Peer-To-Peer Lending: Classification in the Loan Application Process

Wei, Xinyuan; Gotoh, Jun‐ya; Uryasev, Stan

doi:10.3390/risks6040129

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…To quantitatively measure the creditworthiness of financial companies and credit unions, as one of the components of assessing the risk of their lending, the work uses the coefficient method, which is characterized by the simplicity of calculations and interpretation of results, the ability to identify trends in changes in the financial condition of financial institutions, and so on (Wei et al, 2018). To form a system of quantitative indicators, the author used criteria for the selection of financial indicators, which are used in the works of various authors.…”

Section: Methodsmentioning

confidence: 99%

Retracted: Internal procedures of the risk‐oriented lending process in the bank

Ushanov

2022

Risk Manage Insurance Review

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Information about the financial condition of financial institutions is the basis for assessing the risk of their lending. In the conditions of economic instability, the validity and effectiveness of management decisions significantly depend on the quality of financial and analytical calculations. To take into account, the specifics of the activities of various types of financial institutions when assessing the risk of their lending, the author put forward a hypothesis about the advisability of clarifying the composition of indicators used to assess the creditworthiness of borrowers. The analysis of the creditworthiness of financial companies and credit unions has been carried out. It was found that, during 2020–2021, no financial institution had the highest creditworthiness; at the same time, financial companies were more capable of meeting loan obligations than credit unions. A system of quantitative and qualitative indicators has been formed using correlation and factor analysis to assess the risk of lending to the activities of financial companies and credit unions. Certain indicators are suitable for integral assessment, and, therefore, create the basis for improving the methodological approach to assessing the risk of lending to financial institutions.

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Section: Methodsmentioning

confidence: 99%

Retracted: Internal procedures of the risk‐oriented lending process in the bank

Ushanov

2022

Risk Manage Insurance Review

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“…Other research mainly includes investment strategy designation, the role of P2PL in financial market, information asymmetry, interest rate, etc., to name a few [11][12][13][14][15].…”

Section: Literature Reviewmentioning

confidence: 99%

Accessing Information Asymmetry in Peer-to-Peer Lending by Default Prediction from Investors’ Perspective

Wei

Liu

2020

Symmetry

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Recent a few years have witnessed the rapid expansion of the peer-to-peer lending marketplace. As a new field of investment and a novel channel of financing, it has drawn extensive attention throughout the world. Many investors have shown great enthusiasm for this field. However, investors are at the disadvantage of information asymmetry, which is a key issue in this marketplace that is unavoidable and can lead to moral hazard or adverse selection. In this paper, we propose an L 1 / 2 -regularized weighted logistic regression model for default prediction of peer-to-peer lending loans from investors’ perspective, which can reduce the impact of information asymmetry in the process of loan decision. Rather than solely focus on the accuracy of the prediction, we take into consideration the different risk preferences of different investors. We try to find a trade-off between the risk of losing principal and that of losing potential investment opportunities on the basis of investors’ risk preferences. Meanwhile, due to the nature of peer-to-peer lending loans, we add an L 1 / 2 -regularization term to reduce the chance of overfitting. Xu’s algorithm for L 1 / 2 -regularization problems is applied to solve our model. We perform training, in-sample test, and out-of-sample test with data from LendingClub. Numerical experiments demonstrate that regularization could enhance out-of-sample the area under the Precision–Recall curve (AUPRC). By applying the proposed model, the risk-averse investors could apply a higher penalty factor to lower the risk of losing principal at the cost of the loss of some potential investment opportunities according to their own risk preferences. This model can help investors reduce the impact of information asymmetry to a great extent.

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“…This research paper aims to examine the impact of tax implementation on the number of P2P loan recipients and the amount of P2P loan disbursement in fintech industry of Indonesia. Peer-to-peer lending, commonly known as P2P, is the process of borrowing and lending money by borrowers and lenders through online platforms without needing an official, traditional financial institution as an intermediary (Wei et al, 2018). In other words, P2P lending involves people with extra money (lenders), people who need money (borrowers), and a platform (online channel).…”

Section: Introductionmentioning

confidence: 99%

Impact of Tax Implementation on Fintech P2P Loans in Indonesia

Stanley

2023

ecb

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This research paper aims to examine the impact of tax implementation on the number of P2P loan recipients and the amount of P2P loan disbursement in fintech industry of Indonesia. This empirical study is based on the number of accounts and loan data acquired from one of Indonesia’s government agencies – the Financial Services Authority (OJK) from February 2022 to April 2022 and June 2022 to August 2022 that are analyzed using various tests such as Skewness-Kurtosis Test, the variance ratio test, and t-test. The results show that tax implementation does not significantly affect the number of P2P loan recipients and the amount of P2P loan disbursement. This is because fintech offers greater benefits than traditional banks, such as convenience, time-saving, more access to financial products, and better security measures. The limited amount of available data and the number of provinces available become the limitation of the study to obtain optimum results.

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Peer-To-Peer Lending: Classification in the Loan Application Process

Cited by 7 publications

References 33 publications

Retracted: Internal procedures of the risk‐oriented lending process in the bank

Retracted: Internal procedures of the risk‐oriented lending process in the bank

Accessing Information Asymmetry in Peer-to-Peer Lending by Default Prediction from Investors’ Perspective

Impact of Tax Implementation on Fintech P2P Loans in Indonesia

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