Credit risk assessment in person-to-person lending using weighted kernel regression methods and simple ranking

Document Type : Original Article

Authors
Habil Khavari 1
fatemeh ahmadi 2
Mojtaba Moradpour 3
Rahmatollah Mohamadipour 3
Reza Mashhadizadeh 4
1 Accounting and financeDepartment, Ilam Branch, Islamic Azad University, Ilam, Iran
2 Accounting and finance Department, Ilam Branch, Islamic Azad University, Ilam, Iran.
3 Accounting and finance, Ilam Branch, Islamic Azad University, Ilam, Iran
4 Management Department, Karaj Branch, Islamic Azad University, Karaj, Iran
10.30495/jik.2023.62005.4296
Abstract
Person-to-person lending is another method of investment that eliminates the involvement of traditional financial institutions and has been growing more and more in recent years in other countries. The purpose of this article is to evaluate the credit risk based on the sample with the ability to evaluate the risk and return of each loan using logistic regression with Gaussian function that does not have a ranking approach in person-to-person lending. Lending Club's lending platform has been used to confirm and validate the effectiveness of the proposed method. that each borrower has a credit profile. The k-fold validation method has been used to determine the training and test samples. Therefore, according to ten thousand data samples, seven thousand random loan samples have been considered as training loan samples and the remaining three thousand loan samples have been considered as test loan samples. The results of the tests show the better performance of the data-driven investment method based on nuclear regression compared to the ranking methods in the application of person-to-person lending.
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