Fraud Data Analytics Tools and Techniques in Big Data Era

Sara Makki, Rafiqul Haque, Yehia Taher, Zainab Assaghir, Gregory DItzler, Mohand Saïd Hacid, Hassan Zeineddine

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

2 Scopus citations

Abstract

Fraudulent activities (e.g., suspicious credit card transaction, financial reporting fraud, and money laundering) are critical concerns to various entities including bank, insurance companies, and public service organizations. Typically, these activities lead to detrimental effects on the victims such as a financial loss. Over the years, fraud analysis techniques underwent a rigorous development. However, lately, the advent of Big data led to vigorous advancement of these techniques since Big Data resulted in extensive opportunities to combat financial frauds. Given that the massive amount of data that investigators need to sift through, massive volumes of data integrated from multiple heterogeneous sources (e.g., social media, blogs) to find fraudulent patterns is emerging as a feasible approach.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages186-187
Number of pages2
ISBN (Electronic)9781538619391
DOIs
StatePublished - Oct 9 2017
Event4th IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017 - Tucson, United States
Duration: Sep 18 2017Sep 22 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017

Other

Other4th IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017
CountryUnited States
CityTucson
Period9/18/179/22/17

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Control and Optimization

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