Title: Sample selection algorithms for credit risk modelling through data mining techniques

Authors: Eftychios Protopapadakis; Dimitrios Niklis; Michalis Doumpos; Anastasios Doulamis; Constantin Zopounidis

Addresses: School of Rural and Surveying Engineering, National Technical University of Athens, 9 Iroon Polytechneiou Str., 157 80, Zographos, Greece ' Department of Accounting and Finance, Western Macedonia University of Applied Sciences, Koila Kozani, 50100, Greece ' Financial Engineering Laboratory, School of Production Engineering and Management, Technical University of Crete, University Campus, 73100, Chania, Greece ' School of Rural and Surveying Engineering, National Technical University of Athens, 9 Iroon Polytechneiou Str., 157 80, Zographos, Greece ' Financial Engineering Laboratory, School of Production Engineering and Management, Technical University of Crete, University Campus, 73100, Chania, Greece; Audencia Business School, 8 route de la Joneliere, B.P. 31222, 44312 Nantes, Cedex 3, France

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.

Keywords: credit risk modelling; data mining; sampling; classification.

DOI: 10.1504/IJDMMM.2019.098969

International Journal of Data Mining, Modelling and Management, 2019 Vol.11 No.2, pp.103 - 128

Received: 07 Feb 2018
Accepted: 10 Jul 2018

Published online: 20 Feb 2019 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article