Decision Sciences Journal
Volume 31, Number 2
Spring 2000
A Comparison of Selected Artificial Neural Networks that
Help Auditors Evaluate Client Financial Viability
Harlan L. Etheridge
Department of Accounting, College of Business, University of
Louisiana at Lafayette, Lafayette, LA 70504
Ram S. Sriram
School of Accountancy, J. Mack Robinson College of Business,
Georgia State University, 35 Broad Street, Atlanta, GA 30303-4321,
email: rsriram@gsu.edu
H. Y. Kathy Hsu
Department of Accounting, College of Business, University of
Louisiana at Lafayette, Lafayette, LA 70504
ABSTRACT. This study compares the performance of three
artificial neural network (ANN) approaches—backpropagation,
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.
Subject Areas: Artificial Intelligence, Auditor Judgment,
Decision Support, and Financial Distress. |