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Forecasting credit default risk with graph attention networks · Study

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ArticleForecasting credit default risk with graph attention networks

Binbin Zhou2023

Article2023

Forecasting credit default risk with graph attention networks

Binbin Zhou · Jiayun Jin · Hang Zhou · Xuye Zhou · Longxiang Shi · Jianhua Ma +1 more

29 citations·0/0 files stored·Electronic Commerce Research and Applications

No readable copy stored yet — this record is metadata only.

Financial Distress and Bankruptcy Prediction Advanced Graph Neural Networks Customer churn and segmentation

Abstract

No abstract was published for this record.

Citations in this library

Stored only when both papers are in the corpus — 29 citations exist upstream.

References

2018Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning 2012Multiperiod corporate default prediction—A forward intensity approach 2018Credit Risk Analysis Using Machine and Deep Learning Models 2020A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique 2016A deep learning approach for credit scoring using credit default swaps 2017Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending 2019A study on predicting loan default based on the random forest algorithm 2018Predicting mortgage default using convolutional neural networks

Details

Year2023

Typearticle

Published2023-11-01

VenueElectronic Commerce Research and Applications

ISSN-L1567-4223

Languageen

Citations29

Files0 public / 0

ShelfFinancial Distress and Bankruptcy Prediction

Identifiers

doi10.1016/j.elerap.2023.101332

openalex_workW4388625980

Links

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