Abstract
Automated credit approval helps credit-granting institutions in reducing time and efforts in analyzing credit approval requests and to distinguish good customers from bad ones. Enhancing the automated process of credit approval by integrating it with a good business intelligence (BI) system puts financial institutions and banks in a better position compared to their competitors. In this paper, a novel hybrid approach based on neural network model called Cycle Reservoir with regular Jumps (CRJ) and Support Vector Machines (SVM) is proposed for classifying credit approval requests. In this approach, the readout learning of CRJ will be trained using SVM. Experiments results confirm that in comparison with other data mining techniques, CRJ with SVM readout gives superior classification results.
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References
Khashman, A.: Neural networks for credit risk evaluation: investigation of different neural models and learning schemes. Expert Syst. Appl. 37(9), 6233–6239 (2010)
Khashman, A.: A neural network approach for credit risk evaluation. Int. J. Neural Syst. 19(4), 285–294 (2009)
Bei, H.: Research on credit card approval models based on data mining technology. Comput. Eng. Des. 29(11), 2989–2991 (2008)
Yu, L., Yao, X., Wang, S., Lai, K.: Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection. Expert Syst. Appl. 38, 15392–15399 (2011)
Khandani, A., Kim, A., Lo, A.: Consumer credit-risk models via machine-learning algorithms. J. Bank. Finance 34, 2767–2787 (2010)
Sakprasat, S., Sinclair, M.: Classification rule mining for automatic credit approval using genetic programming. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 548–555 (2007)
Kraus, A.: Recent methods from statistics and machine learning for credit scoring, Ph.D. thesis, Ludwig-Maximilians-Universitat Munchen (2014)
Lukosevicius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127–149 (2009)
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks, Technical report gmd report 148. German National Research Center for Information Technology, Technical report (2001)
Rodan, A., Tino, P.: Minimum complexity echo state network. IEEE Trans. Neural Netw. 22(1), 131–144 (2011)
Rodan, A., Tino, P.: Simple deterministically constructed cycle reservoirs with regular jumps. Neural Comput. 24(7), 1822–1852 (2012)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Vapnik, V.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 5, 988–999 (1999)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Base Learning Methods. Cambridge University Press, Cambridge (2000)
Vapnik, V., Vashist, A.: A new learning paradigm: learning using privileged information. Neural Netw. 22(5–6), 544–557 (2009)
Chan, W., Cheung, K., Harris, C.: On the modelling of nonlinear dynamic system using support vector neural networks. Eng. Appl. Artif. Intell. 14, 105–113 (2001)
Zhu, G., Liu, S., Yu, J.: Support vector machine and its applications to function approximation. J. East China Univ. Sci. Technol. 5, 555–559 (2002)
Marcano-Cedeno, A., Marin-de-la-Barcena, A., Jimenez-Trillo, J., Pinuela, J., Andina, D.: Artificial metaplasticity neural network applied to credit scoring. Int. J. Neural Syst. 21, 311–317 (2011)
Zhao, Z., Xu, S., Kang, B.H., Kabir, M., Liu, Y., Wasinger, R.: Investigation and improvement of multi-layer perception neural networks for credit scoring. Expert Syst. Appl. 42(7), 3508–3516 (2015)
Hens, A., Tiwari, M.: Computational time reduction for credit scoring: an integrated approach based on support vector machine and stratified sampling method. Expert Syst. Appl. 39, 6774–6781 (2012)
Wang, J., Hedar, A., Wang, S., Mac, J.: Rough set and scatter search metaheuristic based feature selection for credit scoring. Expert Syst. Appl. 39, 6123–6128 (2012)
Zhou, X., Jiang, W., Shi, Y., Tian, Y.: Credit risk evaluation with kernel-based affine subspace nearest points learning method. Expert Syst. Appl. 38, 4272–4279 (2011)
Chen, F., Li, F.: Combination of feature selection approaches with SVM in credit scoring. Expert Syst. Appl. 37, 4902–4909 (2010)
Luo, S., Cheng, B., Hsieh, C.: Prediction model building with clustering-launched classification and support vector machines in credit scoring. Expert Syst. Appl. 36, 7562–7566 (2009)
Lichman, M.: UCI machine learning repository (2013)
Lai, K.K., Yu, L., Zhou, L., Wang, S.-Y.: Credit risk evaluation with least square support vector machine. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 490–495. Springer, Heidelberg (2006)
Yu, L., Yue, W., Wang, S., Lai, K.K.: Support vector machine based multiagent ensemble learning for credit risk evaluation. Expert Syst. Appl. 37, 1351–1360 (2010)
Wang, Y., Wang, S., Lai, K.K.: A new fuzzy support vector machine to evaluate credit risk. IEEE Trans. Fuzzy Syst. 13, 820–831 (2005)
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Rodan, A., Faris, H. (2016). Credit Risk Evaluation Using Cycle Reservoir Neural Networks with Support Vector Machines Readout. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_57
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DOI: https://doi.org/10.1007/978-3-662-49381-6_57
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