Building credit scoring models using genetic programming
Expert Systems with ApplicationsPublished 25 January 2005
Choon Nam Ong, Jih‐Jeng Huang, Gwo‐Hshiung Tzeng
Citations357
SJR quartileQ1
SJR score1.85
SNIP2.55
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TL;DR
In this paper, genetic programming (GP) is used to build credit scoring models and it is concluded that GP can provide better performance than other models.
Abstract
Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed to significantly improving the accuracy of the credit scoring mode. In this paper, genetic programming (GP) is used to build credit scoring models. Two numerical examples will be employed here to compare the error rate to other credit scoring models including the ANN, decision trees, rough sets, and logistic regression. On the basis of the results, we can conclude that GP can provide better performance than other models.
Keywords
Computer ScienceEconomics, Econometrics and FinanceBusiness, Management and Accounting
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