The digitalization of credit scoring has become essential for financial institutions and commercial banks, especially in the era of digital transformation. Machine learning techniques are commonly used to evaluate customers' creditworthiness. However, the predicted outcomes of machine learning models can be biased toward protected attributes, such as race or gender. Numerous fairness-aware machine learning models and fairness measures have been proposed. Nevertheless, their performance in the context of credit scoring has not been thoroughly investigated. In this paper, we present a comprehensive experimental study of fairness-aware machine learning in credit scoring. The study explores key aspects of credit scoring, including financial datasets, predictive models, and fairness measures. We also provide a detailed evaluation of fairness-aware predictive models and fairness measures on widely used financial datasets. The experimental results show that fairness-aware models achieve a better balance between predictive accuracy and fairness compared to traditional classification models.

翻译：信用评分的数字化对金融机构和商业银行已变得至关重要，尤其是在数字化转型时代。机器学习技术通常用于评估客户的信用状况。然而，机器学习模型的预测结果可能对受保护属性（如种族或性别）产生偏见。目前已提出了许多公平性感知机器学习模型和公平性度量方法。尽管如此，它们在信用评分背景下的性能尚未得到深入研究。本文对信用评分中的公平性感知机器学习进行了全面的实验研究。该研究探讨了信用评分的关键方面，包括金融数据集、预测模型和公平性度量。我们还对广泛使用的金融数据集上的公平性感知预测模型和公平性度量进行了详细评估。实验结果表明，与传统分类模型相比，公平性感知模型在预测准确性和公平性之间实现了更好的平衡。