Binary choice models for external auditors decisions in Asian banks

Summarization: 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 2002–2004. 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 an out-of-sample accuracy around 60%. The highest classification accuracy is achieved by UTADIS, followed by logit analysis, nearest neighbours and discriminant analysis. Both financial variables and the environment in which banks operate appear to be important factors.Presented on: Operational Research, An International Journa

Identification of financial statement fraud in Greece by using computational intelligence techniques

The consequences of financial fraud are an issue with far-reaching for investors, lenders, regulators, corporate sectors and consumers. The range of development of new technologies such as cloud and mobile computing in recent years has compounded the problem. Manual detection which is a traditional method is not only inaccurate, expensive and time-consuming but also they are impractical for the management of big data. Auditors, financial institutions and regulators have tried to automated processes using statistical and computational methods. This paper presents comprehensive research in financial statement fraud detection by using machine learning techniques with a particular focus on computational intelligence (CI) techniques. We have collected a sample of 2469 observations since 2002 to 2015. Research gap was identified as none of the existing researchers address the association between financial statement fraud and CI-based detection algorithms and their performance, as reported in the literature. Also, the innovation of this research is that the selection of data sample is aimed to create models which will be capable of detecting the falsification in financial statements

Euro and Profitability of Greek Banks

The Greek Banking System, in its effort to prepare itself for the changeover to the EURO, will face some initial costs. Being the basic institution of money distribution, this changeover will impose a heavy burden on banks. In addition to the costs that banks will sustain, they will derive new benefits. The impact of the EURO on Greek Banks is explained through a cost-benefit analysis, by providing a perspective of the anticipated costs, benefits and outcome. The primary objective of this paper is to examine the costs that will arise from this changeover and the benefits that will be produced, as explained by the change in the bank profits. The study results consider the existence of two projects: one without the introduction to EURO and one with the introduction to EURO. We proceed through an incremental method to determine when profits will be produced. To further demonstrate this, we have calculated the NPV of the introduction to the EURO by considering the year 2002 as the basic year. The analysis shows that during the period 2002 - 2007 banks will face a loss in their bank profits. Further analysis indicates that profits will rapidly show increases in the long-term period. Therefore, the changeover to the EURO will probably be very lucrative for the banking system of Greece and the economy in general over the long-term.