School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Comput Intell Neurosci. 2022 May 31;2022:4796075. doi: 10.1155/2022/4796075. eCollection 2022.
Commercial banks are facing unprecedented credit risk challenges as the financial market becomes more volatile. Based on this, this study proposes and builds a credit risk assessment model for commercial banks based on GANN from the standpoint of commercial banks. In order to provide commercial banks with an effective and dependable credit risk assessment method, the indicators in this study are classified using cluster analysis, and then various representative indicators are chosen using a factor model, which takes into account the comprehensiveness of the information and reduces the complexity of the subsequent empirical analysis. On this basis, the network structure, learning parameters, and learning algorithm of commercial banks' credit risk assessment models are determined. Furthermore, advancements in data preprocessing and genetic operation have been made. According to simulation results, the highest accuracy rate of this method is 94.17 percent, which is higher than the BPNN algorithm 89.46 percent and the immune algorithm 90.14 percent. The optimization algorithm presented in this study improves the convergence speed and search efficiency of traditional algorithms, and the final experimental results show that the scheme is feasible and effective and can be used for commercial bank credit risk assessment.
商业银行正面临着前所未有的信用风险挑战，因为金融市场变得更加不稳定。基于此，本研究从商业银行的角度出发，提出并构建了一种基于 GANN 的商业银行信用风险评估模型。为了为商业银行提供一种有效可靠的信用风险评估方法，本研究使用聚类分析对指标进行分类，然后使用因子模型选择各种有代表性的指标，该模型考虑了信息的全面性，并降低了后续实证分析的复杂性。在此基础上，确定了商业银行信用风险评估模型的网络结构、学习参数和学习算法。此外，还对数据预处理和遗传操作进行了改进。根据模拟结果，该方法的最高准确率为 94.17%，高于 BPNN 算法的 89.46%和免疫算法的 90.14%。本研究提出的优化算法提高了传统算法的收敛速度和搜索效率，最终的实验结果表明，该方案是可行和有效的，可用于商业银行信用风险评估。
