Modelling Intelligent Information Systems for Auditing

Accounting reports are an important source of information for managers, investors, creditors, and financial analysts. Moreover, developments in the capital markets and in information technology have emphasised the importance of reliable accounting information. Computerised bookkeeping provides more timely and exact information than manual bookkeeping. The auditor’s task is to inspect the reliability, completeness, and accuracy of accounting records. However, auditors have to achieve an appropriate balance between effectiveness and efficiency in their control task. In practice, this means that their statements are based on aggregated data and ratios. Therefore, auditors and other interest groups would benefit from a model that enables them to monitor the financial performance of a firm based on more detailed available data than aggregated data and ratios. Researchers have tried out different statistical methods and expert systems to support in different auditing tasks for example in the analytical review process. However, these methods have not found their ways to practice to any larger extent, mainly because they have been too complicated to use, too unreliable and the cost of applying them has been too high compared to the benefits Artificial neural networks (ANNs) are a computing paradigm that can be used as a basis for building intelligent information systems. ANNs are suitable for a set of applications like classification, prediction, control, and inference. Unlike traditional expert systems, where knowledge is made explicit in the form of rules, neural networks generate their own rules by training examples. ANNs have been successfully applied to many problems also studied using statistical and management science techniques. For example, ANNs are feasible for those business applications which require the solution of very complex system of equations, recognising patterns from imperfect inputs, or adapting decisions to changing environments. Integration of neurocomputing and auditing expertise in a computer system might be an answer of auditors' paradox of time and cost when auditing firms. There are already preliminary research results on applying neural networks on detecting errors