Determinants of Borrowers’ Default in P2P Lending under Consideration of the Loan Risk Class

Polena, Michal; Regner, Tobias

doi:10.3390/g9040082

Cited by 35 publications

(26 citation statements)

References 13 publications

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“…Regulation of loan-based crowd funding could connecting investors crowd funding with entrepreneurs through transparency and transactions are fast and almost hassle-free in documentation [18]. Loan-based crowd funding is a digital service in the new financial sector, the service provider platform connects borrowers and lenders, so that loanbased crowd funding This is a threat to traditional banks because of the convenience and advantages it offers [19].…”

Section: Resultsmentioning

confidence: 99%

Financing Analysis of Loan-Based Crowd Funding on Medium Small Micro Enterprises

Dewanti¹,

Septyaningtyas²,

Yuliansyah³

et al. 2021

Proceedings of the 2nd Annual Conference on Social Science and Humanities (ANCOSH 2020)

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This study aims to determine the results of analysis Loan-Based Crowd funding on MSMEs in several countries on helping MSMEs obtain capital loans without a series of difficulties in administration and regulations. So, it can be used as a consideration for the government in making policies that can be applied in Indonesia. In this study, references from journal articles analyze to solve the problem. In this study, using references from journal articles analyzed to solve the problem. This research shows that loan-based crowd funding can be of positive value for business actors with the ease of making loans to maintain and develop the business.

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Section: Resultsmentioning

confidence: 99%

Financing Analysis of Loan-Based Crowd Funding on Medium Small Micro Enterprises

Dewanti¹,

Septyaningtyas²,

Yuliansyah³

et al. 2021

Proceedings of the 2nd Annual Conference on Social Science and Humanities (ANCOSH 2020)

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show abstract

“…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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“…Some researchers selected features using statistical methods (Polena & Regner, ) or information gains (Chen, ) before training machine learning models. The selected features are as follows: loan amount, debt‐to‐income ratio, home ownership, delinquencies, etc.…”

Section: Related Workmentioning

confidence: 99%

Predicting repayment of borrows in peer‐to‐peer social lending with deep dense convolutional network

Kim

Cho

2019

Expert Systems

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In peer‐to‐peer lending, it is important to predict the repayment of the borrower to reduce the lender's financial loss. However, it is difficult to design a powerful feature extractor for predicting the repayment as user and transaction data continue to increase. Convolutional neural networks automatically extract useful features from big data, but they use only high‐level features; hence, it is difficult to capture a variety of representations. In this study, we propose a deep dense convolutional network for repayment prediction in social lending, which maintains the borrower's semantic information and obtains a good representation by automatically extracting important low‐ and high‐level features simultaneously. We predict the repayment of the borrower by learning discriminative features depending on the loan status. Experimental results on the Lending Club dataset show that our model is more effective than other methods. A fivefold cross‐validation is performed to run the experiments.

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Determinants of Borrowers’ Default in P2P Lending under Consideration of the Loan Risk Class

Cited by 35 publications

References 13 publications

Financing Analysis of Loan-Based Crowd Funding on Medium Small Micro Enterprises

Financing Analysis of Loan-Based Crowd Funding on Medium Small Micro Enterprises

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

Predicting repayment of borrows in peer‐to‐peer social lending with deep dense convolutional network

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