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A Comparison of Selected Artificial Neural Networks that Help Auditors Evaluate Client Financial Viability

机译：所选人工神经网络的比较，这些神经网络可帮助审计师评估客户的财务生存能力

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摘要

This study compares the performance of three artificial neural network (ANN) approacheback propagation, categorical learning, and probabilistic neural network-- as classification tools to assist and support auditor's judgment about a client's continued financial viability into the future (going concern status). ANN performance is compared on the basis of overall error rates and estimated relative costs of misclassification (incorrectly classifying an insolvent firm as solvent versus classifying a solvent firm as insolvent). When only the overall error rate is considered, the probabilistic neural network is the most reliable in classification, followed by backpropagation and categorical learning network. When the estimated relative costs of misclassification are considered, the categorical learning network is the least costly, followed by backpropagation and probabilistic neural network.
机译：这项研究比较了三种人工神经网络（ANN）的反向传播，分类学习和概率神经网络的性能-作为分类工具，以协助和支持审计师对客户未来财务状况的判断（持续关注状态）。根据总错误率和错误分类的估计相对成本（将溶剂公司错误分类为溶剂与将溶剂公司错误分类为溶剂）对ANN的性能进行比较。当仅考虑整体错误率时，概率神经网络在分类中最可靠，其次是反向传播和分类学习网络。当考虑估计的错误分类的相对成本时，分类学习网络的成本最低，其次是反向传播和概率神经网络。

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