Abstract
Due to the convenience of the Peer to Peer (P2P) platform loan, the P2P platform is becoming more and more popular. Medical care has always been the most concerned issue. The emergence of medical P2P solves the insufficient funds problems of many people. In order to predict default customers, reduce the credit risk of credit institutions, and shorten credit approval time, this article uses historical data to establish a credit scoring model.
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References
Jasmina, N., Amar, S.: Using data mining approaches to build credit scoring model. In: 17th International Symposium INFOTEH-JAHORINA, East Sarajevo (2018)
Luis Eduardo Boiko, F., Heitor Murilo, G.: Improving credit risk prediction in online peer-to-peer (P2P) lending using imbalanced learning techniques. In: 2017 International Conference on Tools with Artificial Intelligence, Boston, pp. 175–181 (2017)
Wang, Y.: Applied analysis of social network data in personal credit evaluation. In: Aiello, M., Yang, Y., Zou, Y., Zhang, L.-J. (eds.) AIMS 2018. LNCS, vol. 10970, pp. 221–232. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94361-9_17
Durga, C.R., Manicka, R.: A relative evaluation of the performance of ensemble learning in credit scoring. In: 2016 IEEE International Conference on Advances in Computer Applications (ICACA), Coimbatore, pp. 161–165 (2017)
Xiao, W.B.: A study of personal credit scoring models on support vector machine with optimal choice of kernel function parameters. Syst. Eng.-Theor. Pract. 26(10), 73–79 (2006)
Road, G.E.: Comparative study of individual and ensemble methods of classification for credit scoring. In: 2017 International Conference on Inventive Computing and Informatics (ICICI), 23–24 November 2017 (2017)
Guo, G., Zhu, F., Chen, E., Liu, Q., Wu, L., Guan, C.: From footprint to evidence: an exploratory study of mining social data for credit scoring. Assoc. Comput. Mach. (ACM) 10(4), Article No. 22 (2016)
Zhang, X., Zhou, Z., Yang, Y.: A novel credit scoring model based on optimized random forest. In: IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). Las Vegas, pp. 60–65 (2018)
Shi, X., Li, Q.: An accident prediction approach based on XGBoost. In: 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nanjing, pp. 1–7 (2017)
Zhong, J., Sun, Y., et al.: XGBFEMF: an XGBoost-based framework for essential protein prediction. IEEE Trans. NanoBiosci. (Early Access) 1–8 (2018)
Chen, T., Guestrin, C.: XGBoost: reliable large-scale tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, USA, pp. 785–794 (2016)
Armin, L., Firman, A.: Ensemble GradientBoost for increasing classification accuracy of credit scoring. In: 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), Kuta Bali, pp. 1–4 (2017)
Cox, D.R.: The regression analysis of binary sequences. J. Royal Stat. Soc. Ser. B (Methodol.), 215–242 (1958)
Acknowledgement
This work was supported by NSFC(91646202), National Social Science Foundation of China No. 15CTQ028, Research/Project 2017YB142 supported by Ministry of Education of The People’s Republic of China, the 1000-Talent program.
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Li, R., Zhao, C., Li, X., Zhang, G., Zhang, Y., Xing, C. (2018). Comparative Analysis of Medical P2P for Credit Scores. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds) Web Information Systems and Applications. WISA 2018. Lecture Notes in Computer Science(), vol 11242. Springer, Cham. https://doi.org/10.1007/978-3-030-02934-0_29
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DOI: https://doi.org/10.1007/978-3-030-02934-0_29
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