Background: Credit scoring is a statistical tool allowing banks to distinguish between good and bad clients. However, literature in the world of credit scoring is limited. In this article parametric and non-parametric statistical techniques that are used in credit scoring are reviewed. Aim: To build an optimal credit scoring matrix model to predict which clients will go bad in the future. This article also illustrates the use of the credit scoring matrix model to determine an appropriate cut-off score on a more granular level. Setting: Data used in this article are based on a bank in South Africa and are Retail Banking specific. Methods: The methods used in this article were regression, statistical analysis, matrix and comparative study. Results: The matrix provides uplift in the Gini-coefficient when compared to a one-dimensional model and provides greater granularity when setting the appropriate cut-off. Conclusion: The article provides steps to construct a credit scoring matrix model to optimise separation between good and bad clients. An added contribution of the article is the manner in which the credit scoring matrix model provides a greater granularity option for establishing the cut-off score for accepting clients, more appropriately than a one-dimensional scorecard.

中文翻译：

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优化的信用计分卡，可提高截止分数的确定

背景：信用评分是一种统计工具，可让银行区分好客户和坏客户。但是，信用评分领域的文献有限。在本文中，对信用评分中使用的参数和非参数统计技术进行了回顾。目的：建立一个最佳的信用评分矩阵模型，以预测未来哪些客户会破产。本文还说明了信用评分矩阵模型在更细粒度的层次上确定适当的截止分数的用法。设置：本文中使用的数据基于南非的一家银行，并且特定于零售银行业务。方法：本文使用的方法是回归，统计分析，矩阵和比较研究。结果：与一维模型相比，该矩阵可提高基尼系数，在设置适当的截止值时可提供更大的粒度。结论：本文提供了构建信用评分矩阵模型的步骤，以优化好客户与坏客户之间的分离。文章的另一贡献是，与一维记分卡相比，信用评分矩阵模型提供了更大的粒度选项来建立接受客户的截止分数，这种方式更为合适。