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

<p>With the strong promotion of financial globalization and marketization and the rapid development of financial industry innovation, credit risk management is becoming an important work that banks need to pay more and more attention to. This paper focuses on the construction of the bank credit risk model to carry out research. First of all, according to the credit characteristics selection method, the optimal indicators are combined with the indicators for the correlation test of variables. On this basis, the sample data is calculated using the up-sampling method of SMOTE to complete the algorithm and sampling. After obtaining the data, data preprocessing is carried out using the MinMaxScaler scaler method, and the processed data is inputted into the improved momentum BP neural network to complete the credit risk assessment. Profiling with risk feature data, it is obtained that each feature is not a separate individual from the other. They are interdependent and connected and have obvious correlations. For example, there are 47 features in the dataset with sample missing ratios greater than 0.97, which is too high and indicates an invalid feature. The results of the study show that SMOTE’s up-sampling method and Momentum BP algorithm can quickly utilize big data to provide a more accurate decision basis for bank credit risk assessment.</p>
</abstract>

With the strong promotion of financial globalization and marketization and the rapid development of financial industry innovation, credit risk management is becoming an important work that banks need to pay more and more attention to. This paper focuses on the construction of the bank credit risk model to carry out research. First of all, according to the credit characteristics selection method, the optimal indicators are combined with the indicators for the correlation test of variables. On this basis, the sample data is calculated using the up-sampling method of SMOTE to complete the algorithm and sampling. After obtaining the data, data preprocessing is carried out using the MinMaxScaler scaler method, and the processed data is inputted into the improved momentum BP neural network to complete the credit risk assessment. Profiling with risk feature data, it is obtained that each feature is not a separate individual from the other. They are interdependent and connected and have obvious correlations. For example, there are 47 features in the dataset with sample missing ratios greater than 0.97, which is too high and indicates an invalid feature. The results of the study show that SMOTE’s up-sampling method and Momentum BP algorithm can quickly utilize big data to provide a more accurate decision basis for bank credit risk assessment.

Optimizing bank credit risk assessment models using big data analytics

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Optimizing bank credit risk assessment models using big data analytics"}

With the strong promotion of financial globalization and marketization and the rapid development of financial industry innovation, credit risk management is...

Optimizing bank credit risk assessment models using big data analytics

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 25. Apr. 2024

Akzeptiert: 11. Juli 2024

DOI: https://doi.org/10.2478/amns-2024-2139

Schlüsselwörter
Bank credit, SMOTE, Momentum BP, Risk assessment

© 2024 Feiyu Yang et al., published by Sciendo

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

Optimizing bank credit risk assessment models using big data analytics

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 25. Apr. 2024

Akzeptiert: 11. Juli 2024

DOI: https://doi.org/10.2478/amns-2024-2139

SchlüsselwörterBank credit, SMOTE, Momentum BP, Risk assessment

© 2024 Feiyu Yang et al., published by Sciendo

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

Schlüsselwörter
Bank credit, SMOTE, Momentum BP, Risk assessment