Credit default poses significant challenges to financial institutions and consumers, resulting in substantial financial losses and diminished trust. As such, credit default risk management has been a critical topic in the financial industry. In this paper, we present Combinatorial Fusion Analysis (CFA), a model fusion framework, that combines multiple machine learning algorithms to detect and predict credit card approval with high accuracy. We present the design methodology and implementation using five pre-trained models. The CFA results show an accuracy of 89.13% which is better than conventional machine learning and ensemble methods.

翻译：信贷违约给金融机构和消费者带来了重大挑战，导致巨大的财务损失和信任度下降。因此，信贷违约风险管理一直是金融行业的一个关键课题。在本文中，我们提出了一种模型融合框架——组合融合分析（CFA），该框架结合了多种机器学习算法，以高精度检测和预测信用卡审批。我们介绍了使用五个预训练模型的设计方法和实现过程。CFA结果显示其准确率达到89.13%，优于传统的机器学习方法和集成方法。