In practice, as a well-known statistical method, the logistic regression model is used to evaluate the credit-worthiness of borrowers due to its simplicity and transparency in predictions. However, in literature, sophisticated machine learning models can be found that can replace the logistic regression model. Despite the advances and applications of machine learning models in credit scoring, there are still two major issues: the incapability of some of the machine learning models to explain predictions; and the issue of imbalanced datasets. As such, there is a need for a thorough survey of recent literature in credit scoring. This article employs a systematic literature survey approach to systematically review statistical and machine learning models in credit scoring, to identify limitations in literature, to propose a guiding machine learning framework, and to point to emerging directions. This literature survey is based on 74 primary studies, such as journal and conference articles, that were published between 2010 and 2018. According to the meta-analysis of this literature survey, we found that in general, an ensemble of classifiers performs better than single classifiers. Although deep learning models have not been applied extensively in credit scoring literature, they show promising results.

实际上，作为一种众所周知的统计方法，逻辑回归模型由于其预测的简单性和透明性而用于评估借款人的信用度。但是，在文献中，可以找到可以代替逻辑回归模型的复杂的机器学习模型。尽管机器学习模型在信用评分方面取得了进步和应用，但仍然存在两个主要问题：某些机器学习模型无法解释预测；以及数据集不平衡的问题。因此，需要对信用评分中的最新文献进行全面的调查。本文采用系统的文献调查方法来系统地审查信用评分中的统计模型和机器学习模型，以确定文献中的局限性，提出指导性机器学习框架，并指出新的方向。这项文献调查基于2010年至2018年之间发表的74项主要研究（例如期刊和会议文章）。根据该文献调查的荟萃分析，我们发现，总体而言，分类器的效果要优于单个分类器。分类器。尽管深度学习模型尚未在信用评分文献中广泛应用，但它们显示出令人鼓舞的结果。