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: 254

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


Abdelahlim A, Traore I (2009). Identity application fraud detection using web mining and rule-based decision tree. Int. J. Netw. Comput. Secur. 1(1):31-44.


Ajiboye AR, Abdul-Hadi J, Akintola AG, Ameen AO (2015). Anomaly Detection in Dataset for Improved Model Accuracy Using DBSCAN Clustering Algorithm. Afr. J. Comp ICTs. 8(1):39-46.


Keerthi A, Remya MS, Nitha L. (2015). Detection of Credit Card Fraud using SOM Neural Network.


Nobel F (2015). Data Mining Rule Based Classification. 



Ogwueleka FN (2011). Data Mining Application in Credit Card Fraud Detection System. School of Engineering. Taylor's University. J. Eng. Sci. Technol. 6(3):311-322.


Salem SM (2012). An Overview of Research on Auditor's Responsibility to Detect Fraud on Financial Statements. J. Glob. Bus. Manag. 8(2):218-229.


Salganicoff M (1993). Density adaptive learning and forgetting. In Proceeding of the Tenth International Conference on Machine Learning. 276-283 Amherst, MA. Morgan Kaufmann


Sander J, Ester M, Kriegel HP, Xu X (1998). Density-based Clustering in Spatial Databases: The Algorithm DBSCAN and its Applications. Data Min. Knowl. Discov. 2(2):169-194.


Saravanan SK, Babu Suresh GNK (2013). An Analysis of Fraud Detection from Credit Card using Web Data Mining Techniques. Intl. J. Advanc. Res. Data Mining Cloud Comput. 1(1).


Seeja KR, Masoumeh Z (2014). Fraud Miner. A Novel Credit Card Fraud Detection Model Based on Frequent Item set Mining. The Sci. World J. Article ID 252797, 10p.


Sevda S, Mohammad AB (2015). The Study of Fraud Detection in Financial and Credit Institutions with Real Data. J. Comput. Sci. Eng. 5(2):30-36


Srivastava A, Kundu A, Sural S, Majumdar AK (2008). Credit card fraud detection using hidden Markov model. IEEE Trans. Depend. Secure Comp. 5(1):37-48.


Stolfo SJ, Fan DW, Lee W, Prodromidis A, Chan PK, (1997). Credit card fraud detection using meta-learning: Issues and initial results. Proceedings of AAAI-97 Workshop on AI Approaches to Fraud Detection and Risk Management, AAAI Press, Menlo Park, California pp. 83-90.


Wikipedia (2015). Support Vector Machines. 


Witten IH, Frank E, Hall MA (2011). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, Calif, USA, 3rd edition.