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

Publication Type

Conference Proceeding Article

Publication Date

11-2022

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.

Keywords

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

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Web Information Systems Engineering, WISE 2022: 23rd International Conference, Biarritz, France, November 1-3: Proceedings

Volume

13742

First Page

616

Last Page

629

ISBN

9783031208911

Identifier

10.1007/978-3-031-20891-1_44

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/978-3-031-20891-1_44

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