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Classification based credit risk analysis: The case of Lending Club

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  • Aadi Gupta
  • Priya Gulati
  • Siddhartha P. Chakrabarty

Abstract

In this paper, we performs a credit risk analysis, on the data of past loan applicants of a company named Lending Club. The calculation required the use of exploratory data analysis and machine learning classification algorithms, namely, Logistic Regression and Random Forest Algorithm. We further used the calculated probability of default to design a credit derivative based on the idea of a Credit Default Swap, to hedge against an event of default. The results on the test set are presented using various performance measures.

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  • Aadi Gupta & Priya Gulati & Siddhartha P. Chakrabarty, 2022. "Classification based credit risk analysis: The case of Lending Club," Papers 2210.05136, arXiv.org.
    • Handle: RePEc:arx:papers:2210.05136
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    References listed on IDEAS

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    1. Duffie, Darrell & Singleton, Kenneth J, 1999. "Modeling Term Structures of Defaultable Bonds," The Review of Financial Studies, Society for Financial Studies, vol. 12(4), pages 687-720.
    2. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2020. "Corporate Default Predictions Using Machine Learning: Literature Review," Sustainability, MDPI, vol. 12(16), pages 1-11, August.
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