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Sample selection algorithms for credit risk modelling through data mining techniques

Author

Listed:
  • Eftychios Protopapadakis
  • Dimitrios Niklis
  • Michalis Doumpos
  • Anastasios Doulamis
  • Constantin Zopounidis

Abstract

Credit risk assessment is a very challenging and important problem in the domain of financial risk management. The development of reliable credit rating/scoring models is of paramount importance in this area. There are different algorithms and approaches for constructing such models to classify credit applicants (firms or individuals) into risk classes. Reliable sample selection is crucial for this task. The aim of this paper is to examine the effectiveness of sample selection schemes in combination with different classifiers for constructing reliable default prediction models. We consider different algorithms to select representative cases and handle class imbalances. Empirical results are reported for a dataset of Greek companies from the commercial sector.

Suggested Citation

  • Eftychios Protopapadakis & Dimitrios Niklis & Michalis Doumpos & Anastasios Doulamis & Constantin Zopounidis, 2019. "Sample selection algorithms for credit risk modelling through data mining techniques," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 11(2), pages 103-128.
    • Handle: RePEc:ids:ijdmmm:v:11:y:2019:i:2:p:103-128
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