Neural networks in accounting and auditing slidecast

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Neural Networks in Accounting and Auditing

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Link to MP3

http://www.archive.org/details/NeuralNetworksSlidecastMp3

Agenda

Introduction

Background

How it Works

History

Current State

Cost Effective?

Recommendations

Conclusion

Introduction

Neural Networks not widely used in audit

Used mainly in science, and has some applications for fraud detection

Has been tested and proposed for various uses in accounting and auditing

Background

Neural networks are a type of artificial intelligence, and are based on the structure of the human brain and are composed of a large number of interconnected processors

Pattern recognition is one of the most important aspects of the neural network technology

The main advantage of artificial neural networks is that they can learn from their inputs and examples that are inputted into them can see relationships in data that will not be noticeable to human observers

How it Works

History

In 1994, neural networks were a new type of technology

In 2003, large companies began to implement neural networks

Robert Hecht-Nielsen, a professor at the University of California, San Diego, called neural networks “the most important scientific challenge of our time”

Current State of Neural Networks in Auditing

Neural networks have been used in various applications across the scientific world, but they have not seen widespread implementation in the areas of accounting and auditing

This section will examine the application of neural networks in various areas

Continuous Auditing

Continuous auditing is defined as a type of auditing which produces audit results simultaneously, or a short period of time after, the occurrence of relevant events

Internal auditors have a very prominent role in continuous auditing of a company

Training efforts should have sufficient depth

Fraud Detection

Neural networks have become the method of choice in the realm of fraud detection

For example, a study used a neural network system to identify possible areas in financial data that would lead to fraud lawsuits

The model will be able to assess this probability of fraud litigation

Accounting

Buried deep within accounting data are patterns

Accounts that traditionally are not viewed as having strong correlations with each other may in fact present substantial relationships

Auditor Decisions

Many parts of the auditing field have been subject to neural network testing

The two duties that stand out in terms of importance are the evaluation of a going concern opinion, and issuing qualified opinions in audit reports

Going Concern

Being able to forecast earnings would allow for auditors to see if a company is likely to be able to continue as a going concern, and neural networks can establish patterns to see financial viability in the future

No matter how many inputs are put into the system, and what the system generates as an output, it is still ultimately the auditor’s decision in the end

Qualified Opinions

Being able to determine predictive patterns with regards to qualified audit opinions would allow auditors to “plan specific auditing procedures to achieve an acceptable level of audit risk”

Large amounts of data could be used to focus auditors’ attention on possible problem areas that may result in a qualified opinion, and allow for a greater degree of testing and analysis with regards to these problem areas

Improvements?

Their current lack of usage by the auditing profession should be reassessed

The large amount of studies being performed over the past ten years is evidence of a growing number of academics and professionals who believe that this technology can provide great benefits to the auditing community

Cost Effective?

It will become increasingly necessary to keep up by employing neural networks to observe patterns that are beyond the ability of human auditors

Employing neural networks would also lead to a greater degree of accuracy in dangerous situations

If auditors are not able to have a tool on their side that can generate predictive data about these issues, then they may not be able to keep pace with their own profession

Recommendations

It is recommended that in the presence of evolving technology, a neural network model should be implemented into usage by auditing professionals

Using the system would likely require a full-time commitment

The neural network would have to be applied to each audit on an individual basis, so once a firm implements the necessary infrastructure for the network, individual audits can be analyzed

Conclusion

Their specific application to various issues within the auditing world, such as continuous auditing, fraud detection, auditor decision making (going concern evaluation as well as issuing qualified audit opinions) could change the entire profession

It is important for the auditing world to see that neural networks are not a replacement for the expertise and professional judgment of auditors, but simply a means of directing their attention and recognizing patterns in large amounts of data that humans would not see

Works Cited

Vasarhelyi, Miklos A., and Alexander Kogan. "Part II." Artificial Intelligence in Accounting and Auditing. Vol. 4: towards New Paradigms.Vol. 4. Princeton, NJ: Markus Wiener Pub, 1997. 64. Print.

Garrity, Edward J., Joseph B. O'Donnell, and G. Lawrence Sanders. "Continuous Auditing and Data Mining." Idea Group, 2006. Web. 25 May 2011. <2. http://www.irma-international.org/viewtitle/10596/>.

Aparaschivei, Florin. "A Conexionist Intelligent System for Accounting." Revista Informatica Economica, 2008. Web. 25 May 2011. <http://revistaie.ase.ro/content/45/11%20-%20Florin_Aparaschivei.pdf>.

Bhattacharya, Sukanto, Dongming Xu, and Kuldeep Kumar. "An ANN-based Auditor Decision Support System Using Benford's Law." Decision Support Systems 50 (2011): 576-84. Print.

Chen, H., S. Huang, and C. Kuo. "Using the Artificial Neural Network to Predict Fraud Litigation: Some Empirical Evidence from Emerging Markets." Expert Systems with Applications 36.2 (2009): 1478-484. Print.

Gaganis, C., F. Pasiouras, and M. Doumpos. "Probabilistic Neural Networks for the Identification of Qualified Audit Opinions." Expert Systems with Applications 32.1 (2007): 114-24. Print.

Works Cited

Pasiouras, F., C. Gaganis, and C. Zopounidis. "Multicriteria Decision Support Methodologies for Auditing Decisions: The Case of Qualified Audit Reports in the UK." European Journal of Operational Research 180.3 (2007): 1317-330. Print.

Kirkos, E., C. Spathis, and Y. Manolopoulos. "Data Mining Techniques for the Detection of Fraudulent Financial Statements." Expert Systems with Applications 32.4 (2007): 995-1003. Print.

Stefanou, Constantinos J. "The Complexity and the Research Area of AIS." Journal of Enterprise Information Management 19.1 (2006): 9-12. Print.

Verschoor, Curtis C. "Continuous Auditing: An Operational Model for Internal Auditors." Internal Auditing 21.2 (2006): 43-44. Print.

Warren Jr., J Donald, and L Murphy Smith. "Continuous Auditing: An Effective Tool for Internal Auditors." Internal Auditing 21.2 (2006): 27-35. Print.

Etheridge, Harlan L., Ram S. Sriram, and H. Y. Kathy Hsu. "A Comparison of Selected Artificial Neural Networks That Help Auditors Evaluate Client Financial Viability." Decision Sciences 31.2 (2000): 531-50. Print.

Works Cited

Etheridge, Harlan L., and Ram S. Sriram. "A Comparison of the Relative Costs of Financial Distress Models: Artificial Neural Networks, Logit and Multivariate Discriminant Analysis." International Journal of Intelligent Systems in Accounting, Finance & Management 6.3 (1997): 235-48. Print.

Etheridge, Harlan L., and Richard C. Brooks. "Neural Networks: A New Technology." The CPA Journal 64.3 (1994): 36+. Print.

Cerullo, Michael J., and M. Virginia Cerullo. "Using Neural Network Software as a Forensic Accounting Tool." ISACA Journal (2006): 1-5. Print.

Hoover, J. Nicholas. "ABOUT FACE; CA's Alive with New People, Products, and Practices, but Old Habits-and Impressions-die Hard." INFORMATIONWEEK (2006): 1-8. Print.

Koprowski, Gene J. "Technology News: News: Neural-Network Technology Moves into the Mainstream." TechNewsWorld: All Tech - All The Time. Web. 20 June 2011. <http://www.technewsworld.com/story/31280.html?wlc=1308456992>.

Works Cited

"Probabilistic and General Regression Neural Networks." Probabilistic and General Regression Neural Networks. Web. 20 June 2011. <http://www.dtreg.com/pnn.htm>.

"Interference and Old Data." SCHOOL OF COMPUTER SCIENCE, Carnegie Mellon. Web. 23 June 2011. <http://www.cs.cmu.edu/~schneide/tut5/node28.html>.

"What Are the Pros and Cons of Neural Networks from a Practical Perspective?" Quora. Web. 25 June 2011. <http://www.quora.com/What-are-the-pros-and-cons-of-neural-networks-from-a-practical-perspective>.