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S. T. Luo, B. W. Cheng and C. H. Hsieh, “Prediction Model Building with Clustering-Launched Classification And Support Vector Machines in Credit Scoring,” Expert Systems with Application, Vol. 36, No. 4, 2009, pp. 7562-7566.

has been cited by the following article:

• TITLE: An Overview of Personal Credit Scoring: Techniques and Future Work

  AUTHORS: Xiao-Lin Li, Yu Zhong

  KEYWORDS: Credit Scoring; Ensemble Learning; Dynamic Behavioral Scoring; Type I and Type II Error

  JOURNAL NAME: International Journal of Intelligence Science, Vol.2 No.4A, November 1, 2012

  ABSTRACT: Personal credit scoring is the application of financial risk forecasting. It becomes an even important task as financial institutions have been experiencing serious competition and challenges. In this paper, the techniques used for credit scoring are summarized and classified and the new method—ensemble learning model is introduced. This article also discusses some problems in current study. It points out that changing the focus from static credit scoring to dynamic behavioral scoring and maximizing revenue by decreasing the Type I and Type II error are two issues in current study. It also suggested that more complex models cannot always been applied to actual situation. Therefore, how to use the assessment models widely and improve the prediction accuracy is the main task for future research.