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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 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 languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages243-250
Number of pages8
ISBN (Electronic)9781509065585
DOIs
StatePublished - Oct 9 2017
Event2nd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017 - Tucson, United States
Duration: Sep 18 2017Sep 22 2017

Publication series

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

Other

Other2nd IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017
CountryUnited States
CityTucson
Period9/18/179/22/17

Keywords

  • Big Data
  • Data Mining
  • Fraud Analysis
  • Machine Learning
  • Statistical Modeling

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Computational Mechanics

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