A comparison of neural networks and linear scoring models in the credit union environment
A comparison of neural networks and linear scoring models in the credit union environment
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DOI：
10.1016/0377-2217(95)00246-4
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发表时间：
1996-11-22
影响因子：
6.4
通讯作者：
Overstreet, GA
中科院分区：
文献类型：
--
作者：
Desai, VS;Crook, JN;Overstreet, GA
本论文的目的是探索的能力，神经网络，如多层感知器和模块化神经网络，和传统的技术，如线性判别分析和逻辑回归，在信用社环境中建立信用评分模型。此外，由于资金和小样本量往往排除使用定制的信用评分模型在小型信用合作社，我们调查的通用模型的性能，并与定制的模型进行比较。我们的研究结果表明，定制的神经网络提供了一个非常有前途的途径，如果性能的措施是正确分类的不良贷款的百分比。然而，如果衡量绩效的指标是正确分类的良好和不良贷款的百分比，则逻辑回归模型与神经网络方法相当。通用模型的性能不如定制模型，特别是在正确分类不良贷款方面。虽然我们发现三个信用社的结果存在显着差异，但我们的模块化神经网络无法适应这些差异，这表明可能需要更创新的架构来构建有效的通用模型。
The purpose of the present paper is to explore the ability of neural networks such as multilayer perceptrons and modular neural networks, and traditional techniques such as linear discriminant analysis and logistic regression, in building credit scoring models in the credit union environment. Also, since funding and small sample size often preclude the use of customized credit scoring models at small credit unions, we investigate the performance of generic models and compare them with customized models. Our results indicate that customized neural networks offer a very promising avenue if the measure of performance is percentage of bad loans correctly classified. However, if the measure of performance is percentage of good and bad loans correctly classified, logistic regression models are comparable to the neural networks approach. The performance of generic models was not as good as the customized models, particularly when it came to correctly classifying bad loans. Although we found significant differences in the results for the three credit unions, our modular neural network could not accommodate these differences, indicating that more innovative architectures might be necessary for building effective generic models.