Abstract
In recent years, as China’s credit market continues to expand, a large number of P2P (person-to-person borrow or lend money in Internet Finance) platforms were born and developed. Most of the P2P platforms in China use data mining methods to evaluate the credit risk of loan applicants. Artificial neural network (ANN) is an emerging data mining tool and has good classification ability in many application fields. This paper presents a model of credit risk assessment based on flexible neural tree (FNT), which can reduce the overdue rate and save the analysis time. Overdue and non-overdue sample data are provided by the Jinan Hengxin Micro-Investment Advisory Co., Ltd., and used to build the model. Experiments show that the proposed model is more accurate and has less time cost for the overdue classification of credit risk assessment.
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Acknowledgements
This research was supported by the 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 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., Zhao, Y., Shao, P., Meng, Q. (2017). Credit Risk Assessment Based on Flexible Neural Tree Model. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_26
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DOI: https://doi.org/10.1007/978-3-319-59072-1_26
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