Binary choice models for external auditors decisions in Asian banks

Abstract

The present study investigates the efficiency of four classification techniques, namely discriminant analysis, logit analysis, UTADIS multicriteria decision aid, and nearest neighbours, in the development of classification models that could assist auditors during the examination of Asian commercial banks. To develop the auditing models and examine their classification ability, the dataset is split into two distinct samples. The training sample consists of 1,701 unqualified financial statements and 146 ones that received a qualified opinion over the period 1996-2001. The models are tested in a holdout sample of 527 unqualified financial statements and 52 ones that received a qualified opinion over the period [2002][2003][2004]. The results show that the developed auditing models can discriminate between financial statements that should receive qualified opinions from the ones that should receive unqualified opinions with a satisfactory accuracy. Both financial variables and the environment in which banks operate appear to be important. The highest classification accuracy is achieved by UTADIS, followed by logit analysis, nearest neighbours and discriminant analysis.

Predicting Audit Opinion in Consolidated Financial Statements with Artificial Neural Networks

“…Additionally, profitability and credit rating were the most predictive variables. Subsequently, these same authors replicated this study with a sample of Asian commercial banks [23].…”