Abstract
This paper introduces a machine learning algorithm to evaluate the risk level of the current P2P online lending platform in my country. This article is divided into two levels: firstly, estimate the risk of online loan platform; secondly, assess the credit risk of borrowers on the platform. Introduce the unsupervised learning algorithm in machine learning dichotomous k-means clustering, classify online lending platforms, analyze the performance of various platforms, determine the level of each category, and give the risk rating results of online lending platforms. Further use the supervised learning algorithm to study the credit risk of the borrower of the online lending platform. This paper uses the K-Means algorithm and related functions in AdaBoost to achieve data crawling of such dynamic web pages. After the end, it was found that there were still missing data on some platforms, and finally a large amount of information on the official website of the P2P online lending platform was manually checked to collect the required data. After collecting the data, the author processed the string by constructing a large number of regular expressions to correct obvious errors and provide data consistency. The experimental research results show that this article sorts the classification results by analyzing the variable values of the online loan platform to obtain different classification levels. In order to better rate the online loan platform, the rating results are more comprehensive and accurate.
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References
Teluguntla, P., Thenkabail, P.S., Oliphant, A., et al.: A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google earth engine cloud computing platform. ISPRS J. Photogram. Remote Sens. 144, 325–340 (2018)
Xia, K., Guo, H., He, S., et al.: Binary classification model based on machine learning algorithm for the DC serial arc detection in electric vehicle battery system. Power Electron. IET 12(1), 112–119 (2019)
Grassi, M., Perna, G., Caldirola, D., et al.: A clinically-translatable machine learning algorithm for the prediction of Alzheimer’s disease conversion in individuals with mild and premild cognitive impairment. J. Alzheimers Disease 61(4), 1555–1573 (2018)
Meng, B., Yi, S., Liang, T., et al.: Modeling alpine grassland above ground biomass based on remote sensing data and machine learning algorithm: a case study in the east of Tibetan Plateau, China. IEEE J. Selected Top. Appl. Earth Observ. Remote Sens. PP(99), 1 (2020)
Nain, S.S., Sai, R., Sihag, P., et al.: Use of machine learning algorithm for the better prediction of SR peculiarities of WEDM of Nimonic-90 superalloy. Arch. Mater. Sci. Eng. 1(95), 12–19 (2019)
Hu, R., Liu, M., He, P., et al.: Can investors on P2P lending platforms identify default risk? Int. J. Electron. Commerce 23(1), 63–84 (2019)
Chen, J., Zhang, Y., Yin, Z.: education premium in the online peer-to-peer lending marketplace: evidence from the big data in China. Singapore Econ. Rev. 63(1), 1–20 (2018)
Huang, R.H.: Online P2P lending and regulatory responses in China: opportunities and challenges. Europ. Bus. Organ. Law Rev. 19(1), 63–92 (2018)
Li, Q., Chen, L., Zeng, Y.: The mechanism and effectiveness of credit scoring of P2P lending platform: Evidence from Renrendai.com. China Financ. Rev. Int. 8(3), 256–274 (2018)
Zhibin, W., Kaiyi, W., Shouhui, P., et al.: Segmentation of crop disease images with an improved K-means clustering algorithm. Appl. Eng. Agric. 34(2), 277–289 (2018)
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Xie, W. (2021). The Analysis on the Application of Machine Learning Algorithms in Risk Rating of P2P Online Loan Platforms. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2020. Advances in Intelligent Systems and Computing, vol 1303. Springer, Singapore. https://doi.org/10.1007/978-981-33-4572-0_206
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DOI: https://doi.org/10.1007/978-981-33-4572-0_206
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