper, we present fraud analysis approaches in the context of Big Data. Then, we study the approaches rigorously and identify their limits by exploiting Big Data analytics.

AB - Fraud is a criminal practice for illegitimate gain of wealth or tampering information. Fraudulent activities are of critical concern because of their severe impact on organizations, communities as well as individuals. Over the last few years, various techniques from different areas such as data mining, machine learning, and statistics have been proposed to deal with fraudulent activities. Unfortunately, the conventional approaches display several limitations, which were addressed largely by advanced solutions proposed in the advent of Big Data. In this paper, we present fraud analysis approaches in the context of Big Data. Then, we study the approaches rigorously and identify their limits by exploiting Big Data analytics.

KW - Big Data

KW - Data Mining

KW - Fraud Analysis

KW - Machine Learning

KW - Statistical Modeling

UR - http://www.scopus.com/inward/record.url?scp=85035237588&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85035237588&partnerID=8YFLogxK

U2 - 10.1109/FAS-W.2017.154

DO - 10.1109/FAS-W.2017.154

M3 - Conference contribution

AN - SCOPUS:85035237588

T3 - Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017

SP - 243

EP - 250

BT - Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017

Y2 - 18 September 2017 through 22 September 2017

ER -

Fraud Analysis Approaches in the Age of Big Data-A Review of State of the Art

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Cite this