Abstract
Credit assessment for Small and Medium-sized Enterprises (SMEs) is of great interest to financial institutions such as commercial banks and Peer-to-Peer lending platforms. Effective credit rating modeling can help them make loan-granted decisions while limiting their risk exposure. Despite a substantial amount of research being conducted in this domain, there are three existing issues. Firstly, many of them are mainly developed based on financial statements, which usually are not publicly-accessible for SMEs. Secondly, they always neglect the rich relational information embodied in financial networks. Finally, existing graph-neural-network-based (GNN) approaches for credit assessment are only applicable to homogeneous networks. To address these issues, we propose a heterogeneous-attention-network-based model (HAT) to facilitate SMEs bankruptcy prediction using publicly-accessible data. Specifically, our model has two major components: a heterogeneous neighborhood encoding layer and a triple attention output layer. While the first layer can encapsulate target nodes’ heterogeneous neighborhood information to address the graph heterogeneity, the latter can generate the prediction by considering the importance of different metapath-based neighbors, metapaths, and networks. Extensive experiments in a real-world dataset demonstrate the effectiveness of our model compared with baselines.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Small and Medium Enterprises (SME) finance (2020). https://www.worldbank.org/en/topic/smefinance. Accessed 4 Nov 2020
Chaudhuri, A., De, K.: Fuzzy support vector machine for bankruptcy prediction. Appl. Soft Comput. 11(2), 2472–2486 (2011)
Chen, Z., Chen, W., Shi, Y.: Ensemble learning with label proportions for bankruptcy prediction. Expert Syst. Appl. 146, 113115 (2020)
Cheng, D., Zhang, Y., Yang, F., Tu, Y., Niu, Z., Zhang, L.: A dynamic default prediction framework for networked-guarantee loans. In: CIKM (2019)
Erdogan, B.E.: Prediction of bankruptcy using support vector machines: an application to bank bankruptcy. J. Stat. Comput. Simul. 83(8), 1543–1555 (2013)
Fu, X., Zhang, J., Meng, Z., King, I.: MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding. In: WWW 2020 (2020)
Hauser, R.P., Booth, D.: Predicting bankruptcy with robust logistic regression. J. Data Sci. 9(4), 565–584 (2011)
Huang, Q., Yu, J., Wu, J., Wang, B.: Heterogeneous graph attention networks for early detection of rumors on Twitter. In: IJCNN (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2016)
Linmei, H., Yang, T., Shi, C., Ji, H., Li, X.: Heterogeneous graph attention networks for semi-supervised short text classification. In: EMNLP-IJCNLP (2019)
Mai, F., Tian, S., Lee, C., Ma, L.: Deep learning models for bankruptcy prediction using textual disclosures. Eur. J. Oper. Res. 274(2), 743–758 (2019)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: KDD (2014)
Shumovskaia, V., Fedyanin, K., Sukharev, I., Berestnev, D., Panov, M.: Linking bank clients using graph neural networks powered by rich transactional data (2020)
Vaswani, A., et al.: Attention is all you need. In: NeurIPS (2017)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: ICLR (2017)
Wang, H., Zhou, C., Chen, X., Wu, J., Pan, S., Wang, J.: Graph stochastic neural networks for semi-supervised learning. In: NeurIPS (2020)
Wang, X., et al.: Heterogeneous graph attention network. In: WWW (2019)
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. In: IEEE Transactions on Neural Networks and Learning Systems (2020)
Zhang, C., Song, D., Huang, C., Swami, A., Chawla, N.V.: Heterogeneous graph neural network. In: SIGKDD (2019)
Zhu, S., Pan, S., Zhou, C., Wu, J., Cao, Y., Wang, B.: Graph geometry interaction learning. In: NeurIPS (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zheng, Y., Lee, V.C.S., Wu, Z., Pan, S. (2021). Heterogeneous Graph Attention Network for Small and Medium-Sized Enterprises Bankruptcy Prediction. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12712. Springer, Cham. https://doi.org/10.1007/978-3-030-75762-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-75762-5_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75761-8
Online ISBN: 978-3-030-75762-5
eBook Packages: Computer ScienceComputer Science (R0)