行业分析对信用评分机器学习模型发展的影响

Ayoub El Qadi, M. Trocan, Thomas Frossard, Natalia Díaz Rodríguez
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引用次数: 1

摘要

中小企业在全球经济增长中发挥着至关重要的作用。这些公司获得信贷的机会使它们能够为其活动的发展提供资金。人工智能已经成为一种潜在的工具,可以帮助金融和保险机构评估中小企业,从而加速它们的活动。在确定违约风险时,企业所处的经济领域是一个重要因素。另一方面,要在高度监管的行业中引入人工智能,主要参与者需要了解模型的行为。在本文中,我们重点发展了基于人工智能的不同经济部门模型,并使用SHapley加性解释分析了模型的行为。我们比较了不同经济部门模型与全球模型的表现和解释。我们的研究表明,当创建不同的行业模型时,在性能方面有轻微的改善。每个模型的解释之间的比较揭示了在最相关的特征方面的某些分歧。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sectorial Analysis Impact on the Development of Credit Scoring Machine Learning Models
Small and Medium-sized Enterprises play an essential role in the growth of the global economy. The access to credit for these companies allows them to fund the development of their activities. Artificial Intelligence has emerged as a potential tool to help financial and insurance institutions to assess Small and Medium-sized companies and thus, accelerate their activities. The economic sector in which companies operate is an essential factor when it comes to determining the risk of default. On the other hand, to introduce Artificial Intelligence in a highly regulated industry, the principal actors need to understand the behavior of the models. In this paper, we focus on the development of Artificial Intelligence-based models for different economic sectors Furthermore, we analyze the model behavior using SHapley Additive exPlanations. We compare both the performance and the explanations of the different economic sector models with the global model. Our study shows that there is a slight improvement in terms of performance when creating the different sectorial models. The comparison between the explanations for each model reveals certain disagreements in terms of the most relevant features.
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