Sectorial Analysis Impact on the Development of Credit Scoring Machine Learning Models

被引:0
|
作者
El-Qadi, Ayoub [1 ,2 ]
Trocan, Maria [3 ]
Frossard, Thomas [2 ]
Diaz-Rodriguez, Natalia [4 ,5 ]
机构
[1] Sorbonne Univ, Paris, France
[2] Tinubu Sq, Issy Les Moulineaux, France
[3] Inst Super Elect Paris, Issy Les Moulineaux, France
[4] Univ Granada, Comp Sci & Artificial Intelligence Dept, Andalusian Res Inst Data Sci & Computat Intellige, Granada, Spain
[5] Univ Granada, Res Ctr Informat & Commun Technol, Granada, Spain
关键词
Machine Learning; SHapley Additive eXplanations; Credit Scoring; Small and Medium Enterprises; Economic Sectors; FINANCIAL RATIOS; PREDICTION;
D O I
10.1145/3508397.3564835
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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.
引用
收藏
页码:115 / 122
页数:8
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