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
N1 - Publisher Copyright:
© 2017 IEEE.
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 -