African Journal of
Mathematics and Computer Science Research

  • Abbreviation: Afr. J. Math. Comput. Sci. Res.
  • Language: English
  • ISSN: 2006-9731
  • DOI: 10.5897/AJMCSR
  • Start Year: 2008
  • Published Articles: 227

Full Length Research Paper

A multi-algorithm data mining classification approach for bank fraudulent transactions

Oluwafolake Ayano
  • Oluwafolake Ayano
  • Department of Computer Science, University of Ibadan, Nigeria.
  • Google Scholar
Solomon O. Akinola
  • Solomon O. Akinola
  • Department of Computer Science, University of Ibadan, Nigeria.
  • Google Scholar


  •  Received: 22 February 2017
  •  Accepted: 19 April 2017
  •  Published: 30 June 2017

Abstract

This paper proposes a multi-algorithm strategy for card fraud detection. Various techniques in data mining have been used to develop fraud detection models; it was however observed that existing works produced outputs with false positives that wrongly classified legitimate transactions as fraudulent in some instances; thereby raising false alarms, mismanaged resources and forfeit customers’ trust. This work was therefore designed to develop a hybridized model using an existing technique Density-Based Spatial Clustering of Applications with Noise (DBSCAN) combined with a rule base algorithm to reinforce the accuracy of the existing technique. The DBSCAN algorithm combined with Rule base algorithm gave a better card fraud prediction accuracy over the existing DBSCAN algorithm when used alone.

Key words: Card fraud detection, density-based spatial clustering of applications with noise (DBSCAN), rule base algorithm, data mining.