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Clustering Model of False Positive Elimination in Moroccan Fiscal Fraud Detection

  • Houda JihalEmail author
  • Soumaya Ounacer
  • Soufiane Ardchir
  • Mohammed Azouazi
Conference paper
  • 26 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1104)

Abstract

Every year government faces tax fraud with many methods. Big data and analytics enable government organizations to improve existing processes of detection and engage in entirely new types of analyses to allow tax authority to prevent tax fraud, reduce the cost of managing taxes and optimize public spending. Fraud detection often includes analyzing large datasets to locate irregularities. Anomaly detection helps by exploring every possible path to find fraudsters but it’s often results in large number of false positive, that is entries wrongly identified as fraud. When a taxpayer is wrongly flagged, the intervention of an audit agent is necessary and so a big time of investigations to finally lose time, effort and money.

The purpose of this paper is to propose a model of false positive elimination in Moroccan fiscal fraud detection based on clustering.

Keywords

Anomaly detection False positive elimination Tax fraud 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Houda Jihal
    • 1
    Email author
  • Soumaya Ounacer
    • 1
  • Soufiane Ardchir
    • 1
  • Mohammed Azouazi
    • 1
  1. 1.Faculty of Science Ben MsikHassan II University CasablancaCasablancaMorocco

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