Graph neural network with self-attention and multi-task learning for credit default risk prediction

Zihao LI, Xianzhi WANG, Lina YAO, Yakun CHEN, Guandong XU, Ee-Peng LIM

Research output: Chapter in Book/Report/Conference proceedingChapters

2 Citations (Scopus)

Abstract

We propose a graph neural network with self-attention and multi-task learning (SaM-GNN) to leverage the advantages of deep learning for credit default risk prediction. Our approach incorporates two parallel tasks based on shared intermediate vectors for input vector reconstruction and credit default risk prediction, respectively. To better leverage supervised data, we use self-attention layers for feature representation of categorical and numeric data; we further link raw data into a graph and use a graph convolution module to aggregate similar information and cope with missing values during constructing intermediate vectors. Our method does not heavily rely on feature engineering work and the experiments show our approach outperforms several types of baseline methods; the intermediate vector obtained by our approach also helps improve the performance of ensemble learning methods. Copyright © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG.

Original languageEnglish
Title of host publicationWeb information systems engineering – WISE 2022: 23rd International Conference, Biarritz, France, November 1–3, 2022, proceedings
EditorsRichard CHBEIR, Helen HUANG, Fabrizio SILVESTRI, Yannis MANOLOPOULOS, Yanchun ZHANG
Place of PublicationCham
PublisherSpringer
Pages616-629
ISBN (Electronic)9783031208911
ISBN (Print)9783031208904
DOIs
Publication statusPublished - 2022

Citation

Li, Z., Wang, X., Yao, L., Chen, Y., Xu, G., & Lim, E.-P. (2022). Graph neural network with self-attention and multi-task learning for credit default risk prediction. In R. Chbeir, H. Huang, F. Silvestri, Y. Manolopoulos, & Y. Zhang (Eds.), Web information systems engineering – WISE 2022: 23rd International Conference, Biarritz, France, November 1–3, 2022, proceedings (pp. 616-629). Springer. https://doi.org/10.1007/978-3-031-20891-1_44

Keywords

  • Credit default risk prediction
  • Graph neural network
  • Self-attention
  • Multi-task learning

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