Credit scoring by fuzzy support vector machines with a novel membership function

Artikel

Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied in the fields of statistics and machine learning. In this paper a novel fuzzy support vector machine (SVM) credit scoring model is proposed for credit risk analysis, in which fuzzy membership is adopted to indicate different contribution of each input point to the learning of SVM classification hyperplane. Considering the methodological consistency, support vector data description (SVDD) is introduced to construct the fuzzy membership function and to reduce the effect of outliers and noises. The SVDD-based fuzzy SVM model is tested against the traditional fuzzy SVM on two real-world datasets and the research results confirm the effectiveness of the presented method.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 9 ; Year: 2016 ; Issue: 4 ; Pages: 1-10 ; Basel: MDPI

Classification and Topics

• Classification: Management
• Subject: fuzzy support vector machine ; support vector data description ; credit scoring

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Shi, Jian ; Xu, Benlian
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2016

Further information

• DOI: doi:10.3390/jrfm9040013
• Handle: http://hdl.handle.net/10419/178580
• Last update: 10.03.2025, 11:44 AM CET