Credit risk prediction is essential for banks and financial institutions as it helps them to evade any inappropriate assessments that can lead to wasted opportunities or monetary losses. In recent times, the hybrid prediction model, a combination of traditional and modern artificial intelligence (AI) methods that provides better prediction capacity than the use of single techniques, has been introduced. Similarly, using conventional and topical artificial intelligence (AI) technologies, researchers have recommended hybrid models which amalgamate logistic regression (LR) with multilayer perceptron (MLP). To investigate the efficiency and viability of the proposed hybrid models, we compared 16 hybrid models created by combining logistic regression (LR), discriminant analysis (DA), and decision trees (DT) with four types of neural network (NN): adaptive neurofuzzy inference systems (ANFISs), deep neural networks (DNNs), radial basis function networks (RBFs) and multilayer perceptrons (MLPs). The experimental outcome, investigation, and statistical examination express the capacity of the planned hybrid model to develop a credit risk prediction technique different from all other approaches, as indicated by ten different performance measures. The classifier was authenticated on five real-world credit scoring data sets.

中文翻译：

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信用风险预测的混合模型：神经网络方法的应用

信用风险预测对银行和金融机构至关重要，因为它可以帮助他们规避任何可能导致机会浪费或金钱损失的不当评估。最近，混合预测模型被引入，它结合了传统和现代人工智能 (AI) 方法，提供比使用单一技术更好的预测能力。同样，使用传统和局部人工智能 (AI) 技术，研究人员推荐了将逻辑回归 (LR) 与多层感知器 (MLP) 合并的混合模型。为了研究提出的混合模型的效率和可行性，我们比较了通过将逻辑回归 (LR)、判别分析 (DA) 和决策树 (DT) 与四种类型的神经网络 (NN) 相结合创建的 16 个混合模型：自适应神经模糊推理系统 (ANFIS)、深度神经网络 (DNN)、径向基函数网络 (RBF) 和多层感知器 (MLP)。实验结果、调查和统计检查表明了计划中的混合模型开发不同于所有其他方法的信用风险预测技术的能力，如十种不同的绩效指标所示。该分类器在五个真实世界的信用评分数据集上进行了身份验证。正如十个不同的绩效指标所表明的那样。该分类器在五个真实世界的信用评分数据集上进行了身份验证。正如十个不同的绩效指标所表明的那样。该分类器在五个真实世界的信用评分数据集上进行了身份验证。