<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper proposes a split-lending network model for bank credit risk, calculates whether a bank fails by simulating the changes in bank assets and liabilities over time, and adds the default rate calculation considering the characteristics of the default rate when a bank borrows. Meanwhile, the XGBoost-based classifier is used instead of random forest to improve the accuracy of classification, and the grcForest_XGB model is established. The activation function Sigmoid, the error function mean square error function, and the adam optimization function with the best effect at present are used to predict the accuracy of the grcForest_XGB model, and the different models are compared with the grcForest model for comparison experiments. The experiments show that the grcForest model has higher AUC and KS metrics of 0.8224 and 0.6368, respectively. The recall value is 0.8319, which ranks first among the six models. The Acc value is 0.9732, which is only 0.05 lower than LSTM, and is at a higher level. This study shows that the model is more accurate for risk assessment, can predict financial risks in advance, and make an effective assessment of financial risks.</p>
</abstract>

This paper proposes a split-lending network model for bank credit risk, calculates whether a bank fails by simulating the changes in bank assets and liabilities over time, and adds the default rate calculation considering the characteristics of the default rate when a bank borrows. Meanwhile, the XGBoost-based classifier is used instead of random forest to improve the accuracy of classification, and the grcForest_XGB model is established. The activation function Sigmoid, the error function mean square error function, and the adam optimization function with the best effect at present are used to predict the accuracy of the grcForest_XGB model, and the different models are compared with the grcForest model for comparison experiments. The experiments show that the grcForest model has higher AUC and KS metrics of 0.8224 and 0.6368, respectively. The recall value is 0.8319, which ranks first among the six models. The Acc value is 0.9732, which is only 0.05 lower than LSTM, and is at a higher level. This study shows that the model is more accurate for risk assessment, can predict financial risks in advance, and make an effective assessment of financial risks.

A deep learning model-based approach to financial risk assessment and prediction

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"A deep learning model-based approach to financial risk assessment and prediction"}

This paper proposes a split-lending network model for bank credit risk, calculates whether a bank fails by simulating the changes in bank assets and...

A deep learning model-based approach to financial risk assessment and prediction

Data publikacji: 04 paź 2023

Zakres stron: -

Otrzymano: 06 lis 2022

Przyjęty: 14 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00489

Słowa kluczowe
Financial risk, Unbundling network model, grcForest model, Optimization function, Activation function

© 2023 Xin Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A deep learning model-based approach to financial risk assessment and prediction

Data publikacji: 04 paź 2023

Zakres stron: -

Otrzymano: 06 lis 2022

Przyjęty: 14 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00489

Słowa kluczoweFinancial risk, Unbundling network model, grcForest model, Optimization function, Activation function

© 2023 Xin Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Financial risk, Unbundling network model, grcForest model, Optimization function, Activation function