P2P Network Lending, Loss Given Default and Credit Risks

Zhou, Guangyou; Zhang, Yijia; Luo, Shusheng

doi:10.3390/su10041010

Cited by 23 publications

(15 citation statements)

References 14 publications

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“…However, a small number of studies used real-life credit scoring dataset, but these datasets are not available for retrieving and analyzing [24][25][26][27]. For example, in accordance to bank managers' expert opinions in Taiwan, Chen [24] discussed the evaluation and selection factors for client credit granting quality and adopts Decision-Making Trial and Evaluation Laboratory to compare and analyze the similarities and the differences in a bank's evaluation for client traits, abilities, financial resources, collaterals, and other dimensions (criteria).…”

Section: Introductionmentioning

confidence: 99%

An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments

et al. 2019

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Machine learning and artificial intelligence have achieved a human-level performance in many application domains, including image classification, speech recognition and machine translation. However, in the financial domain expert-based credit risk models have still been dominating. Establishing meaningful benchmark and comparisons on machine-learning approaches and human expert-based models is a prerequisite in further introducing novel methods. Therefore, our main goal in this study is to establish a new benchmark using real consumer data and to provide machine-learning approaches that can serve as a baseline on this benchmark. We performed an extensive comparison between the machine-learning approaches and a human expert-based model—FICO credit scoring system—by using a Survey of Consumer Finances (SCF) data. As the SCF data is non-synthetic and consists of a large number of real variables, we applied two variable-selection methods: the first method used hypothesis tests, correlation and random forest-based feature importance measures and the second method was only a random forest-based new approach (NAP), to select the best representative features for effective modelling and to compare them. We then built regression models based on various machine-learning algorithms ranging from logistic regression and support vector machines to an ensemble of gradient boosted trees and deep neural networks. Our results demonstrated that if lending institutions in the 2001s had used their own credit scoring model constructed by machine-learning methods explored in this study, their expected credit losses would have been lower, and they would be more sustainable. In addition, the deep neural networks and XGBoost algorithms trained on the subset selected by NAP achieve the highest area under the curve (AUC) and accuracy, respectively.

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Section: Introductionmentioning

confidence: 99%

An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments

et al. 2019

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“…The problem of choosing factors to produce a reliable credit score is a subject of many studies [8], [9]. Various factors are used to assess the creditworthiness and probability of default of the loan obligations, such as gender, age, marital status, education, employment length, experience, income [10] income, interest rate, purpose of the loan [11] indebtedness, term of the loan [12], total assets of the borrower [13] customer behavior before and after approval of the loan [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing Performance of Machine Learning Algorithms for Default Risk Prediction in Peer to Peer Lending

Aleksandrova¹

2021

TEM Journal

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The purpose of this research is to evaluate several popular machine learning algorithms for credit scoring for peer to peer lending. The dataset to fit the models is extracted from the official site of Lending Club. Several models have been implemented, including single classifiers (logistic regression, decision tree, multilayer perceptron), homogeneous ensembles (XGBoost, GBM, Random Forest) and heterogeneous ensemble classifiers like Stacked Ensembles. Results show that ensemble classifiers outperform single ones with Stacked Ensemble and XGBoost being the leaders.

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“…Recently, studies have sought to evaluate the LGD in the P2P setting. For instance, Zhou et al ( 2018 ) present the first model of LGD, using data from LendingClub, and describe the probability density function of LGD as a unimodal distribution with the high value peaking in the unsecured bond market. They also find negative relationships between credit grade, debt-to-income ratio, and LGD, and that borrowers’ total assets do not have a significant impact.…”

Section: Related Literaturementioning

confidence: 99%

“…Also, this study is linked to works by Oleksandr and Xu ( 2018 ) based on loan verification and Pursainen ( 2020 ) that show that the LendingClub platform does not adjust the pricing on loans for misreporting borrowers. Finally, this paper contributes significantly to the growing literature on the estimation of the loss given default (LGD) and RR in the unsecured market (Calabrese 2014 ; Gourieroux and Lu 2019 ; Ye and Bellotti 2019 ; Siao et al 2016 ; Zhou et al 2018 ), advising lenders to focus on additional credit risk measures to accurately assess borrower creditworthy. In marketplace lending, the information asymmetries between borrowers and lenders lead to higher default rates and large LGD.…”

Section: Introductionmentioning

confidence: 99%

Fintech platforms: Lax or careful borrowers’ screening?

Gallo

2021

Financ Innov

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Can peer-to-peer lending platforms mitigate fraudulent behaviors? Or have lending players been acting similar to free-riders? This paper constructs a new proxy to investigate lending platform misconduct and compares the FICO score and the LendingClub credit grade. To examine whether the lack of verification by the Fintech platform affects lenders’ collection performance, I explore the recovery rate (RR) of non-performing loans through a mixed-continuous model. The regression results show that the degree of prudence taken by the lending platform in the pre-screening activity negatively affects the detection of some misreporting borrowers. I also find that the Fintech platform’s missing verification information (e.g., annual income and employment length) affects the RR of non-performing loans, thereby hampering lenders’ collection performance.

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P2P Network Lending, Loss Given Default and Credit Risks

Cited by 23 publications

References 14 publications

An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments

An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments

Comparing Performance of Machine Learning Algorithms for Default Risk Prediction in Peer to Peer Lending

Fintech platforms: Lax or careful borrowers’ screening?

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