An improved SMO algorithm for financial credit risk assessment : Evidence from China's banking

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

5 Scopus Citations
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Original languageEnglish
Pages (from-to)314-325
Journal / PublicationNeurocomputing
Early online date8 Jul 2017
Publication statusPublished - 10 Jan 2018


With rapid development of financial services and products, credit risk assessment has recently gained considerable attention in the field of financial risk management. In this paper, an improved credit risk assessment approach is presented. Based on the credit data from China Banking Regulatory Commission (CBRC), a multi-dimensional and multi-level credit risk indicator system is constructed. In particular, we present an improved sequential minimal optimization (SMO) learning algorithm, named four-variable SMO (FV-SMO), for credit risk classification model. At each iteration, it jointly selects four variables into the working set and an theorem is proposed to guarantee the analytical solution of sub-problem. The assessment is made on China credit dataset and two benchmark credit datasets from UCI database and CD-ROM database. Experimental results demonstrate FV-SMO is competitive in saving the computational cost and outperforms other five state-of-the-art classification methods in credit risk assessment accuracy.

Research Area(s)

  • Credit risk assessment, Four-variable working set, Sequential minimal optimization (SMO), SVM