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Loan default prediction of Chinese P2P market: a machine learning methodology. [PDF]
Sci Rep, 2021 Repayment failures of borrowers have greatly affected the sustainable development of the peer-to-peer (P2P) lending industry. The latest literature reveals that existing risk evaluation systems may ignore important signals and risk factors affecting P2P ...
Xu J, Lu Z, Xie Y.
europepmc +2 more sources
COVID-19 pandemic risk and probability of loan default: evidence from marketplace lending market. [PDF]
Financ Innov, 2021 As the COVID-19 pandemic adversely affects the financial markets, a better understanding of the lending dynamics of a successful marketplace is necessary under the conditions of financial distress.
Nigmonov A, Shams S.
europepmc +2 more sources
Online Loan Default Prediction Model Based on Deep Learning Neural Network.
Comput Intell Neurosci, 2022 With the rapid development of Internet loans and the demand for Internet loans, Internet-based loan default prediction is particularly important. P2P online lending is based on Internet technology.
Li B.
europepmc +2 more sources
Multi-view GCN for Loan Default Risk Prediction
Neural Computing and Applications, 2023 Abstract Loan default risk prediction is a major application of machine learning for financial institutions to evaluate the client's default probability. Existing deep learning models rarely consider the connection among application records for loan default detection.
Zihao Li 0005 +4 more
openaire +2 more sources
DETERMINANTS OF AGRICULTURE-RELATED LOAN DEFAULT: EVIDENCE FROM CHINA
Buletin Ekonomi Moneter dan Perbankan, 2020 This paper investigates agriculture-related loan default in 2002–2009 through a large data set from a leading Chinese state-owned bank. Using logit regression, we find the default rate on agriculture-related loans is significantly higher than that on non–
Zhichao Yin, Lei Meng, Yezhou Sha
doaj +2 more sources
When Words Sweat: Identifying Signals for Loan Default in the Text of Loan Applications
Journal of Marketing Research, 2019 The authors present empirical evidence that borrowers, consciously or not, leave traces of their intentions, circumstances, and personality traits in the text they write when applying for a loan. This textual information has a substantial and significant
Oded Netzer, Michal Herzenstein
exaly +2 more sources
A study on predicting loan default based on the random forest algorithm
Procedia Computer Science, 2019 Recently, with the advance of electronic commerce and big data technology, P2P online lending platforms have brought opportunities to businessmen, but at the same time, they are also faced with the risk of user loan default, which is related to the ...
Cai Ying, Daji Ergu
exaly +2 more sources
One-factor model for default rates by loan type [PDF]
Bankarstvo, 2021 This paper investigates the link between default rates by loan types and the systemic credit risk component. This link is described by a linear model that combines systemic and idiosyncratic contributions.
Božović Miloš
doaj +1 more source
Credit Scoring and Loan Default [PDF]
International Review of Finance, 2011 AbstractA metric of credit score performance is developed to study the usage and performance of credit scoring in the loan origination process. We examine the performance of originationFICOscores as measures ofex anteborrower creditworthiness using loan‐level data onex postperformance of subprime mortgages.
Sengupta, Rajdeep, Bhardwaj, Geetesh
openaire +4 more sources
Loan Default Prediction Based on Convolutional Neural Network and LightGBM
International Journal of Data Warehousing and Mining, 2023 With the change of people's consumption mode, credit consumption has gradually become a new consumption trend. Frequent loan defaults give default prediction more and more attention.
Q. Zhu +4 more
semanticscholar +1 more source