Abstract
At present, with continuously expanding of Chinese credit market, thus large amounts of P2P (person-to-person borrow or lend money in Internet Finance) platform were born and have been in development. Most of P2P platform in China carries out the credit risk evaluation of loan applicant by data mining method. As an emerging data mining tool, the artificial neural network has better classification capability. The improvement of risk assessment capabilities of applicant can effectively reduce the overdue rate of analysis, thus in this paper, a kind of credit risk evaluation model based on the Long Short-Term Memory (LSTM) model is presented. The sample data of overdue and non-overdue credits are provided by Hengxin Investment Consulting Co., Ltd. in Jinan, by which the model is established. After the trial, this model is applied to the aspect of overdue classification of credit evaluation with higher accuracy.
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Jiang, D., Li, X.: The study on the credit risk assessment of borrower in P2P network of China. In: Xu, J., Hajiyev, A., Nickel, S., Gen, M. (eds.) Proceedings of the Tenth International Conference on Management Science and Engineering Management. AISC, vol. 502, pp. 1619–1630. Springer, Singapore (2017). doi:10.1007/978-981-10-1837-4_131
Guo, Y., Zhou, W., Luo, C., Liu, C., Xiong, H.: Instance-based credit risk assessment for investment decisions in P2P lending. Eur. J. Oper. Res. 249(2), 417–426 (2016)
Blanco, A., Mejias, R., Lara, J., Rayo, S.: Credit scoring models for the microfinance industry using neural networks: evidence from Peru. Exp. Syst. Appl. 40(1), 356–364 (2013)
Heiat, A.: Comparing performance of data mining models for computer credit scoring. J. Int. Fin. Econ. 12(1), 78–83 (2012)
Chen, N., Ribeiro, B., Chen, A.: Financial credit risk assessment: a recent review. Artif. Intell. Rev. 45(1), 1–23 (2016)
Oricchio, G., Lugaresi, S., Crovetto, A., Fontana, S.: Banking crisis and SME credit risk assessment. In: Oricchio, G., Crovetto, A., Lugaresi, S., Fontana, S. (eds.) SME Funding, pp. 1–6. Palgrave Macmillan, London (2017)
Khashman, A.: Neural network for credit risk evaluation: investigation of different neural models and learning schemes. Exp. Syst. Appl. 37(9), 6233–6239 (2010)
Bekhet, H., Eletter, S.: Credit risk assessment model for Jordanian commercial banks: neural scoring approach. Rev. Dev. Finance 4(1), 20–28 (2014)
Wang, L., Chen, Y., Zhao, Y., Meng, Q., Zhang, Y.: Credit management based on improved BP neural network. IHMSC 1, 497–500 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Bao, W., Chen, Y., Wang, D.: Prediction of protein structure classes with flexible neural tree. Bio-Med. Mater. Eng. 24(6), 3797–3806 (2014)
Ji, Z., et al.: NMFBFS: a NMF-based feature selection method in identifying pivotal clinical symptoms of Hepatocellular carcinoma. Comput. Math. Methods Med. 2015, 1–12 (2015)
Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition (in Chinese). Publishing House of Electronic Industry of China, May 1996
Ji, Z., Xia, Q., Meng, G.: A review of parameter learning methods in bayesian network. In: Huang, D.-S., Han, K. (eds.) ICIC 2015. LNCS, vol. 9227, pp. 3–12. Springer, Cham (2015). doi:10.1007/978-3-319-22053-6_1
Xu, L.-L., et al.: Immune-based rough sets attribute reduction algorithm and its application. Comput. Eng. Des. 30(22), 5158–5161 (2009)
Huang, D.S., Du, J.-X.: A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Trans. Neural Netw. 19(12), 2099–2115 (2008)
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: Continual prediction with LSTM. Neural Comput. 12(10), 2451–2471 (2000)
Han, S.-Y., Chen, Y.-H., Tang, G.-Y.: Sensor fault and delay tolerant control for networked control systems subject to external disturbances. Sensors 17, 700 (2017)
Han, S.-Y., Zhang, C.-H., Tang, G.-Y.: Approximation optimal vibration for networked nonlinear vehicle active suspension with actuator time delay. Asian J. Control (2017). doi:10.1002/asjc.1419
Acknowledgements
This research was supported by Na National Key Research and Development Program of China (No. 2016YFC0106000), National Natural Science Foundation of China (Grant No. 61302128, 61573166, 61572230, 61671220, 61640218), the Youth Science and Technology Star Program of Jinan City (201406003), the Natural Science Foundation of Shandong Province (ZR2013FL002), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. ZR2016FB14), the Project of Shandong Province Higher Educational Science and Technology Program, China (Grant No. J16LN07), the Shandong Province Key Research and Development Program, China (Grant No. 2016GGX101022).
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Zhang, Y., Wang, D., Chen, Y., Shang, H., Tian, Q. (2017). Credit Risk Assessment Based on Long Short-Term Memory Model. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_62
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DOI: https://doi.org/10.1007/978-3-319-63312-1_62
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