Department of Information and Industrial Engineering, Yonsei University, Seoul, Republic of Korea.
PLoS One. 2021 Dec 31;16(12):e0261737. doi: 10.1371/journal.pone.0261737. eCollection 2021.
Potential relationship among loan applicants can provide valuable information for evaluating default risk. However, most of the existing credit scoring models either ignore this relationship or consider a simple connection information. This study assesses the applicants' relation in terms of their distance estimated based on their characteristics. This information is then utilized in a proposed spatial probit model to reflect the different degree of borrowers' relation on the default prediction of loan applicant. We apply this method to peer-to-peer Lending Club Loan data. Empirical results show that the consideration of information on the spatial autocorrelation among loan applicants can provide high predictive power for defaults.
潜在的借款人之间的关系可以为评估违约风险提供有价值的信息。然而，现有的大多数信用评分模型要么忽略这种关系，要么考虑简单的连接信息。本研究根据借款人的特征，评估他们之间的距离，以此来评估借款人之间的关系。然后，将这些信息应用于所提出的空间概率模型中，以反映借款人之间的不同关系程度对贷款申请人违约预测的影响。我们将这种方法应用于 P2P 借贷俱乐部的贷款数据。实证结果表明，考虑借款人之间的空间自相关信息可以为违约预测提供较高的准确率。
