Integrating Neurocomputing and Auditing Expertise

Abstract

Investigates the possibility of applying artificial intelligence to solve practical auditing problems faced by the public sector, namely the tax auditor of the Internal Revenue Services, when targeting firms for further investigation. Suggests that organizations which incorporate an operational artificial neural network system will raise their performance greatly. Proposes that the neural network will overcome problems faced by a direct knowledge acquisition method in building an expert system to preserve the expertise of senior auditors by the IRS in Taiwan. Provides an explanation of the neural network theory with regard to multi‐ and single‐layered neural networks. Statistics reveal the neural network performs favourably, and that three‐layer networks perform better than two‐layer neural networks. The results strongly suggest that neural networks can be used to identify firms requiring further auditing investigation, and also suggest future implications for intelligent auditing machines.

Citation

Chung‐Fern Wu, R. (1994), "Integrating Neurocomputing and Auditing Expertise", Managerial Auditing Journal, Vol. 9 No. 3, pp. 20-26. https://doi.org/10.1108/02686909410054745