Credit scoring and rejected instances reassigning through evolutionary computation techniques

Mu-Chen Chen*, Shih Hsien Huang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

150 Scopus citations


The credit industry is concerned with many problems of interest to the computation community. This study presents a work involving two interesting credit analysis problems and resolves them by applying two techniques, neural networks (NNs) and genetic algorithms (GAs), within the field of evolutionary computation. The first problem is constructing NN-based credit scoring model, which classifies applicants as accepted (good) or rejected (bad) credits. The second one is better understanding the rejected credits, and trying to reassign them to the preferable accepted class by using the GA-based inverse classification technique. Each of these problems influences on the decisions relating to the credit admission evaluation, which significantly affects risk and profitability of creditors. From the computational results, NNs have emerged as a computational tool that is well-matched to the problem of credit classification. Using the GA-based inverse classification, creditors can suggest the conditional acceptance, and further explain the conditions to rejected applicants. In addition, applicants can evaluate the option of minimum modifications to their attributes.

Original languageEnglish
Pages (from-to)433-441
Number of pages9
JournalExpert Systems with Applications
Issue number4
StatePublished - 1 May 2003


  • Classification
  • Credit scoring
  • Genetic algorithms
  • Inverse classification
  • Neural networks


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