A roadmap for future neural networks research in auditing and risk assessment

Abstract This paper focuses on the use of neural networks (NNs) as an enabler of the new business risk auditing framework and provides insight into future research opportunities. In general, NNs, which are classifiers by nature, offer the capacity to simultaneously consider multiple types of evidence and can assist auditors in assessing risks and making judgments [Int. J. Intell. Syst. Account. Finance Manag. 2 (1993) 19; Decis. Sci. 26 (1995) 209; Eur. J. Oper. Res. 103 (1997) 350; Audit. J. Pract. Theory (1997) 14; Int. J. Intell. Syst. Account. Finance Manag. 7 (1998) 21; The use of neural networks as an audit tool in fraud risk assessment. Proceedings of American Accounting Association Northeast Regional Meeting, Rochester (NY), 1999; Int. J. Intell. Syst. Account. Finance Manag. 8 (1999) 159]. Furthermore, NNs may be superior to other approaches in circumstances where data are available in relatively large samples, the range of values to be analyzed for each case is large, and the underlying associations among the data are fuzzy and ill-defined [Decis. Support Syst. 11 (1994) 497.]. The paper reviews several published studies, which are grouped into six categories—preliminary information risk assessment (1 study), control risk assessment (2 studies), errors and fraud (6 studies), going-concern audit opinion (3 studies), financial distress (3 studies), and bankruptcy (12 studies). The paper includes a brief introduction to NNs, followed by a description and analysis of the methods employed by and findings of researchers who used NNs as a tool for research in the auditing and risk assessment domain. The literature review leads to discussion of several broad foci areas that need further exploration in order to gain a better understanding of the efficacy of NNs as an enabler of business risk-based auditing. A discussion of the general limitations of NNs as an auditing and risk assessment tool and an outline of implications for future research opportunities conclude the paper.