Abstract
This study analyzes data from Lending Club 2011 January to 2016 January. We use survival analysis and proportional hazards model to find what loan characteristics will have lower default rate in high-risk group and low risk group. The grouping way is through the lending Club credit score. Our study provides a way to analyze loan characteristics to reduce information asymmetry and default rate. To earn higher interest and take the principal back have always been the biggest issue in the financial world. Our research gives the advices through survival analysis with empirical data. The results show the repayment characteristics of the high-risk group and the low-risk group is similar. Except for the following four characteristics, ‘mortgage’, ‘education’, ‘home improvement’, and ‘medical’ are opposite.
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References
Emekter, R., Tu, Y., Jirasakuldech, B., Lu, M.: Evaluating credit risk and loan performance in online peer-to-peer (P2P) lending. Appl. Econ. 47(1), 54–70 (2015). https://doi.org/10.1080/00036846.2014.962222
Jin, Y., Zhu, Y.: A data-driven approach to predict default risk of loan for online peer-to-peer (P2P) lending. In: Proceedings - 2015 5th International Conference on Communication Systems and Network Technologies, CSNT 2015, pp. 609–613 (2015). https://doi.org/10.1109/CSNT.2015.25
Malik, M., Thomas, L.C.: Modelling credit risk of portfolio of consumer loans. J. Oper. Res. Soc. 61(3), 411–420 (2010). https://doi.org/10.1057/jors.2009.123
Serrano-Cinca, C., Gutiérrez-Nieto, B., López-Palacios, L.: Determinants of default in P2P lending. PLoS ONE 10(10), e0139427 (2015). https://doi.org/10.1371/journal.pone.0139427
Lin, M., Prabhala, N.R., Viswanathan, S.: Judging borrowers by the company they keep: friendship networks and information asymmetry in online peer-to-peer lending. Manage. Sci. 59(1), 17–35 (2013). https://doi.org/10.1287/mnsc.1120.1560
Freedman, S., Jin, G.Z.: The information value of online social networks: lessons from peer-to-peer lending. Int. J. Ind. Organ. 51, 185–222 (2017)
Bachmann, A., et al.: Online peer-to-peer lending-a literature review. J. Internet Bank. Commer. 16(2), 1 (2011)
Cox, D.R.: Models and life-tables regression. J. R. Stat. Soc. Ser. B 34(2), 187–220 (1972)
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Jin, BH., Li, YM., Ho, KT. (2024). Analysis of the Characteristics of Different Peer-To-Peer Risky Loans. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F., Colla, V. (eds) Information Systems and Technologies. WorldCIST 2023. Lecture Notes in Networks and Systems, vol 799. Springer, Cham. https://doi.org/10.1007/978-3-031-45642-8_9
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DOI: https://doi.org/10.1007/978-3-031-45642-8_9
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