Xu Tianpei, Qu Min
Department of Educational Technology, Hulunbuir University, Hulunbuir, 021008, China.
Department of Digital Commerce, Jiangsu Vocational Institute of Commerce, Nanjing, 211168, China.
Heliyon. 2024 Apr 23;10(9):e30134. doi: 10.1016/j.heliyon.2024.e30134. eCollection 2024 May 15.
In today's banking and financial system, using a credit card has become indispensable. The credit card industry has existed due to a shift in consumer preferences and a rise in national economic growth. The number of issuing banks, card issuers, and transaction volumes has increased significantly. Nevertheless, owing to the growth in the number of transactions made with credit cards, both the total amount due and the rate of defaults on credit card loans have become issues that cannot be neglected. This issue must be resolved to ensure the continued and prosperous growth of the banking industry in the years to come. Currently, a few optimization algorithms-Whale optimization algorithm (WOA), Harmony Search (HS), Multi-verse optimization (MVO), and Vortex Search (VS)-have been used to achieve this purpose. However, because credit card default data is volatile and unequal, it is challenging for typical optimization algorithms to offer steady approaches with optimal performance. Studies have indicated that optimizing algorithms with suitable properties can significantly improve performance. To improve performance, some tuning was applied to the ANN. This study will assess twenty-three parameters, and the efficacy of all four approaches will be compared using ROC and AUC evaluations. The suggested model's performance is contrasted with a scenario where the classifiers were trained using original data. In contrast, the AUC values for VS-MLP were 0.7407 and 0.7271, while those for HS-MLP were 0.7074 and 0.6997. In the training and testing phases, AUC values of 0.7469 and 0.7329 from MVO-MLP and 0.72 and 0.7185 from WOA-MLP, respectively. The results show that the training accuracy of HS, VSA, MVO, and WOA are similar; MVO has the highest training accuracy. The credit card industry can benefit significantly from this methodology, which may help resolve default probabilities.
在当今的银行和金融体系中，使用信用卡已变得不可或缺。信用卡行业的存在得益于消费者偏好的转变以及国民经济增长的上升。发卡银行、卡发行商的数量以及交易量都显著增加。然而，由于信用卡交易数量的增长，信用卡贷款的到期总额和违约率都已成为不可忽视的问题。必须解决这个问题，以确保银行业在未来几年能够持续繁荣发展。目前，一些优化算法——鲸鱼优化算法（WOA）、和声搜索算法（HS）、多宇宙优化算法（MVO）和涡旋搜索算法（VS）——已被用于实现这一目的。然而，由于信用卡违约数据波动且不均衡，典型的优化算法难以提供具有最佳性能的稳定方法。研究表明，具有合适特性的优化算法可以显著提高性能。为了提高性能，对人工神经网络（ANN）进行了一些调整。本研究将评估二十三个参数，并使用ROC和AUC评估来比较这四种方法的有效性。将所提出模型的性能与使用原始数据训练分类器的情况进行对比。相比之下，VS - MLP的AUC值分别为0.7407和0.7271，而HS - MLP的AUC值分别为0.7074和0.6997。在训练和测试阶段，MVO - MLP的AUC值分别为训练阶段0.7469和测试阶段0.7329，WOA - MLP的AUC值分别为训练阶段0.72和测试阶段0.7185。结果表明，HS、VSA、MVO和WOA的训练准确率相似；MVO的训练准确率最高。信用卡行业可以从这种方法中显著受益，这可能有助于解决违约概率问题。
