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July 10, 2026Scientific ReportsOpen Access

Intelligent credit risk assessment for rural small and micro enterprises based on hybrid deep learning architecture

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Authors

TXTao XuChangchun University of Science and Technology CLChen LiuChangchun University of Science and Technology

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Randomized trial evaluates a hybrid deep learning model for credit risk assessment in rural small and micro enterprises, highlighting its potential benefits.

Key Points

This research aims to improve credit risk assessment for rural small and micro enterprises by utilizing a hybrid deep learning model.
Developed a model combining convolutional networks, bidirectional LSTM, and attention layer.
Analyzed 8,647 real loan applications using structured financial indicators and behavioral sequences.
Conducted assessments through ablation studies and subgroup fairness probes.
Achieved AUC-ROC of 0.943 and AUC-PR of 0.867 for credit risk prediction.
Reported F1-score of 0.849 and recall of 87.4%.
Generalization tested through temporal and province-level holdouts, showing robustness of model.

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/6a508afd6eeac72a4379ff27 https://doi.org/10.1038/s41598-026-59328-7

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Implication

Randomized trial evaluates a hybrid deep learning model for credit risk assessment in rural small and micro enterprises, highlighting its potential benefits.

Intelligent credit risk assessment for rural small and micro enterprises based on hybrid deep learning architecture

Discussion

Member takes

Implication

Key Points

Cite This Study

Intelligent credit risk assessment for rural small and micro enterprises based on hybrid deep learning architecture

Discussion

Member takes

Implication

Key Points

Cite This Study

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