Credit scoring model development is very important for the lending decisions of financial institutions. The creditworthiness of borrowers is evaluated by assessing their hard and soft information. However, microfinance borrowers are very sensitive to a local economic downturn and extreme (weather or climate) events. Therefore, this paper is devoted to extending the standard credit scoring models by taking into account the spatial dependence in credit risk. We estimate a credit scoring model with spatial random effects using the distance matrix based on the borrowers’ locations. We find that including the spatial random effects improves the ability to predict defaults and non-defaults of both individual and group loans. Furthermore, we find that several loan characteristics and demographic information are important determinants of individual loan default but not group loans. Our study provides valuable insights for professionals and academics in credit scoring for microfinance and rural finance.

信用评分模型的开发对于金融机构的贷款决策非常重要。通过评估借款人的硬信息和软信息来评估借款人的信誉。然而，小额信贷借款人对当地经济衰退和极端（天气或气候）事件非常敏感。因此，本文致力于通过考虑信用风险的空间依赖性来扩展标准信用评分模型。我们使用基于借款人位置的距离矩阵来估计具有空间随机效应的信用评分模型。我们发现，包括空间随机效应提高了预测个人和团体贷款违约和非违约的能力。此外，我们发现，一些贷款特征和人口统计信息是个人贷款违约的重要决定因素，而不是集体贷款。我们的研究为小额信贷和农村金融信用评分的专业人士和学者提供了宝贵的见解。