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Open Access

Cross-Domain Credit Default Prediction via Interpretable Ensemble Transfer

Zhida Shang1, Hefeng Meng1, Yibowen Zhao1, Ronghua Xu2, Yonghui Xu1( ), Lizhen Cui1
Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) & Software School, Shandong University, Jinan 250100, China
Business School, East China University of Political Science and Law, Shanghai 201620, China
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Abstract

The evaluation and prediction of credit risk have always been a research hotspot to ensure the healthy and orderly development of the credit market. Most researchers use deep learning to predict credit risk. However, when training data are too small, deep learning models often lead to overfitting. Although we have a large amount of available training data, we often cannot ensure that the data are evenly distributed, which is still not conducive to model training. In addition, deep learning is often difficult to explain, and the unexplained model is often difficult to gain the trust of users, thus reducing the usefulness of the model. To solve these problems, we propose an integrated cross-domain credit default prediction network, called Transfer Light Gradient Boosting Machine (TrLightGBM), based on interpretable integration transfer. This network considers the weight of data from different domains in training and implements cross-domain credit default prediction by adjusting the weight. The experiment shows that our method TrLightGBM not only achieves the interpretability of the model to a certain extent but also has good performance.

Keywords

decision treecross-domainensemble learningcredit default predictioninterpretable model

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International Journal of Crowd Science
Volume 7 Issue 3,
September 2023
Pages 106-112
DOI: 10.26599/IJCS.2023.9100011
Cite this article:
Shang Z, Meng H, Zhao Y, et al. Cross-Domain Credit Default Prediction via Interpretable Ensemble Transfer. International Journal of Crowd Science, 2023, 7(3): 106-112. https://doi.org/10.26599/IJCS.2023.9100011

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Received: 30 December 2022
Revised: 23 May 2023
Accepted: 29 May 2023
Published: 30 September 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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