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Default avoidance on credit card portfolios using accounting, demographical and exploratory factors: decision making based on machine learning (ML) techniques

  • Nikolaos SariannidisEmail author
  • Stelios Papadakis
  • Alexandros Garefalakis
  • Christos Lemonakis
  • Tsioptsia Kyriaki-Argyro
S.I.: BALCOR-2017
  • 10 Downloads

Abstract

Effective and thorough credit-risk management is a key factor for lending institutions, as significant financial losses can arise from the borrowers’ default. Consequently, machine learning methods can measure and analyze credit risk objectively when at the same time they face increasingly attention. This study analyzes default payment data from a credit cards’ portfolio containing some 30,000 clients from Taiwan with twenty-three attributes and with no missing information. We compare prediction accuracy of seven classification methods used, i.e. KNN, Logistic Regression, Naïve Bayes, Decision Trees, Random Forest, SVC, and Linear SVC. The results indicate that only few out of most of the typical variables used can adequately analyze default characteristics in terms of lending decisions. The results provide effective feedback to credit evaluators, lending institutions and business analysts for in-depth analysis. Also, they mention to the importance of the precautionary borrowing techniques to be used to better understand credit-card borrowers’ behavior, along with specific accounting, historical and demographical characteristics.

Keywords

Debt Credit card portfolios Machine learning (ML) methods Explanatory factors Accounting data Demographic data Credit history data 

Notes

Acknowledgements

The current publication is based on the following dataset: Lichman (Lichman 2013). We would also like to thank the Laboratory of Artificial Intelligence Systems and Computer Architectures of the Technological Educational Institute of Crete for providing the computer power to complete extensive experimental results for the needs of this work

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Nikolaos Sariannidis
    • 1
    Email author
  • Stelios Papadakis
    • 2
  • Alexandros Garefalakis
    • 2
  • Christos Lemonakis
    • 2
  • Tsioptsia Kyriaki-Argyro
    • 3
  1. 1.Department of Finance and AccountingWestern Macedonia University οf Applied SciencesKozaniGreece
  2. 2.Department of Business AdministrationTechnological Educational Institute of CreteHeraklionGreece
  3. 3.Department of Accounting and FinanceWestern Macedonia University οf Applied SciencesKozaniGreece

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