Details

Autor(en) / Beteiligte

• Jiang, Cuiqing
• Lu, Wang
• Wang, Zhao
• Ding, Yong

Titel

Benchmarking state-of-the-art imbalanced data learning approaches for credit scoring

Ist Teil von

• Expert systems with applications, 2023-03, Vol.213, p.118878, Article 118878

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals

Beschreibungen/Notizen

• •Proposal of a new taxonomy for imbalanced data learning approaches (IDLAs).•Large-scale benchmark of 28 IDLAs across three real-world credit scoring datasets.•Assessment of the performance of generative adversarial nets in credit scoring.•Analysis of the internal cause of class imbalance problem in credit scoring.•Recommendation for the selection strategy of IDLAs in credit scoring. The goal of credit scoring is to identify abnormalities, aiding decision making and maintaining the order of financial transactions. Due to the small number of default records, one inevitably faces a class imbalance problem when handling financial data. The class imbalance problem has received a lot of attention because of the economic loss that can occur when one fails to accurately identify default samples. To solve this problem, there are various classic and mature approaches to learning imbalanced data, including resampling approaches, cost-sensitive strategies, and so on. Especially in recent years, generative adversarial networks (GANs) have attracted the attention of researchers to explore these networks’ effects as imbalanced data learning tools. However, no attention has been paid to the systematic scoring and comparison of these traditional and state-of-the-art imbalanced data learning approaches in relation to credit scoring. Therefore, choosing several related datasets, we compare the performance of the traditional approaches and GANs in solving the class imbalance problem of credit scoring; at the same time, with the help of benchmark analysis, we provide some suggestions for relevant research.