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Keywords Auditing, Neural nets, Financial performance
Abstract This article gives an overview of artificial neural network (ANN) studies conducted in the auditing field. The review pays attention to application domains, data and sample sets, ANN-architectures and learning parameters. The article argues that these auditing ANN-applications could serve the analytical review (AR) process. The summary of the findings pays attention to whether authors state that ANNs have potential to improve analytical review (AR) procedures. Furthermore, the article evaluates which are the most influential contributions and which are open ends in the field. The article, makes some practical suggestions to motivate academics and practitioners to collaborate in further exploration of the potential of ANNs.
1. Introduction
The article gives an overview of artificial neural network (ANN) studies conducted in the auditing field. The development of tools for auditors is important with regard to the workload and demands of auditors. Furthermore, the recent events in the business and auditing environment have highlighted problems in the auditing process.
Auditors may need to take the necessary steps to restore public confidence in the capital market system and accounting profession that might have been shaken by the collapse of Enron and Arthur Andersen and many others. A well-known fact is that too many companies have problems with their bookkeeping. Siebel Systems, Qwest, WorldCom, and Xerox are examples of companies that have been "cooking the books". We can call the year 2002 "the horrible year" from a bookkeeping point of view. Unfortunately the year 2002 was not an exception. This manipulation is still going on. A fairly recent example is the Dutch retail trade company Royal Ahold, whose subsidiary in the USA manipulated the operating profit. There are also examples from earlier years. Two of them come from the banking world, where the UK Baring's Bank and the Japanese Daiwa's Bank lost millions of dollars because they did not have effective control systems. The third one is a Finnish multinational company, whose subsidiary in Italy overestimated the work in progress and recorded fictitious sales. Although the above examples are the tip of the iceberg, they reflect the changing nature and demands in the audit process.
A common fact is that many parties, such as shareholders, investors, creditors, tax authorities, and managers...