External Auditors’ Decisions in EU Credit Institutions: A Multicriteria Approach

Abstract

In this paper we develop classification models that could assist auditors in their decision to issue a qualified or unqualified opinion during the auditing of EU credit institutions. The models are developed with the Utilites Additives Discriminantes (UTADIS) multicriteria technique and incorporate financial and non‐financial variables. Discriminant analysis is also employed for comparison purposes. The dataset consists of 80 qualified financial statements and 2,631 unqualified ones, from 446 credit institutions operating in eight EU countries over the period 1995–2002. The models are developed and tested using a ten‐fold cross‐validation approach. 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. The overall accuracy is 73.55% in the validation sample for a general model and increases up to 88.49% in the case of a model developed specifically for commercial banks.

Classification techniques for the identification of falsified financial statements: a comparative analysis

“…In addition to the seven fi nancial variables discussed above, I consider six nonfi nancial variables: fi nancial distress, client litigation, audit fi rm, auditors switching, loss or profi t in the year of audit opinion and whether a company is listed or unlisted. I use a score (UTADISCR) estimated from the utilités additives discriminantes (UTADIS) bankruptcy prediction model of Zopounidis et al (2006). I anticipate that the use of this measure, which indicates the likelihood of default of Greek fi rms over the 12 months following the date of its calculation, might provide useful information.…”

A Multiple Criteria Sorting Methodology With Multiple Classifi Cation Criteria and an Application to Country Risk Evaluation

“…There are several applications of UtADiS methodology to various fields in the literature. The identification of acquisition targets in the EU banking industry (Pasiouras et al 2007a), the reproduction of the auditors' opinion on the financial statements of the firms (Pasiouras et al 2007b), development of credit risk assessment models for financial institutions using publicly available financial data (Baourakis et al 2009) and development of classification models that could assist auditors in their decision to issue a qualified or unqualified opinion during the auditing of EU credit institutions (Gaganis et al 2006) are some recent examples of these studies. The evaluation of the performance of mutual funds (Pendaraki et al 2005), replication of the credit ratings issued by a rating agency (Doumpos, Pasiouras 2005), investigation of the relationship between client performance measures and the auditors' qualification decisions (Spathis et al 2003), evaluation of credit applications in shipping industry (Dimitras et al 2002), detecting falsified financial statements (Spathis et al 2002), evaluation of Greek industrial SMEs' performance (Voulgaris et al 2000), energy analysis and policy making (Diakoulakia et al 1999), business failure prediction (Zopounidis, Doumpos 1999a), bankruptcy risk and business failure prediction (Zopounidis, Doumpos 1999b), are other application oriented studies of UtADiS methodology.…”

“…to evaluate the performance of UtADiS methodology numerous studies compare the results of UtADiS with other techniques. comparisons with MHDiS and PAiRclASS (Pasiouras et al 2007a), MHDiS (Pasiouras et al 2007b), linear discriminant analysis and ordered logistic regression (Baourakis et al 2009), discriminant analysis (Gaganis et al 2006), linear discriminant analysis, quadratic discriminant analysis, logit analysis, linear programming formulation, and a nearest neighbour classifier (Pendaraki et al 2005), linear discriminant analysis, logistic analysis, the nearest-neighbour algorithm, probabilistic neural networks, and artificial neural networks (Doumpos, Pasiouras 2005), linear discriminant analysis and logistic regression (Spathis et al 2003;Spathis et al 2002;Voulgaris et al 2000;Zopounidis, Doumpos 1999a), discriminant analysis (Zopounidis, Doumpos 1999b), are examples of these type of studies. in almost all of these studies, the classification accuracy of UtADiS outweighs other techniques.…”