Estimation procedures of using five alternative machine learning methods for predicting credit card default

Huei Wen Teng*, Michael Lee

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Machine learning has successful applications in credit risk management, portfolio management, automatic trading, and fraud detection, to name a few, in the domain of finance technology. Reformulating and solving these topics adequately and accurately is problem specific and challenging along with the availability of complex and voluminous data. In credit risk management, one major problem is to predict the default of credit card holders using real dataset. We review five machine learning methods: the k-nearest neighbors decision trees, boosting, support vector machine, and neural networks, and apply them to the above problem. In addition, we give explicit Python scripts to conduct analysis using a dataset of 29,999 instances with 23 features collected from a major bank in Taiwan, downloadable in the UC Irvine Machine Learning Repository. We show that the decision tree performs best among others in terms of validation curves.

Original languageEnglish
Article number1950021
JournalReview of Pacific Basin Financial Markets and Policies
Volume22
Issue number3
DOIs
StatePublished - 1 Sep 2019

Keywords

  • Artificial intelligence
  • booting
  • credit card
  • credit risk
  • default
  • delinquency
  • k -nearest neighbors, decision tree
  • machine learning
  • neural network
  • Python
  • supervised learning
  • support vector machine

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