Application of Instance-Based Entropy Fuzzy Support Vector Machine in Peer-To-Peer Lending Investment Decision

Cho, Poongjin; Chang, Woojin; Song, Jae Wook

doi:10.1109/access.2019.2896474

Cited by 35 publications

(20 citation statements)

References 56 publications

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“…This can be done, in the simplest case, by relying on some available characterizing variables of a borrower. One of these variables, and the most informative when assessing the credibility of a loan, is the credit rating (Cho et al, 2019). For example, in the Lending Club platform, there are seven grades according to the loan's credit risk.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Data‐driven optimization of peer‐to‐peer lending portfolios based on the expected value framework

Byanjankar

Mezei

Heikkilä

2021

Intell Sys Acc Fin Mgmt

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In recent years, peer‐to‐peer (P2P) lending has been gaining popularity amongst borrowers and individual investors. This can mainly be attributed to the easy and quick access to loans and the higher possible returns. However, the risk involved in these investments is considerable, and for most investors, being nonprofessionals, this increases the complexity and the importance of investment decisions. In this study, we focus on generating optimal investment decisions to lenders for selecting loans. We treat the loan selection process in P2P lending as a portfolio optimization problem, with the aim being to select a set of loans that provide a required return while minimizing risk. In the process, we use internal rate of return as the measure of return. As the starting point of the model, we use machine‐learning algorithms to predict the default probabilities and calculate expected values for the loans based on historical data. Afterwards, we calculate the distance between loans using (i) default probabilities and, as a novel step, (ii) expected value. In the calculations, we utilize kernel functions to obtain similarity weights of loans as the input of the optimization models. Two optimization models are tested and compared on data from the popular P2P platform Lending Club. The results show that using the expected‐value framework yields higher return.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Data‐driven optimization of peer‐to‐peer lending portfolios based on the expected value framework

Byanjankar

Mezei

Heikkilä

2021

Intell Sys Acc Fin Mgmt

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“…While interpretive components naturally appear in classical econometric models based on regression, in machine learning proposals, if they are considered, it is only through feature importance. See, for example, the works by Serrano-Cinca and Gutierrez-Nieto [2], Xia et al [20], Ye et al [41], Bastani et al [42] and Cho et al [43].…”

Section: Machine Learning and Credit Risk Modeling In P2p Lendingmentioning

confidence: 99%

Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending

Garzón

Arroyo

Caparrini³

et al. 2020

IEEE Access

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Peer-to-peer (P2P) lending demands effective and explainable credit risk models. Typical machine learning algorithms offer high prediction performance, but most of them lack explanatory power. However, this deficiency can be solved with the help of the explainability tools proposed in the last few years, such as the SHAP values. In this work, we assess the well-known logistic regression model and several machine learning algorithms for granting scoring in P2P lending. The comparison reveals that the machine learning alternative is superior in terms of not only classification performance but also explainability. More precisely, the SHAP values reveal that machine learning algorithms can reflect dispersion, nonlinearity and structural breaks in the relationships between each feature and the target variable. Our results demonstrate that is possible to have machine learning credit scoring models be both accurate and transparent. Such models provide the trust that the industry, regulators and end-users demand in P2P lending and may lead to a wider adoption of machine learning in this and other risk assessment applications where explainability is required.

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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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Application of Instance-Based Entropy Fuzzy Support Vector Machine in Peer-To-Peer Lending Investment Decision

Cited by 35 publications

References 56 publications

Data‐driven optimization of peer‐to‐peer lending portfolios based on the expected value framework

Data‐driven optimization of peer‐to‐peer lending portfolios based on the expected value framework

Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending

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

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