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.
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Notes
- 1.
Small Business Act for Europe (SBA) Fact Sheet - Netherlands.
References
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)
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/s13753-020-00247-0
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)
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)
Wang, H., Hernandez, J.M., Van Mieghem, P.: Betweenness centrality in a weighted network. Phys. Rev. E 77, 046105 (2008)
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)
Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. 31, 833–852 (2019)
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)
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)
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)
Brennan, P.J.: A comprehensive survey of methods for overcoming the class imbalance problem in fraud detection (2012)
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)
Bekkar, M., Djema, H., Alitouche, T.: Evaluation measures for models assessment over imbalanced data sets. J. Inf. Eng. Appl. 3, 27–38 (2013)
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Poenaru-Olaru, L., Redi, J., Hovanesyan, A., Wang, H. (2022). Default Prediction Using Network Based Features. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_60
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DOI: https://doi.org/10.1007/978-3-030-93409-5_60
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