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首页> 外文期刊>Journal of systems science & information >A Credit Risk Evaluation Approach to Neural Network Training by Means of Financial Ratios
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A Credit Risk Evaluation Approach to Neural Network Training by Means of Financial Ratios

机译：基于财务比率的神经网络训练信用风险评估方法

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摘要

In recent yeaxs artificial neural networks are used to recognize the risk category of investigated companies. The research is based on data from 81 listed enterprises that applied for credit in domestic regional banks operating in China. The backpropa-gation algorithm-the multilayer feedforward network structure is described. Each firm is described by 9 diagnostic variables and potential borrowers are classified into four classes. The efficiency of classification is evaluated in terms of classification errors calculated from the actual classification made by the credit officers. The results of the experiments show that LevenbergMarque training error is smallest among 4 learning algorithms and its performance is better, and application of artificial neural networks and classification functions can support the creditworthiness evaluation of borrowers.
机译：在最近的yeaxs中，人工神经网络用于识别被调查公司的风险类别。该研究基于来自81家在中国境内区域性银行申请信贷的上市企业的数据。描述了反向传播算法-多层前馈网络结构。每个公司都有9个诊断变量来描述，潜在借款人分为四类。根据从信贷员的实际分类中计算出的分类错误来评估分类效率。实验结果表明，LevenbergMarque训练误差在4种学习算法中最小，性能更好，人工神经网络和分类函数的应用可以支持借款人的信用评价。

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