The design of consistent classifiers to forecast credit-granting choices is critical for many financial decision-making practices. Although a number of artificial and statistical techniques have been developed to predict customer insolvency, how to provide an inclusive appraisal of prediction models and recommend adequate classifiers is still an imperative and understudied area in credit default prediction (CDP) modeling. Previous evidence demonstrates that the ranking of classifiers varies for different criteria with measures under different circumstances. In this study, we address this methodological flaw by proposing the simultaneous application of support vector machine and probabilistic neural network (PNN)-based CDP algorithms, together with frequently used high-performance models. We fill the gap by introducing a set of multidimensional evaluation measures combined with some novel metrics that are helpful in discovering unseen features of the model’s performance. For effectiveness and feasibility purposes, six real-world credit data sets have been applied. Our empirical study shows that the PNN model is more robust than its rivals, and traditional performance evaluations are more or less consistent with their original counterparts. With these contributions, therefore, our investigations offer several advantages to practitioners of financial risk management.

中文翻译：

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使用支持向量机和概率神经网络的信用违约预测

设计一致的分类器来预测信贷授予选择对于许多财务决策实践至关重要。尽管已经开发了许多人工和统计技术来预测客户破产，但如何提供对预测模型的包容性评估并推荐适当的分类器仍然是信用违约预测 (CDP) 建模中一个必要且未被充分研究的领域。先前的证据表明，分类器的排名因不同的标准和不同情况下的措施而异。在这项研究中，我们通过同时应用支持向量机和基于概率神经网络 (PNN) 的 CDP 算法以及常用的高性能模型来解决这一方法缺陷。我们通过引入一组多维评估措施以及一些有助于发现模型性能的不可见特征的新指标来填补这一空白。出于有效性和可行性的目的，已经应用了六个真实世界的信用数据集。我们的实证研究表明，PNN 模型比其竞争对手更稳健，传统的性能评估或多或少与其原始对应物一致。因此，有了这些贡献，我们的调查为金融风险管理从业者提供了几个优势。我们的实证研究表明，PNN 模型比其竞争对手更稳健，传统的性能评估或多或少与其原始对应物一致。因此，有了这些贡献，我们的调查为金融风险管理从业者提供了几个优势。我们的实证研究表明，PNN 模型比其竞争对手更稳健，传统的性能评估或多或少与其原始对应物一致。因此，有了这些贡献，我们的调查为金融风险管理从业者提供了几个优势。