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.

[1]  Edward I. Altman,et al.  Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) , 1994 .

[2]  Kurt Fanning,et al.  Neural Network Detection of Management Fraud Using Published Financial Data , 1998 .

[3]  Andrew D. Bailey,et al.  Risk assessment in internal auditing: a neural network approach , 1999 .

[4]  Clarence N. W. Tan,et al.  A study of using artificial neural networks to develop an early warning predictor for credit union financial distress with comparison to the probit model , 2001 .

[5]  Daniel E. O’Leary.  Using neural networks to predict corporate failure , 1998 .

[6]  Terry R. Rakes,et al.  The effect of sample size and variability of data on the comparative performance of artificial neural networks and regression , 1998, Comput. Oper. Res..

[7]  Timothy B. Bell,et al.  Neural nets or the logit model? A comparison of each model’s ability to predict commercial bank failures , 1997 .

[8]  Barry Eaglestone,et al.  An integrity constraint for database systems containing embedded static neural networks , 2001, Int. J. Intell. Syst..

[9]  Tor Guimaraes,et al.  Integrating artificial neural networks with rule-based expert systems , 1994, Decis. Support Syst..

[10]  Salvatore Sessa,et al.  Merging fuzzy logic, neural networks, and genetic computation in the design of a decision‐support system , 2000 .

[11]  R. C. Wu,et al.  Integrating Neurocomputing and Auditing Expertise , 1994 .

[12]  Gregory R. Madey,et al.  The Application of Neural Networks and a Qualitative Response Model to the Auditor's Going Concern Uncertainty Decision* , 1995 .

[13]  Desmond Fletcher,et al.  Forecasting with neural networks: An application using bankruptcy data , 1993, Inf. Manag..

[14]  Timothy Masters,et al.  Practical neural network recipes in C , 1993 .

[15]  Pamela K. Coats,et al.  Recognizing Financial Distress Patterns Using a Neural Network Tool , 1993 .

[16]  Marjorie B. Platt,et al.  Probabilistic Neural Networks in Bankruptcy Prediction , 1999 .

[17]  Balaji Rajagopalan,et al.  Financial decision support with hybrid genetic and neural based modeling tools , 1997 .

[18]  Carol E. Brown,et al.  Artificial neural networks in accounting and finance: modeling issues , 2000 .

[19]  Rajendra P. Srivastava,et al.  Detection of Management Fraud: A Neural Network Approach , 1995 .

[20]  Murugan Anandarajan,et al.  Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks , 2001, Intell. Syst. Account. Finance Manag..

[21]  Kevin N. Gurney,et al.  An introduction to neural networks , 2018 .

[22]  W. Robert Knechel,et al.  Auditing: Assurance and Risk , 2000 .

[23]  E. Mine Cinar,et al.  Neural Networks: A New Tool for Predicting Thrift Failures , 1992 .

[24]  Ram S. Sriram,et al.  A comparison of the relative costs of financial distress models: artificial neural networks, logit and multivariate discriminant analysis , 1997 .

[25]  Bharat A. Jain,et al.  Performance Evaluation of Neural Network Decision Models , 1997, J. Manag. Inf. Syst..

[26]  Ramesh Sharda,et al.  A neural network model for bankruptcy prediction , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[27]  Sharon Kay Heatley,et al.  Data mining computer audit logs to detect computer misuse , 1998 .

[28]  James V. Hansen,et al.  Artificial Intelligence and Generalized Qualitative‐Response Models: An Empirical Test on Two Audit Decision‐Making Domains , 1992 .

[29]  Barbara A. Osyk,et al.  INTEGRATING EXPERT SYSTEMS AND NEURAL NETS Exploring the Boundaries of AI , 1995 .

[30]  Ramesh Sharda,et al.  Bankruptcy prediction using neural networks , 1994, Decis. Support Syst..

[31]  Ran Barniv,et al.  Predicting the outcome following bankruptcy filing: a three-state classification using neural networks , 1997, Intell. Syst. Account. Finance Manag..

[32]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[33]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[34]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[35]  Melody Y. Kiang,et al.  Managerial Applications of Neural Networks: The Case of Bank Failure Predictions , 1992 .

[36]  Carol E. Brown,et al.  Artificial neural networks applied to ratio analysis in the analytical review process , 1993 .

[37]  B. Green,et al.  Assessing the risk of management fraud through neural network technology , 1997 .

[38]  Anne P. Massey,et al.  Supporting a complex audit judgment task: An expert network approach , 1997 .