An insight into the effects of class imbalance and sampling on classification accuracy in credit risk assessment

Kristina Andrić1, Damir Kalpić2 and Zoran Bohaček3

  1. Privredna Banka Zagreb
    Radnička 50, 10000 Zagreb, Croatia
    kristina.andric@fer.hr
  2. University of Zagreb Faculty of electrical engineering and computing
    Unska 3, 10000 Zagreb, Croatia
    damir.kalpic@fer.hr
  3. Croatian Banking Association
    Nova Ves 17, 10000 Zagreb, Croatia
    zoran.bohacek@alumni.unizg.hr

Abstract

In this paper we investigate the role of sample size and class distribution in credit risk assessments, focusing on real life imbalanced data sets. Choosing the optimal sample is of utmost importance for the quality of predictive models and has become an increasingly important topic with the recent advances in automating lending decision processes and the ever growing richness in data collected by financial institutions. To address the observed research gap, a large-scale experimental evaluation of real-life data sets of different characteristics was performed, using several classification algorithms and performance measures. Results indicate that various factors play a role in determining the optimal class distribution, namely the performance measure, classification algorithm and data set characteristics. The study also provides valuable insight on how to design the training sample to maximize prediction performance and the suitability of using different classification algorithms by assessing their sensitivity to class imbalance and sample size.

Key words

credit risk assessment, imbalanced data sets, class distribution, classification algorithms, sample size, undersampling

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS180110037A

Publication information

Volume 16, Issue 1 (January 2019)
Year of Publication: 2019
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Andrić, K., Kalpić, D., Bohaček, Z.: An insight into the effects of class imbalance and sampling on classification accuracy in credit risk assessment. Computer Science and Information Systems, Vol. 16, No. 1, 155-178. (2019), https://doi.org/10.2298/CSIS180110037A