Abstract
Credit scoring model is a popular tool for the financial institutions (FIs) to assess their customers’ credit risk. Since the large amount of money in credit granting business for FIs, an improvement in the accuracy of the credit scoring model to recognize good and bad customers, even a fraction of one percent can help to reduce significant loss. Some existing researches suggest that adaboost model can help to improve the accuracy of classification for base classifiers. In this paper, two adaboost models with different weights strategies are introduced for credit scoring. Multilayer perceptron neural network with back-propagation training method is employed as the base classifier. The models are tested on one real-world dataset and the experimental results show that adaoosting neural network model is outperformed than the single neural network and traditional adaboost model.
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References
The Nilson Report. Oxnard, California (October 2003)
Thomas, L.C., Oliver, R.W., Hand, D.J.: A Survey of the Issues in Consumer Credit Modelling Research. Journal of the Operational Research Society 56, 1006–1015 (2005)
Thomas, L.C., Edelman, D.B., Crook, J.N.: Credit Scoring and Its applications. Siam, Philadelphia (2002)
Zhang, G.: Neural Networks for Classification: a Survey. Systems, Man, and Cybernetics. IEEE Transactions on Applications and Reviews 30, 451–462 (2000)
Jensen, H.L.: Using Neural Networks for Credit Scoring. Managerial Finance 18, 15–26 (1992)
West, D.: Neural Network Credit Scoring Models. Computers & Operations Research 27, 1131–1152 (2000)
Baesens, B., Van Gestel, T., Stepanova, M., Van den Poel, D., Vanthienen, J.: Neural Network Survival Analysis for Personal Loan Data. Journal of the Operational Research Society 56, 1089–1098 (2005)
Bensic, M., Sarlija, N., Zekic-Susac, M.: Modelling Small-business Credit Scoring by Using Logistic Regression, Neural Networks and Decision Trees. International Journal of Intelligent Systems in Accounting Finance & Management 13, 133–150 (2005)
Desai, V.S., Crook, J.N.: A Comparison of Neural Networks and Linear Scoring Models in the Credit Union Environment. European Journal of Operational Research 95, 24–37 (1996)
Wang, W., Xu, Z., Lu, J.: Three Improved Neural Network Models for Air Quality Forecasting. Engineering Computations 20, 192–210 (2003)
Ritchie, M., White, B., Parker, J., Hahn, L., Moore, J.: Optimization of Neural Network Architecture Using Genetic Programming Improves Detection and Modeling of Gene-gene. Interactions in Studies of Human Diseases 4, 285–301 (2003)
Schwenk, H., Bengio, Y.: Boosting Neural Networks 12, 1869–1887 (2000)
Tsai, C.-F., Wu, J.-W.: Using Neural Network Ensembles for Bankruptcy Prediction and Credit Scoring. Expert Systems with Applications 34, 2639–2649 (2008)
Breiman, L.: Prediction Games and Arcing Algorithms. Neural Computation 11, 1493–1517 (1999)
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Zhou, L., Lai, K.K. (2009). Adaboosting Neural Networks for Credit Scoring. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_93
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DOI: https://doi.org/10.1007/978-3-642-01216-7_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01215-0
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