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INTELLIGENT SYSTEMS INAIACCOUNTING, FINANCE AND MANAGEMENT DEVELOPMENT OPPORTUNITIES IN ACCOUNTING Intell. Sys. Acc. Fin. Mgmt. 14, 77–86 (2006) Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/isaf.277

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OPPORTUNITIES FOR ARTIFICIAL INTELLIGENCE DEVELOPMENT IN THE ACCOUNTING DOMAIN: THE CASE FOR AUDITING AMELIA A. BALDWIN,a* CAROL E. BROWNb AND BRAD S. TRINKLEc a University of Alabama in Huntsville, Huntsville, AL 35899, USA Oregon State University, College of Business, Corvallis, OR 97331-2603, USA College of Charleston, School of Business and Economics, Charleston, SC 29401, USA b

c

SUMMARY This paper reviews the nature of accounting and auditing problems and the need for application of artificial intelligence (AI) technologies to the discipline. The discussion includes current accounting issues for which new AI development should be fruitful, particularly auditing and assurance. Copyright © 2007 John Wiley & Sons, Ltd.

1.

INTRODUCTION

The domain of accounting has a history of artificial intelligence (AI) applications going back more than 25 years (Abdolmohammadi, 1987; Bailey et al., 1987; Borthick and West, 1987; Connell, 1987; Brown, 1989). Accounting researchers applied various AI technologies and techniques with some success to specific tasks in financial reporting and analysis (Lam, 2004), as well as in auditing and assurance (Hansen and Messier, 1982; Bailey et al., 1985; Dungan and Chandler, 1985; Boritz and Wensley, 1990; Murphy and Brown, 1992) and in other areas. The most welldeveloped area of AI literature in the accounting discipline involves the development and use of expert systems (Zhao et al., 2004). Unfortunately, these expert systems have not lived up to their potential (O’Leary, 2003). The expansion of research into expert systems and other AI applications for accounting tasks began in the 1980s. These applications have been proposed, studied and developed in auditing (Abdolmohammadi, 1987; Biggs, 1988; Murphy, 1990; Baldwin, 1993, 1998), taxation (McCarty, 1977; Michaelsen, 1984; Dungan and Chandler, 1985; Messier and Michaelsen, 1987; Brown, 1988; Brown and Streit, 1988), management accounting (Rice and Shim, 1988; Brown and Phillips, 1995, Sangster 1994, 1996) and financial accounting and analysis (Agarwal et al., 1997; Etheridge and Sriram, 1997; Haven, 1998). 1.1.

Audit and Assurance Tasks

Accounting tasks involve a wide range of structured, semi-structured and unstructured decisions. The heart of auditing and assurance involves the less-structured decisions and analysis that include much

* Correspondence to: A. A. Baldwin, 350 Admin. Science Bldg., Dept. of Accounting & Finance, College of Admin. Science, University of Alabama in Huntsville, Huntsville, AL 35899, USA. E-mail: [email protected] Copyright © © Copyright 2007 2007 John John Wiley Wiley & Sons,& Ltd. Sons, Ltd.

Intell. Sys. Acc. Fin. Mgmt. 14, 77–86 (2006) DOI: 10.1002/isaf

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A. A. BALDWIN ET AL.

uncertainty, caused by risks and lack of information. The reason that accounting is a very well recognized profession is largely due to its nature as a specialized domain requiring significant education, experience and expertise for which only a relative few have trained. Unsurprisingly, research on accounting, then, is typically done most successfully by accountants. Two main related areas of accounting are (i) the domain of auditing and assurance and (ii) the area of financial reporting and analysis. Both of these broad areas of accounting suffer from similar general issues of increasing risk and broad uncertainty. Additionally, in these modern days of scandals and failures, the threat of litigation is ever present. 1.2.

The Audit Environment

The current environment of audit and assurance, arguably the most visible and important part of the accounting profession, is one of uncertainty and litigation. In the wake of a deluge of high-profile audit failures with huge financial implications, government and professional bodies have been making changes in regulations, rules and training for accountants. Both the auditors and the audited are now subject to a myriad of rules and potential pitfalls that were not apparent a few years ago. In the United States, a new government oversight board now oversees the profession and Congress passed a far-reaching law, the Sarbanes–Oxley Act of 2002, that highlights the risks of auditing and creates even more audit-related tasks and issues for inquiry. The European Union’s changes to the 8th Directive are meant to provide similar changes there. In New Zealand, recent exposure drafts on quality control are meant to achieve international convergence with international auditing and assurance standards. Clearly, the pressure to provide quality audit and assurance services is high. 1.3.

Artificial Intelligence Research Needs

AI is important to the future of the accounting profession (Elliott, 1992). As information providers and risk assessors, accountants need new tools to increase the efficiency and effectiveness of their tasks, particularly in audit and assurance contexts. The research on AI in accounting has almost exclusively been undertaken by accounting researchers. The vast majority of these authors are experts on one or more areas of accounting, but they lack an educational and experience background in AI. Many have come to AI through a general background in information systems. Others simply recognize the need for AI applications in the task domain they study and have educated themselves in the AI domain for the purpose of performing that research. Some have the goal of educating other accounting researchers about a specific AI technique; see Etheridge et al. (2000). The literature on AI in accounting is almost exclusively authored by these accounting researchers. Some AI researchers who identify opportunities for research in business applications do not even mention auditing and assurance areas (e.g. Metaxiotis and Psarras, 2003). With a few possible exceptions (e.g. Best et al., 2004), a disconnect seems to exist between the application domain of auditing and assurance and the technology domain of AI. A major opportunity exists for interdisciplinary work between accounting domain specialists and AI application specialists. This sort of collaboration could vault the development of AI in accounting forward significantly. By pairing those most knowledgeable about the accounting domains that could best benefit from AI development with those most knowledgeable about the AI applications and technologies that could or should be applied to particular types of problem, the discipline could see an explosion of fruitful research and development that far surpasses the theory and prototype Copyright © 2007 John Wiley & Sons, Ltd.


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development that currently characterizes the literature. That is what this paper desires to solicit and encourage.

2.

AUDITING AND ASSURANCE

The very nature of auditing provides motivation for the use of AI. Auditing and assurance involve assessment of risk, of unstructured and semi-structured but often repetitive decisions, and of incomplete information and uncertainty. Abdolmohammadi (1991; Abdolmohammadi and Usoff, 2001) studied auditors’ perceptions of decision aids used for audit tasks. Interestingly, the results suggest that, regardless of task complexity, auditors prefer human processing to decision aids or knowledge-based systems by a very wide margin. Today, however, the very idea that human experts have limitations is no longer an untested theory. Recall the scandals of Enron, WorldCom, Tyco, Parmalat, AIG, etc. and the demise of Arthur Andersen for evidence. On the positive side, Thibodeau (2003) studied the transferability of audit task knowledge. He concluded that knowledge developed in an audit firm may be transferable across task and industry contexts. Clearly, the potential capture, transfer and sharing of audit knowledge across the firm has the potential to enhance firm performance. Decision aids used in auditing have potentially far-reaching consequences, and not just for enhancing audit effectiveness and efficiency. Dillard and Yuthas (2001) suggest a responsibility ethic for the use of audit expert systems. Lowe et al. (2002) studied how auditor use of decision aids affected jurors’ evaluation of auditor legal liability. Jurors attributed lower responsibility to auditors relying on highly reliable decision aids, even when the aid was incorrect. Apparently, developing good decisions aids may impact auditor legal liability in multiple ways. First, good decision aids may help auditors make better decisions and thus avoid legal liability that results from audit failure. Second, good decision aids may help auditors avoid responsibility for legal liability in the event of audit failure. In contrast, overreliance on decision aids could lead to auditor difficulties. Anderson et al. (2003) studied the potential for auditors to rely on decision aids versus information from clients. Auditors tended to rate explanations provided by decision aids as more sufficient than explanations provided by the client. More research on decision-aid reliance is needed. Swinney (1999) found a similar problem of overreliance, particularly when the output is negative. In a slightly different study, Murphy and Yetmar (1996) studied the effect of subordinate auditor use of an expert system on superiors’ decisions. The expert system use by subordinates did affect superiors’ beliefs (higher likelihoods), but the use by superiors did not affect their beliefs about their own conclusions. Ye and Johnson (1995) found auditors more likely to accept expert-system recommendations if explanations were provided. 2.1.

Artificial Intelligence Applications

A number of types of decision-making theory and AI technology have been applied to auditing and assurance problems. However, that application has been largely sparse and mostly only at the theoretical level. Some expert systems have been in use at public accounting firms, such as ADAPT (Gillett, 1993), Deloitte Touche’s Audit Planning Advisor, Price Waterhouse’s Planet, Arthur Andersen’s WinProcess and KPMG’s KRisk (Brown, 1991; Bell et al., 2002; Zhao et al., 2004). Most of these systems address risk assessment (Zhao et al., 2004). Copyright © 2007 John Wiley & Sons, Ltd.


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Not all applications of AI to audit problems have proven successful in the long run. In 1995, Arthur Andersen was reported to have developed a system to help in assessing the litigation risk associated with audit clients (Berton, 1995). History suggests that it was not ultimately beneficial. Conversely, AI has mostly been applied successfully only to the more, structured, programmable and repetitive tasks in which gathering human expertise is not an extreme difficulty. See, for example, the extensive literature on expert systems for audit tasks that dates from the mid 1980s (Abdolmohammadi, 1987; Gal and Steinbart, 1987; Hansen and Messier, 1987; Brown and Murphy, 1990; Denna et al., 1991; Brown and Coakley, 2000). In auditing in particular, the uncertainly issue has driven the development of new areas of research, such as Dempster–Shafer theory and belief functions. However, progress in applying intelligent systems to auditing problems has not been impressive. Therefore, this section of the paper reviews the literature and identifies auditing tasks for which working AI applications should be developed. Abdolmohammadi (1991) studied 332 tasks that auditors perform. Although the number of potential tasks is high, not all are suitable for AI application. Some are very structured and fairly routine, such as computation of inventory ratios. Others are much less structured and rely on uncertain and incomplete information, such as a going-concern determination. 2.2.

Audit Tasks

Audit tasks elicit a wide range of characteristics. Over 400 individual audit tasks have been identified. Though the study of audit decision aids has been going on for years, no systematic model identifies audit tasks for decision-aid development (Abdolmohammadi, 1991). Some of the major tasks are discussed here. Analytical review procedures. Analytical review procedures are undertaken by auditors for the purpose of obtaining audit evidence. They may use a wide variety of techniques. Koskivaara (2004) reviews the use of neural networks for these purposes. Classification. Some audit tasks are largely classification problems: Is this a collectible debt or a bad debt? Is this a legitimate transaction or a questionable one? Welch et al. (1998) studied auditor decision behaviour in a fraud setting and suggest that genetic algorithms are an appropriate approach to solving these problems. Viaene et al. (2002) tested several AI techniques to detect fraudulent insurance claims. Their results indicate that the non-linear techniques (e.g. neural networks) did not perform as well as linear techniques. The poor performance of the non-linear techniques is attributed to a lack of domainspecific data in the limited test scenario and leads to the conclusion that, if the user has the domainspecific knowledge and skills, non-linear techniques provide more flexibility in developing fraud classification models. The researchers also note that more research is needed in this area. Materiality assessments are also a type of classification. Comunale and Sexton (2005) proposed an elementary prototype fuzzy expert system approach to assess materiality as a continuous variable. Steinbart (1987) described an expert system developed to make planning-stage materiality judgments. Internal control evaluation. With the onset of Sarbanes–Oxley, the evaluation of internal controls has become even more important to audit. Meservey et al. (1986) developed a computational model of the internal control review process of one auditor and implemented it as an expert system. Changchit and Holsapple (2001) developed an expert system to support managers’ internal control evaluations and describe the managers’ reluctance to use it. Changchit and Holsapple (2004) found Copyright © 2007 John Wiley & Sons, Ltd.



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that an expert system of an auditor’s internal control knowledge is an effective and efficient means of transferring knowledge to managers. A fuzzy model is developed by de Korvin et al. (2004) to assess the exposure risk from internal control threats. Risk assessment. Many audit tasks boil down to risk assessment. Risk assessment involves pattern matching and identifying deviations or variations (Ramamoorti et al., 1999). Chiu and Scott (1994) suggested the use of neural networks to assist in risk assessment. Lin et al. (2003) evaluated an integrated fuzzy neural network for financial fraud detection and found that it outperformed most statistical models and prior artificial neural networks. Davis et al. (1997) describe a prototype system for risk assessment that combines both neural network and expert systems technology. Hwang et al. (2004) apply case-based reasoning to internal control risk assessment. Eining and Jones (1997) found that relying on an expert system enhanced auditors’ abilities to discriminate among varying levels of management fraud risk and that they were more consistent in choosing subsequent audit actions. Peters (1990) developed and implemented an expert system for assessing inherent risk during audit planning. Going-concern decisions. A going-concern uncertainty decision is given by an auditor when the client is at risk of failure or otherwise is in distress that threatens its continuance. This decision is an unstructured audit task that can benefit from the use of decision models. Often, the decision involves both qualitative judgment and quantitative analysis. Biggs et al. (1993) developed an expert system for going-concern judgments. Neural networks have also been proposed as potential alternative models (Lenard et al., 1995; Koh, 2004). Etheridge et al. (2000) compared three neural network techniques for going-concern decisions. Hybrid systems, using both statistical models and expert systems, have also been developed (Lenard et al., 2001). Lenard et al. (2000) describe a system that combines statistical, expert systems and fuzzy clustering to support going-concern decisions. Fuzzy clustering and a hybrid model are used by Lenard et al. (2000) to model auditors’ decision-making processes regarding going-concern analysis. Bankruptcy prediction. A large body of research exists investigating the use of AI in the prediction of bankruptcy. Zhang et al. (1999) develop a framework for the use of neural networks in bankruptcy prediction and thoroughly review the extant literature before 1999. McKee and Lensberg (2002) combine genetic programming and rough set theory to develop a hybrid system for predicting bankruptcy. Anandarajan et al. (2001) uses neural networks to predict bankruptcy in financially distressed firms and finds that the non-linear models are more accurate than traditional linear models. Pendharkar (2005) utilizes several AI techniques (e.g. neural networks, genetic programming and classification trees) to develop binary bankruptcy prediction models. Aggregating audit evidence. Srivastava and co-workers (Srivastava and Shafer, 1992; Dutta and Srivastava, 1993; Gillett and Srivastava, 2000) have investigated the use of belief functions and probability to aggregating audit evidence. 2.3.

Artificial Intelligence Technologies and Techniques

With all the research on audit expert systems, their use ought to be widespread now. However, they have not lived up to their potential because they have a problem with a lack of user neutrality (O’Leary, 2003). Therefore, other, more complex AI approaches need to be investigated for audit tasks. Genetic algorithms are proposed by Welch et al. (1998) as a potentially useful application for modelling auditor behaviour in fraud decisions. Lensberg et al. (2006) applied genetic programming to bankruptcy prediction. This may also be useful in going-concern decisions. Copyright © 2007 John Wiley & Sons, Ltd.


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Neural networks have been proposed as a good application for a range of audit tasks (Calderon and Cheh, 2002). Because of their ability to model non-linear relationships and to handle incomplete data, neural networks may be particularly helpful for risk assessment tasks. Ramamoorti et al. (1999) suggests their application to internal auditing will increase the ability of internal auditors to make recommendations on process control and business re-engineering. Fanning et al. (1995) uses neural networks to assess risk of management fraud. Chiu and Scott (1994) also promote the use of neural networks for risk assessment. Koh (2004) suggests the use of neural networks and data mining for going-concern predictions. Koh discovered that neural networks and decision trees are powerful tools in analysing the complex, non-linear and interactive relationships involved in going-concern analysis. Koskivarra (2000) uses neural networks to model the monthly balances for a manufacturing firm. Fuzzy systems may be particularly useful for some audit tasks because of their inherent allowance of qualitative factors. For materiality decisions, this may be much better than typical quantitative rules of thumb (Comunale and Sexton, 2005). Deshmukh et al. (1997) provides a framework for developing fuzzy systems for assessing the risk of management fraud. Their framework builds off the research on audit red flags and on fuzzy set theory. Research opportunities exist for building and testing fuzzy system developed from the Deshmukh et al. (1997) framework. Hybrid systems. Because some audit tasks involve the use of both quantitative analysis and qualitative judgment, hybrid systems may be appropriate. Lenard et al. (1998) developed a hybrid system combining a statistical model with an expert system to suppose going-concern judgments. Other audit tasks may benefit from this approach (Lenard, 2001). May et al. (1993) applied a similar approach to claims auditing at Blue Cross, in a commercial application. Davis et al. (1997) constructed a prototype hybrid expert network, combining expert systems and neural networks, for the control risk assessment task. Stefanowski and Wilk (2001) use a hybrid system of decision rules and case-based learning to classify business credit applications according to five levels of risk.

3.

CONCLUSION AND FUTURE RESEARCH

Audit tasks are numerous and complex. Most of the AI research in auditing and accounting has been done by accounting researchers and has not involved AI experts. Research on AI for these tasks will be improved if accounting researchers and AI researchers cross disciplinary lines and work together. Accounting researchers must bridge the gap between the business and accounting domains and the computer science and AI domains and begin collaborations with AI researchers to improve auditing and assurance. Furthermore, most of the AI research in auditing and accounting has involved expert system technology. Clearly, more complex AI applications can be created to solve some auditing problems more fully. AI researchers hold the key to solving some audit and assurance task issues through the use of such AI techniques as fuzzy logic, neutral networks and perhaps other areas of AI that have never before been applied in an accounting context. Audit tasks, such as analytical review procedures, materiality assessments, going-concern decisions and risk assessment, are complex and important. Performing these tasks poorly has dire consequences (e.g. Arthur Andersen). The potential for improvement through the development and use of complex AI applications, such as expert systems, genetic programming, neural networks, fuzzy systems and hybrid systems, should be investigated to the fullest extent possible. Copyright © 2007 John Wiley & Sons, Ltd.



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