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 liabilities over time, and adds the default rate calculation considering the characteristics of the default rate when a bank borrows. Meanwhile, the XG...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2023.2.00489 |
| _version_ | 1797340750077231104 |
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| author | Li Xin Li Lin |
| author_facet | Li Xin Li Lin |
| author_sort | Li Xin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T10:07:50Z |
| format | Article |
| id | doaj.art-867bec301d804368ab796bea2af42320 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:07:50Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-867bec301d804368ab796bea2af423202024-01-29T08:52:33ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.00489A deep learning model-based approach to financial risk assessment and predictionLi Xin0Li Lin11School of Finance, Sichuan Vocational College of Finance and Economics, Chengdu, Sichuan, 610000, China.1School of Finance, Sichuan Vocational College of Finance and Economics, Chengdu, Sichuan, 610000, China.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.https://doi.org/10.2478/amns.2023.2.00489financial riskunbundling network modelgrcforest modeloptimization functionactivation function68t05 |
| spellingShingle | Li Xin Li Lin A deep learning model-based approach to financial risk assessment and prediction Applied Mathematics and Nonlinear Sciences financial risk unbundling network model grcforest model optimization function activation function 68t05 |
| title | A deep learning model-based approach to financial risk assessment and prediction |
| title_full | A deep learning model-based approach to financial risk assessment and prediction |
| title_fullStr | A deep learning model-based approach to financial risk assessment and prediction |
| title_full_unstemmed | A deep learning model-based approach to financial risk assessment and prediction |
| title_short | A deep learning model-based approach to financial risk assessment and prediction |
| title_sort | deep learning model based approach to financial risk assessment and prediction |
| topic | financial risk unbundling network model grcforest model optimization function activation function 68t05 |
| url | https://doi.org/10.2478/amns.2023.2.00489 |
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