Sample selection algorithms for credit risk modelling through data mining techniques. (20th February 2019)

• Record Type: Journal Article
• Authors: Protopapadakis, Eftychios; Niklis, Dimitrios; Doumpos, Michalis; Doulamis, Anastasios; Zopounidis, Constantin
• Is Part Of: International journal of data mining, modelling and management. Volume 11:Number 2(2019)
• Page Start: 103
• Page End: 128
• Publication Date: 2019-02-20
• Subjects: credit risk modelling -- data mining -- sampling -- classification; Data mining -- Periodicals; Information science -- Periodicals; Databases -- Periodicals; 005.7
• Languages: English
• ISSNs: 1759-1163

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.