nach oben

Erschienen in:

2022 | OriginalPaper | Buchkapitel

Default Prediction Using Network Based Features

verfasst von : Lorena Poenaru-Olaru, Judith Redi, Arthur Hovanesyan, Huijuan Wang

Erschienen in: Complex Networks & Their Applications X

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Small and medium enterprises (SME) are crucial for economy and have a higher exposure rate to default than large corporates. In this work, we address the problem of predicting the default of an SME. Default prediction models typically only consider the previous financial situation of each analysed company. Thus, they do not take into account the interactions between companies, which could be insightful as SMEs live in a supply chain ecosystem in which they constantly do business with each other. Thereby, we present a novel method to improve traditional default prediction models by incorporating information about the insolvency situation of customers and suppliers of a given SME, using a graph-based representation of SME supply chains. We analyze its performance and illustrate how this proposed solution outperforms the traditional default prediction approaches.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel The COVID-19 Pandemic and Export Disruptions in the United States

Nächstes Kapitel Can You Always Reap What You Sow? Network and Functional Data Analysis of Venture Capital Investments in Health-Tech Companies

Small Business Act for Europe (SBA) Fact Sheet - Netherlands.

Chong, S., et al.: The role of small- and medium-sized enterprises in the Dutch economy: an analysis using an extended supply and use table. J. Econ. Struct. 8, 12 (2019)CrossRef

Asgary, A., Özdemir, A., Özyürek, H.: Small and medium enterprises and global risks: evidence from manufacturing SMEs in Turkey. Int. J. Disaster Risk Sci. 11, 59–73 (2020). https://doi.org/10.1007/s13753-020-00247-0CrossRef

Ha, S., Nam, N., Nhan, N.: A novel credit scoring prediction model based on feature selection approach and parallel random forest. Indian J. Sci. Technol. 9, 05 (2016)

Xu, D., Xuyao, Z., Hu, J., Chen, J.: A novel ensemble credit scoring model based on extreme learning machine and generalized fuzzy soft sets. Math. Probl. Eng. 2020, 1–12 (2020)

Misheva, B.H., Giudici, P., Pediroda, V.: Network-based models to improve credit scoring accuracy. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 623–630 (2018)

Zhan, X.-X., Li, Z., Masuda, N., Holme, P., Wang, H.: Susceptible-infected-spreading-based network embedding in static and temporal networks. EPJ Data Sci. 9, 30 (2020)CrossRef

Yoshiyuki, A.: Bankruptcy propagation on a customer-supplier network: an empirical analysis in Japan (2018)

Altman, E.I., Sabato, G.: Modeling credit risk for SMEs: evidence from the us market (2007)

Rodrigues, F.A.: Network centrality: an introduction. arXiv: Physics and Society, pp. 177–196 (2019)

10.

Wang, H., Hernandez, J.M., Van Mieghem, P.: Betweenness centrality in a weighted network. Phys. Rev. E 77, 046105 (2008)CrossRef

11.

Li, C., Wang, H., Haan, W., Stam, C., Mieghem, V.: The correlation of metrics in complex networks with applications in functional brain networks. J. Stat. Mech: Theor. Exp. 2011, 11 (2011)MathSciNetCrossRefMATH

12.

Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. 31, 833–852 (2019)CrossRef

13.

Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2016, pp. 855–864. ACM Press (2016)

14.

Leevy, J.L., Khoshgoftaar, T.M., Bauder, R.A., Seliya, N.: A survey on addressing high-class imbalance in big data. J. Big Data 5, 1–30 (2018)CrossRef

15.

Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)CrossRefMATH

16.

Brennan, P.J.: A comprehensive survey of methods for overcoming the class imbalance problem in fraud detection (2012)

17.

Maurya, C.K., Toshniwal, D., Venkoparao, G.V.: Online anomaly detection via class-imbalance learning. In: 2015 Eighth International Conference on Contemporary Computing (IC3), pp. 30–35 (2015)

18.

Bekkar, M., Djema, H., Alitouche, T.: Evaluation measures for models assessment over imbalanced data sets. J. Inf. Eng. Appl. 3, 27–38 (2013)

Titel: Default Prediction Using Network Based Features
verfasst von: Lorena Poenaru-Olaru
Judith Redi
Arthur Hovanesyan
Huijuan Wang
Verlag: Springer International Publishing
Buch: Complex Networks & Their Applications X
Print ISBN: 978-3-030-93408-8

Electronic ISBN: 978-3-030-93409-5

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-93409-5_60

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner