A Comparative Study of Machine Learning Classifiers for Credit Card Fraud Detection


  • Md. Nur-E-Arefin Department of Computer Science & Engineering, Royal University of Dhaka, Bangladesh
  • Mohammad Sultan Mahmud Department of Computer Science & Engineering, Shenzhen University, 3688 Nanhai Boulevard, Nanshan, Shenzhen, China




Computational Intelligence, Credit Card Fraud Detection, Machine Learning, Data Mining.


Now a day’s credit card transactions have been gaining popularity with the growth of e-commerce and shows tremendous opportunity for the future. Therefore, due to surge of credit card transaction, it is a crying need to secure it . Though the vendors and credit card providing authorities are showing dedication to secure the details of these transactions, researchers are searching new scopes or techniques to ensure absolute security which is the demand of time. To detect credit card fraud, along with other technologies, applications of machine learning and computational intelligence can be used and plays a vital role. For detecting credit card anomaly, this paper analyzes and compares some popular classifier algorithms. Moreover, this paper focuses on the performance of the classifiers. UCSD -FICO Data Mining Contest 2009 dataset were used to measure the performance of the classifiers. The final results of the experiment suggest that (1) meta and tree classifiers perform better than other types of classifiers, (2) though classification accuracy rate is high but fraud detection success rate is low. Finally, fraud detection rate  should be taken into consideration to assess the performance of the classifiers in a credit card fraud detection system.


Duman E, Sahin Y. A Comparison of Classification Models on Credit Card Fraud Detection with respect to Cost-Based Performance Metrics. NATO Science for Peace and Security Series E: Human and Societal Dynamics. IOS Press. 2011;88:88–99.

Ngai EWT, Hu Y, Wong YH, Chen Y, Sun X. The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decision Support Systems. 2011;50(3):559–569.

Zareapoor M, Seeja KR, Alam AM. Analyzing credit card fraud detection techniques: based on certain design criteria. International Journal of Computer Application. 2012;52(3):35–42.

Carter C, Catlett J. Assessing credit card applications using machine learning. IEEE Expert: intelligent systems and their applications. 1987;2:71–79.

Hanagandi V, Dhar A, Buescher K. Density-based clustering and radial basis function modeling to generate credit card fraud scores. Computational Intelligence for Financial Engineering. 1996.

Ghosh S, Reilly DL. Credit card fraud detection with a neural-network. In Proceedings of the 27th Hawaii International Conference on System Sciences. 1994;3:621–630.

Dorronsoro JR, Ginel F, Sanchez C, Cruz CS. Neural fraud detection in credit card operations. In IEEE Transactions on Neural Networks. 1997;8:827-834.

Brause R, Langsdorf T, Hepp M. Credit card fraud detection by adaptive neural data mining. Proceedings of the 11thIEEE International Conference on Tools with Artificial Intelligence. 1999. p. 103-106.

S´anchez D, Vila MA, Cerda L, Serrano JM. Association rules applied to credit card fraud detection. Expert Systems with Applications. 2009;36(2):3630–3640.

Pearl J. A Probabilistic Calculus of Actions, UAI'94 Proceedings of the Tenth International Conference on Uncertainty in Artificial Intelligence. San Mateo CA: Morgan Kaufman. 1994. p. 454–462.

Breiman L. Random forests. Machine Learning. 2001;45(1):5–32.

Hall M, Frank E. Combining Naive Bayes and Decision Tables. In Proceedings of the 21st Florida Artificial Intelligence Society Conference (FLAIRS). 2008. P. 318-319.

Quinlan R. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers. San Mateo CA. 1993.

Landwehr N, Hall M, Frank E. Logistic model trees. Machine Learning. 2005;59:161–205.

Aha, D, Kibler, D. Instance-based learning algorithms. Machine Learning. 1991. Vol.6. p. 37-66.

Frank E, Hall M, Pfahringer B. Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence. 2003. p. 249-256.

Breiman L. Bagging predictors. Machine Learning. 1996;24(2):123-140.

Frank E, Wang Y, Inglis S, Holmes G, Witten IH. Using model trees for classification. Machine Learning. 1998;32(1):63-76.

Dong L, Frank E, Kramer S. Ensembles of balanced nested dichotomies for multi-class problems. Knowledge Discovery in Databases: Pkdd. 2005;3721:84–95.

Freund Y, Schapire RE. Experiments with a new boosting algorithm. Machine Learning: Proceedings of the Thirteenth International Conference. 1996. p.148–156.



2020-02-28 — Updated on 2021-01-23


How to Cite

Nur-E-Arefin, M., & Mohammad Sultan Mahmud. (2021). A Comparative Study of Machine Learning Classifiers for Credit Card Fraud Detection. International Journal of Innovative Technology and Interdisciplinary Sciences, 3(1), 395–406. https://doi.org/10.15157/IJITIS.2020.3.1.395-406 (Original work published February 28, 2020)