School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu 611130, China.
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China; Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China; Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing 100190, China.
Neural Netw. 2021 Nov;143:327-344. doi: 10.1016/j.neunet.2021.06.016. Epub 2021 Jun 16.
Credit risk evaluation is a crucial yet challenging problem in financial analysis. It can not only help institutions reduce risk and ensure profitability, but also improve consumers' fair practices. The data-driven algorithms such as artificial intelligence techniques regard the evaluation as a classification problem and aim to classify transactions as default or non-default. Since non-default samples greatly outnumber default samples, it is a typical imbalanced learning problem and each class or each sample needs special treatment. Numerous data-level, algorithm-level and hybrid methods are presented, and cost-sensitive support vector machines (CSSVMs) are representative algorithm-level methods. Based on the minimization of symmetric and unbounded loss functions, CSSVMs impose higher penalties on the misclassification costs of minority instances using domain specific parameters. However, such loss functions as error measurement cannot have an obvious cost-sensitive generalization. In this paper, we propose a robust cost-sensitive kernel method with Blinex loss (CSKB), which can be applied in credit risk evaluation. By inheriting the elegant merits of Blinex loss function, i.e., asymmetry and boundedness, CSKB not only flexibly controls distinct costs for both classes, but also enjoys noise robustness. As a data-driven decision-making paradigm of credit risk evaluation, CSKB can achieve the "win-win" situation for both the financial institutions and consumers. We solve linear and nonlinear CSKB by Nesterov accelerated gradient algorithm and Pegasos algorithm respectively. Moreover, the generalization capability of CSKB is theoretically analyzed. Comprehensive experiments on synthetic, UCI and credit risk evaluation datasets demonstrate that CSKB compares more favorably than other benchmark methods in terms of various measures.
信用风险评估是金融分析中至关重要但具有挑战性的问题。它不仅可以帮助机构降低风险并确保盈利能力，还可以提高消费者的公平实践。人工智能技术等数据驱动算法将评估视为分类问题，旨在将交易分类为违约或非违约。由于非违约样本大大超过违约样本，因此这是一个典型的不平衡学习问题，每个类或每个样本都需要特殊处理。提出了许多数据级、算法级和混合方法，代价敏感支持向量机（CSSVM）是代表性的算法级方法。基于对称和无界损失函数的最小化，CSSVM 使用特定于域的参数对少数实例的分类错误成本施加更高的惩罚。然而，像错误度量这样的损失函数不能具有明显的代价敏感泛化。在本文中，我们提出了一种具有 Blinex 损失（CSKB）的稳健代价敏感核方法，可应用于信用风险评估。通过继承 Blinex 损失函数的优雅特性，即不对称性和有界性，CSKB 不仅灵活地控制了两个类别的不同成本，而且还具有噪声鲁棒性。作为信用风险评估的一种数据驱动决策范式，CSKB 可以为金融机构和消费者实现“双赢”局面。我们分别使用 Nesterov 加速梯度算法和 Pegasos 算法求解线性和非线性 CSKB。此外，还从理论上分析了 CSKB 的泛化能力。综合了合成数据集、UCI 数据集和信用风险评估数据集上的实验结果表明，CSKB 在各种指标上都比其他基准方法表现更优。
