An intelligent decision support system for assessing the default risk in small and medium-sized enterprises

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URI: https://hdl.handle.net/11556/2572
ISSN: 0302-9743
ISBN: 9783319590592
DOI: 10.1007/978-3-319-59060-8_48
Publication date
2017
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Springer Verlag
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Abstract
In the last years, default prediction systems have become an important tool for a wide variety of financial institutions, such as banking systems or credit business, for which being able of detecting credit and default risks, translates to a better financial status. Nevertheless, small and medium-sized enterprises did not focus its attention on customer default prediction but in maximizing the sales rate. Consequently, many companies could not cope with the customers’ debt and ended up closing the business. In order to overcome this issue, this paper presents a novel decision support system for default prediction specially tailored for small and medium-sized enterprises that retrieves the information related to the customers in an Enterprise Resource Planning (ERP) system and obtain the default risk probability of a new order or client. The resulting approach has been tested in a Graphic Arts printing company of The Basque Country allowing taking prioritized and preventive actions with regard to the default risk probability and the customer’s characteristics. Simulation results verify that the proposed scheme achieves a better performance than a naïve Random Forest (RF) classification technique in real scenarios with unbalanced datasets.
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Publisher Copyright: © Springer International Publishing AG 2017.
Keywords
Classification , Clustering , Default prediction , Theoretical Computer Science , General Computer Science , SDG 9 - Industry, Innovation, and Infrastructure
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Manjarres , D , Landa-Torres , I & Andonegui , I 2017 , An intelligent decision support system for assessing the default risk in small and medium-sized enterprises . in J M Zurada , L A Zadeh , R Tadeusiewicz , L Rutkowski , M Korytkowski & R Scherer (eds) , Artificial Intelligence and Soft Computing - 16th International Conference, ICAISC 2017, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 10246 LNAI , Springer Verlag , pp. 533-542 , 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017 , Zakopane , Poland , 11/06/17 . https://doi.org/10.1007/978-3-319-59060-8_48
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