Cluster-based dynamic scoring model

Michael K. Lim, So Young Sohn

Research output: Contribution to journalArticlepeer-review


Importance of early prediction of bad creditors has been increasing extensively. In this paper, we propose a behavioral scoring model which dynamically accommodates the changes of borrowers' characteristics after the loans are made. To increase the prediction efficiency, the data set is segmented into several clusters and the observation period is fractionized. The computational results showed that the proposed model can replace the currently used static model to minimize the loss due to bad creditors. The results of this study will help the loan lenders to protect themselves from the potential borrowers with high default risks in a timely manner.

Original languageEnglish (US)
Pages (from-to)427-431
Number of pages5
JournalExpert Systems With Applications
Issue number2
StatePublished - Feb 2007
Externally publishedYes


  • Behavioral scoring
  • Clustering
  • Credit industry
  • Dynamic model
  • Neural networks

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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