Abstract
Many credit card businesses are no longer profitable due to antiquated and increasingly obsolete methods of acquiring customers, and as importantly, they followed suit when identifying ideal customers. The objective of this study is to identify the high spending and revolving customers through the development of proper parameters. We combined the back propagation neural network, decision tree and logistic methods as a way to overcome each method’s deficiency. Two sets of data were used to develop key eigenvalues that more accurately predict ideal customers. Eventually, after many rounds of testing, we settled on 14 eigenvalues with the lowest error rates when acquiring credit card customers with a significantly improved level of accuracy. It is our hope that data mining and big data can successfully utilize these advantages in data classification and prediction.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Malhotra, R., Malhotra, D.K.: Evaluating consumer loans using neural networks. Omega 31(2), 83–96 (2003)
Khandani, A.E., Kim, A.J., Lo, A.W.: Consumer credit-risk models via machine-learning algorithms. J. Bank. Finance 34(11), 2767–2787 (2010)
Zakaryazad, A., Duman, E., Kibekbaev, A.: Profit-based artificial neural network (ANN) trained by migrating birds optimization: a case study in credit card fraud detection. Department of Industrial Engineering, Ozyegin University, Istanbul, Turkey. A Kibekbaev Proceedings of European Conference on Data Mining, pp. 28–36 (2015)
Ye, Y.C.: Application and Implementation of Neural Network Models. Rulin Publishing House, Taipei (2004)
Chen, C.L., Kaber, D.B., Dempsey, P.G.: A new approach to applying feedforward neural networks to the prediction of musculoskeletal disorder. Appl. Ergon. 31(3), 269–282 (2000)
He, C.Z., Zhu, B., Zhang, M.Z., Zhuang, Y.Y., He, X.L., Du, D.Y.: Customers’ risk type prediction based on analog complexing. Procedia Computer Science, Business School, Sichuan University, Chengdu, China 55, 939–943 (2105)
Harrison, P., Gray, C.T.: Profiling for profit: a report on target marketing and profiling practices in the credit industry. A Joint Research Project by Deakin University and Consumer Action Law Centre (2012)
Rhomas, L.C.: A survey of credit and behavioral scoring: forecasting financial risk of lending to consumers. Int. J. Forecast. 16(2), 149–172 (2000)
Hamilton, R., Khan, M.: Revolving credit card holders: who are they and how can they be identified ? Serv. Ind. J. 21(3), 37–48 (2001)
Chuang, C.C.: Applying neural networks to factors analysis of credit card loan decisions. Master’s thesis, Department of Information Management, National Taiwan University of Science and Technology, Taipei (2005)
Hsieh, N.C.: An integrated data mining and behavioral scoring model of analyzing bank customers. Expert Syst. Appl. 27(4), 623–633 (2004)
Oreski, S., Oreski, D., Oreski, G.: Hybrid system with genetic algorithm and artificial neural networks and its application to retail credit risk assessment. Expert Syst. Appl. 39(16), 12605–12617 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, JP. (2016). Applied Neural Network Model to Search for Target Credit Card Customers. In: Berry, M., Hj. Mohamed, A., Yap, B. (eds) Soft Computing in Data Science. SCDS 2016. Communications in Computer and Information Science, vol 652. Springer, Singapore. https://doi.org/10.1007/978-981-10-2777-2_2
Download citation
DOI: https://doi.org/10.1007/978-981-10-2777-2_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2776-5
Online ISBN: 978-981-10-2777-2
eBook Packages: Computer ScienceComputer Science (R0)