Abstract
Nowadays, the use of credit card becomes an integral part of modern economies. Still, predicting credit card defaulters is considered as the most important. So, its assessment becomes a crucial task. In this context, a few Data mining and intelligent artificial techniques were used for extracting meaningful patterns from a given dataset. In this study, we consider credit card risk assessment as a classification problem based on genetic programming (GP) algorithm, where the goal is to maximize the accuracy of the generated model. We evaluate our proposal using customers default payments dataset of Taiwan, and, we compared it with some existing works. The performance of our finding leads to the assumption that GP is able to generate an effective assessment model based on IF-THEN rules. The result confirms the efficiency of our algorithm with an average of more than 86% of precision, recall, and accuracy.
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References
Li, J.-P.: Applied neural network model to search for target credit card customers. In: Berry, M.W., Hj. Mohamed, A., Yap, B.W. (eds.) SCDS 2016. CCIS, vol. 652, pp. 13–24. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-2777-2_2
Pasha, M., Fatima, M., Dogar, A.M., Shahzad, F.: Performance comparison of data mining algorithms for the predictive accuracy of credit card defaulters. Int. J. Comput. Sci. Netw. Secur. 17(3), 178–183 (2017)
William, H.G.: A Statistical Model for Credit Scoring. Department of Economics, New York University, New York (1992)
Sahin, Y., Duman, E.: Detecting credit card fraud by decision trees and support vector machines. In: International Multi Conference of Engineers and Computer Scientists (2011)
Setiono, R., Baesens, B., Mues, C.: A note on knowledge discovery using neural networks and its application to credit card screening. Eur. J. Oper. Res. 192, 326 (2009)
Koklu, M., Sabanci, K.: Estimation of credit card customers’ payment status by using KNN and MLP. Int. J. Intell. Syst. Appl. Eng. 4, 249–251 (2016). ISSN: 2147-6799
Sahin, Y., Duman, E.: Detecting credit card fraud by ANN and logistic regression (2011)
Lee, T.S., Chiu, C.C., Lu, C.J., Chen, I.-F.: Credit scoring using the hybrid neural discriminant technique. Expert Syst. Appl. 23(3), 245–254 (2002)
Cinko, M.: Comparison of credit scoring techniques. Istanbul Commer. Univ. Soc. Sci. J. 9, 143–153 (2006)
Akkoc, S.: An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: the case of Turkish credit card data. Eur. J. Oper. Res. 222, 168–178 (2012)
Chou, T.N.: A Novel Prediction Model for Credit Card Risk Management. Chaoyang University of Technology, Taichung (2017)
Yeh, I.C., Lien, C.H.: The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Syst. Appl. 36, 2473–2480 (2009)
Ajay, A., Venkatesh, A.: Jacob, S.G: Prediction of credit-card defaulters: a comparative study on performance of classifiers. Int. J. Comput. Appl. 145(7), 36–41 (2016). (0975–8887)
Napierala, K., Stefanowski, J.: BRACID: a comprehensive approach to learning rules from imbalanced data. J. Intell. Inf. Syst. 39(2), 335–373 (2012)
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Soui, M., Smiti, S., Bribech, S., Gasmi, I. (2018). Credit Card Default Prediction as a Classification Problem. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham. https://doi.org/10.1007/978-3-319-92058-0_9
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DOI: https://doi.org/10.1007/978-3-319-92058-0_9
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