A combined entropy-based approach for a proactive credit scoring. (January 2020)

• Record Type: Journal Article
• Authors: Carta, Salvatore; Ferreira, Anselmo; Reforgiato Recupero, Diego; Saia, Marco; Saia, Roberto
• Is Part Of: Engineering applications of artificial intelligence. Volume 87(2020)
• Publication Date: 2020-01
• Subjects: FinTech -- Trust management -- Business intelligence -- Credit scoring -- Data mining -- Entropy; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.103292
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Lenders, such as credit card companies and banks, use credit scores to evaluate the potential risk posed by lending money to consumers and, therefore, mitigating losses due to bad debt. Within the financial technology domain, an ideal approach should be able to operate proactively, without the need of knowing the behavior of non-reliable users. Actually, this does not happen because the most used techniques need to train their models with both reliable and non-reliable data in order to classify new samples. Such a scenario might be affected by the cold-start problem in datasets, where there is a scarcity or total absence of non-reliable examples, which is further worsened by the potential unbalanced distribution of the data that reduces the classification performances. In this paper, we overcome the aforementioned issues by proposing a proactive approach, composed of a combined entropy-based method that is trained considering only reliable cases and the sample under investigation. Experiments done in different real-world datasets show competitive performances with several state-of-art approaches that use the entire dataset of reliable and unreliable cases.