Artificial Neural Networks Applied to Ratio Analysis in the Analytical Review Process

Abstract

Abstract Experts claim that artificial neural network (ANN) technology can outperform standard statistical methods when applied to examine actual financial data. Researchers have used ANNs to analyze bankruptcy prediction, bond rating and the going‐concern problem. Financial firms have employed ANNs commercially to predict commercial bank failures, detect credit card fraud and verify signatures. For accounting and auditing problems, however, application of ANN technology has been limited. Preliminary experiments tested whether an ANN offered improved performance in recognizing material misstatements during the analytical review process of auditing. Four years of audited financial data from a medium‐sized distributor were input as data streams to calibrate the ANN across fifteen financial accounts. Researchers compared a presumed lack of actual errors and certain seeded material errors with signals from the ANN analytical review process to evaluate performance. Results were compared to analyses where financial ratios and regression methods were employed as analytical review techniques. Results tentatively suggest that the ANN method recognized patterns within financial accounts more effectively than did financial ratio and regression methods. ANNs applied as a forecasting tool seem useful for identifying patterns that can indicate potential investigations of a firm's unaudited financial data in the current year.

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