Research Article
Systematic Risk Prediction in Commercial Banks Based on Random Forest and BP Neural Network
@INPROCEEDINGS{10.4108/eai.6-1-2023.2330362, author={Junbin Zhang and Peiying Zhang and Shiyang Song and Junyu Su and Jinhai Tang}, title={Systematic Risk Prediction in Commercial Banks Based on Random Forest and BP Neural Network}, proceedings={Proceedings of the 2nd International Conference on Big Data Economy and Digital Management, BDEDM 2023, January 6-8, 2023, Changsha, China}, publisher={EAI}, proceedings_a={BDEDM}, year={2023}, month={6}, keywords={random forest bp neural network risk forecast machine learning}, doi={10.4108/eai.6-1-2023.2330362} }- Junbin Zhang
Peiying Zhang
Shiyang Song
Junyu Su
Jinhai Tang
Year: 2023
Systematic Risk Prediction in Commercial Banks Based on Random Forest and BP Neural Network
BDEDM
EAI
DOI: 10.4108/eai.6-1-2023.2330362
Abstract
Since the 1990s, the frequent occurrence of systemic financial risks culminating in financial crises has had a serious impact on the economies and financial systems of all countries. Systemic risk analysis has become a very important task for most central banks in the wake of the global financial crisis (GFC). The sudden and destructive nature of systemic financial risks requires that we should pay attention to the foresight of systemic financial risks. In this study, based on establishing a system of systemic financial risk characteristics indicators in China, we construct machine learning models of random forest and support vector machine to warn systemic financial risks in China, and compare the warning effects of the two models using confusion matrix, ROC curve (Receiver Operating Characteristic Curve) and dynamic warning analysis, and The main factors that drive up the level of systemic financial risk in China are identified.