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

Abstract

This study compares the performance of three artificial neural network (ANN) approaches-backpropagation, categorical learning, and probabilistic neural networkas 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.

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