Predicting Audit Opinion in Consolidated Financial Statements with Artificial Neural Networks

Abstract

The models for predicting audit opinion analyze the variables that affect the probability of obtaining a qualified opinion. This helps auditors to plan revision procedures and control their performances. Despite their apparent relevance, existing models have only focused on the context of individual financial statements and none have referred to consolidated financial statements. The consolidated information is essential for decision-making processes and understanding the true financial situation of a company. Our objective is to provide a new audit opinion prediction model for consolidated financial statements. To this end, a sample of group of Spanish companies was chosen and an artificial neural network technique, the multilayer perceptron, was used. The results show that the developed method managed to predict the audit opinion with accuracy above 86%. Moreover, there exist important differences concerning the most significant variables in the audit opinion prediction for individual accounts, since when using consolidated financial statements, the variables referring to industry, group size, auditor, and board members were converted into the main explanatory parameters of the prediction.