Auditor risk assessment: Insights from the academic literature

Accounting Horizons

Volumn 20, Issue 2, 2006, Pages 157-177

  Allen, Robert D ^a   Hermanson, Dana R ^b   Kozloski, Thomas M ^c   Ramsay, Robert J ^d  

^a UNIVERSITY OF UTAH   (United States)

^b KENNESAW STATE UNIVERSITY   (United States)

^c WILFRID LAURIER UNIVERSITY   (Canada)

^d UNIVERSITY OF KENTUCKY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33751569210     PISSN: 08887993     EISSN: 15587975     Source Type: Journal    
DOI: 10.2308/acch.2006.20.2.157     Document Type: Article

References (127)

1. Abbott, L. J., S. Parker, and G. Peters. 2004. Audit committee characteristics and restatements. Auditing: A Journal of Practice & Theory 23 (1): 69-87.
2. Agoglia, C. P., T. Kida, and D. Hanno. 2003. The effects of alternative justification memos on the judgments of audit reviewees and reviewers. Journal of Accounting Research 41 (1): 33-47.
3. Albrecht, W. S., G. W. Wernz, and T. L. Williams. 1995. Fraud: Bringing Light to the Dark Side of Business. New York, NY: McGraw-Hill.
4. Altman, E. I. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23 (4): 589-609.
5. American Institute of Certified Public Accountants (AICPA). 1988. Consideration of Internal Control in a Financial Statement Audit. Statement on Auditing Standards No. 55. New York, NY: AICPA.
6. _. 1997. Consideration of Fraud in a Financial Statement Audit. Statement on Auditing Standards No. 82. New York, NY: AICPA.
7. _. 2002. Consideration of Fraud in a Financial Statement Audit. Statement on Auditing Standards No. 99. New York, NY: AICPA.
8. Anderson, B. H., and M. Maletta. 1994. Auditor attendance to negative and positive information: The effect of experience-related differences. Behavioral Research in Accounting 6: 1-20.
9. Asare, S., and A. Wright. 2004. The effectiveness of alternative risk assessment and program planning tools in a fraud setting. Contemporary Accounting Research 21 (2): 325-352.
10. Ballou, B., C. E. Barley, and J. S. Rich. 2004. The impact of strategic-positioning information on auditor judgments about business-process performance. Auditing: A Journal of Practice & Theory 23 (2): 71-88.
11. _, and D. L. Heitger. 2004. A cognitive characterization of audit analytical procedures under strategic-systems auditing. Working paper, Miami University.
12. Beasley, M. 1996. An empirical analysis of the relation between the board of director composition and financial statement fraud. The Accounting Review 71 (4): 443-465.
13. _, J. Carcello, D. Hermanson, and P. Lapides. 2000. Fraudulent financial reporting: Consideration of industry traits and corporate governance mechanisms. Accounting Horizons 14 (4): 441-455.
14. Beaulieu, P. 2001. The effects of judgments of new clients' integrity upon risk judgments, audit evidence, and fees. Auditing: A Journal of Practice & Theory 20 (2): 85-100.
15. Beaver, W. 1966. Financial ratios as predictors of failure. Journal of Accounting Research 4 (Supplement) 71-111.
16. Bedard, J., and A. M. Wright. 1994. The functionality of decision heuristics: Reliance on prior audit adjustments in evidential planning. Behavioral Research in Accounting 6 (Supplement): 62-89.
17. _, and L. Graham. 2002. The effects of decision aid orientation on risk factor identification and audit test planning. Auditing: A Journal of Practice & Theory 21 (2): 39-56.
18. _, and K. Johnstone. 2004. Auditors' assessments of and responses to earnings management risk and corporate governance risk. The Accounting Review 79 (2): 277-304.
19. _, L. Graham, and C. Jackson. 2005. Information systems risk and audit planning. International Journal of Auditing 9 (2): 147-163.
20. Bell, T., G. S. Ribar, and J. Verchio. 1990. Neural nets versus logistic regression: A comparison of each model's ability to predict commercial bank failures. Proceedings of the 1990 Deloitte & Touche/University of Kansas Symposium on Auditing Problems, 29-54.
21. _, F. Marrs, I. Solomon, and H. Thomas. 1997. Auditing Organizations Through a Strategic-Systems Lens. New York, NY: KPMG LLP.
22. _, and J. V. Carcello. 2000. A decision aid for assessing the likelihood of fraudulent financial reporting. Auditing: A Journal of Practice & Theory 18 (1): 169-184.
23. _, J. M. Bedard, K. F. Johnstone, and E. Smith. 2002. KRisk: A computerized decision aid for client acceptance and continuance risk assessments. Auditing: A Journal of Practice & Theory 21 (2): 97-103.
24. _, M. E. Peecher, and I. Solomon. 2005. The 21st Century Public Company Audit. New York, NY: KPMG LLP.
25. Bernardi, R. 1994. Fraud detection: The effect of client integrity and competence and auditor cognitive style. Auditing: A Journal of Practice & Theory 13 (Supplement): 68-84.
26. Bierstaker, J., and A. Wright. 2001. The effects of fee pressure and partner pressure on audit planning decisions. Advances in Accounting 18: 25-46.
27. _, and _. 2005. The interaction between auditors' risk perceptions and partner preferences on audit program planning. Advances in Accounting 21: 1-24.
28. Blokdijk, H., F. Drieenhuizen, D. A. Simunic, and M. T. Stein. 2003. Determinants of the mix of audit procedures: Key factors that cause auditors to change what they do. Working paper, Vrije Universiteit Amsterdam.
29. _. 2004. Tests of control in the audit risk model: Effective? efficient? International Journal of Auditing 8 (2): 185-194.
30. Bloomfield, R. 1997. Strategic dependence and the assessment of fraud risk: A laboratory study. The Accounting Review 72 (4): 517-538.
31. Boatsman, J., C. Moeckel, and B. Pel. 1997. The effects of decision consequences on auditors' reliance on decision aids in audit planning. Organizational Behavior and Human Decision Processes 71:211-247.
32. Boritz, J. E., and P. Zhang. 1999. The auditor's objectivity under negligence liability system. Auditing: A Journal of Practice & Theory 18 (Supplement): 147-165.
33. Bradbury, M. E., and P. Rouse. 2002. An application of data envelopment analysis to the evaluation of audit risk. Abacus 38 (2): 263-279.
34. Braun, R. 2000. The effect of time pressure on auditor attention to qualitative aspects of misstatements indicative of potential fraudulent financial reporting. Accounting, Organizations and Society 25 (3): 243-259.
35. Brazel, J. F., and C. P. Agoglia. 2005. An examination of auditor planning judgments in a complex AIS environment: The moderating role of auditor AIS expertise. Working paper, North Carolina State University.
36. Carcello, J., and A. Nagy. 2004. Audit firm tenure and fraudulent financial reporting. Auditing: A Journal of Practice & Theory 23 (2): 55-70.
37. Carmichael, D. R. and G. L. Holstrum. 2005. PCAOB standards-setting update. The Auditor's Report (Spring). Available at http://aaahq.org/audit/Pubs/ Audrep/05spring/item10.htm.
38. Carpenter, T. D. 2005a. Audit team brainstorming, fraud risk identification, and fraud risk assessment: Implications of SAS No. 99. Working paper, University of Georgia.
39. _. 2005b. Audit team brainstorming, partner influence, and fraud detection: Implications of SAS No. 99. Working paper, University of Georgia.
40. Cielen, A., L. Peeters, and K. Vanhoof. 2004. Bankruptcy prediction using a data envelopment analysis. European Journal of Operational Research 154 (2): 526-532.
41. Cohen, J. R., and D. M. Hanno. 2000. Auditors' consideration of corporate governance and management control philosophy in preplanning and planning judgments. Auditing: A Journal of Practice & Theory 19 (2): 133-146.
42. _, G. Krishnamoorthy, and A. Wright. 2005. The impact of corporate board focus on auditors' risk assessments and program planning decisions. Working paper, Boston College.
43. Daniel, S. 1988. Some empirical evidence about the assessment of audit risk in practice. Auditing: A Journal of Practice & Theory 7 (2): 174-181.
44. Dusenbury, R. B., and J. L. Reimers. 1996. An empirical study of belief-based and probability-based specifications of audit risk. Auditing: A Journal of Practice & Theory 15 (2): 12-28.
45. _, _, and S. W. Wheeler. 2000. The audit risk model: An empirical test for conditioned dependencies among assessed component risks. Auditing: A Journal of Practice & Theory 19 (2): 105-117.
46. Eilifsen, A., W. Knechel, and P. Wallage. 2001. Application of the business risk audit model: A field study. Accounting Horizons 15 (3): 193-207.
47. Eining, M., D. Jones, and J. Loebbecke. 1997. Reliance on decision aids: An examination of auditors' assessment of management fraud. Auditing: A Journal of Practice & Theory 16 (2): 1-19.
48. Elder, R. J., and R. D. Allen. 2003. A longitudinal field investigation of audit risk assessments and sample size decisions. The Accounting Review 78 (4): 983-1002.
49. Frydman, H., E. I. Altman, and D. Kao. 1985. Introducing recursive partitioning for financial classification: The case of financial distress. Journal of Finance 40 (1): 269-291.
50. Fukukawa, H., T. Mock, and A. Wright. 2005. Audit program plans and audit risk: A study of Japanese practice. Working paper, Nagasaki University.
51. Glover, S., J. Jiambalvo, and J. Kennedy. 2000. Analytical procedures and audit planning decisions. Auditing: A Journal of Practice & Theory 19 (2): 27-45.
52. _, D. Prawitt, J. Schultz, and M. Zimbelman. 2003. A comparison of audit planning decisions in response to increased fraud risk: Before and after SAS No. 82. Auditing: A Journal of Practice & Theory 22 (3): 237-251.
53. Graham, L., and J. Bedard. 2003. Fraud risk factors and audit planning, International Journal of Auditing 7 (1): 55-70.
54. Guess, A., T. Louwers, and J. Strawser. 2000. The role of ambiguity in auditors' determination of budgeted audit hours. Behavioral Research in Accounting 12: 119-138.
55. Hackenbrack, K. 1992. Implications of seemingly irrelevant evidence in audit judgment. Journal of Accounting Research 30 (1): 54-76.
56. _. 1993. The effect of experience with different sized clients on auditor evaluations of fraudulent financial reporting indicators. Auditing: A Journal of Practice & Theory 12 (1): 99-110.
57. Hammersley, J. S. 2004. Pattern identification and industry-specialist auditors. Working paper, University of Georgia.
58. Hansen, J., and W. Messier, Jr. 1991. Artificial neural networks: Foundations and application to a decision problem. Expert Systems With Applications 3: 135-141.
59. _, J. McDonald, W. Messier, Jr., and T. Bell. 1996. A generalized qualitative-response model and the analysis of management fraud. Management Science 42: 1022-1032.
60. Haskins, M. E., and M. W. Dirsmith. 1995. Control and inherent risk assessments in client engagements: An examination of their interdependencies. Journal of Accounting and Public Policy 14 (1): 63-83.
61. Hecht, G. 2005. Accountants' understanding of circular causality as a determinant of unintended consequence identification. Working paper, Emory University.
62. Hoffman, V., and J. Patton. 1997. Accountability, the dilution effect, and conservatism in auditors' fraud judgments. Journal of Accounting Research 35 (2): 227-237.
63. Houston, R. W. 1999. The effects of fee pressure and client risk on audit seniors' time budget decisions. Auditing: A Journal of Practice & Theory 18 (2): 70-86.
64. _, M. F. Peters, and J. H. Pratt. 1999. The audit risk model, business risk, and audit planning decisions. The Accounting Review 74 (3): 281-298.
65. Hunton, J. E., A. M. Wright, and S. Wright. 2004. Are financial auditors overconfident in their ability to assess risks associated with enterprise resource planning systems? Journal of Information Systems 18 (2): 7-28.
66. Huss, H. F., and F. Jacobs. 1991. Risk containment: Exploring auditor decisions in the engagement process. Auditing: A Journal of Practice & Theory 10 (2): 16-32.
67. Jiambalvo, J., and W. Waller. 1984. Decomposition and assessments of audit risk. Auditing: A Journal of Practice & Theory 3 (2): 80-88.
68. Johnson, P., K. Jamal, and R. Berryman. 1991. Effects of framing on auditor decisions. Organizational Behavior and Human Decision Processes 50: 75-105.
69. _, S. Grazioli, and K. Jamal. 1993. Fraud detection: Intentionality and deception in cognition. Accounting, Organizations and Society 18: 467-488.
70. Johnstone, K. 2000. Client-acceptance decisions: Simultaneous effects of client business risk, audit risk, auditor business risk, and risk adaptation. Auditing: A Journal of Practice & Theory 19 (1): 1-25.
71. _, and J. Bedard. 2001. Engagement planning, bid pricing, and client response in the market for initial attest engagements. The Accounting Review 76 (2): 199-221.
72. _, and _. 2005. Audit planning and pricing during the period surrounding passage of the Sarbanes-Oxley Act. Working paper, University of Wisconsin-Madison.
73. Kinney, W. R., Jr. 1989. Achieved audit risk and the audit outcome space. Auditing: A Journal of Practice & Theory 8 (Supplement): 67-84.
74. Knapp, C. A., and M. C. Knapp. 2001. The effects of experience and explicit fraud risk assessment in detecting fraud with analytical procedures. Accounting, Organizations and Society 26 (1): 25-37.
75. Koh, H. C., and S. S. Tan. 1999. A neural network approach to the prediction of going concern status. Accounting and Business Research 29 (3): 211-216.
76. Kopp, L. S., and E. O'Donnell. 2005. The influence of a business-process focus on category knowledge and internal control evaluation. Accounting Organizations and Society 30 (5): 423-434.
77. Kotchetova, N. 2005. Analysis of client's strategy and auditor's risk assessment. Working paper, University of Waterloo.
78. _, T. Kozloski, and W. Messier, Jr. 2005. Do performance and documentation of business process analysis influence auditors' risk assessments? Working paper, University of Waterloo.
79. Lane, W., S. Looney, and J. Wansley. 1986. An application of the Cox proportional hazards model to bank failure. Journal of Banking and Finance 10: 511-531.
80. Lea, R. B., S. J. Adams, and R. F. Boykin. 1992. Modeling of the audit risk-assessment process at the assertion level within an account balance. Auditing: A Journal of Practice & Theory 11 (Supplement): 152-179.
81. Lee, T., R. Ingram, and T. Howard. 1999. The difference between earnings and operating cash flow as an indicator of financial reporting fraud. Contemporary Accounting Research 16 (4): 749-787.
82. Lemon, W., K. Tatum, and W. Turley. 2000. Developments in the Audit Methodologies of Large Accounting Firms. Caxton Hill, Harford, U.K.: Stephen Austin & Sons Ltd.
83. Libby, R., J. T. Artman, and J. J. Willingham. 1985. Process susceptibility, control risk, and audit planning. The Accounting Review 60 (2): 212-230.
84. Lindsay, D. H., and A. Campbell. 1996. A chaos approach to bankruptcy prediction. Journal of Applied Business Research 12 (4): 1-9.
85. Loebbecke, J., M. Eining, and J. Willingham. 1989. Auditors' experience with material irregularities: Frequency, nature and detectability. Auditing: A Journal of Practice & Theory 8 (2): 1-28.
86. Low, K. 2004. The effects of industry specialization on audit risk assessments and audit-planning decisions. The Accounting Review 79 (1): 201-219.
87. Luoma, M., and E. Laitinen. 1991. Survival analysis as a tool for company failure prediction. Omega 19 (6): 673-678.
88. Marais, M., J. Patell, and M. Wolfson. 1984. The experimental design of classification models: An application of recursive partitioning and bootstrapping to commercial bank loan classifications. Journal of Accounting Research 22 (Supplement): 87-114.
89. McKee, T. E. 1998. A mathematically derived rough set model for bankruptcy prediction. In Collected Papers of the Seventh Annual Research Workshop on Artificial Intelligence and Emerging Technologies in Accounting, Auditing and Tax, edited by C. E. Brown. Sarasota, FL: Artificial Intelligence/Emerging Technologies Section of the American Accounting Association.
90. _, and T. Lensberg. 2002. Genetic programming and rough sets: A hybrid approach to bankruptcy classification. European Journal of Operational Research 138: 436-451.
91. _. 2003. Rough sets bankruptcy prediction models versus auditor signaling rates. Journal of Forecasting 22 (8): 569.
92. Messier, W. F., Jr. and J. V. Hansen. 1988. Inducing rules for expert system development: An example using default and bankruptcy data. Management Science 34 (12): 1403-1415.
93. _, and L. A. Austen. 2000. Inherent risk and control risk assessments: Evidence on the effect of pervasive and specific risk factors. Auditing: A Journal of Practice & Theory 19 (2): 119-131.
94. Mock, T. J., and A. Wright. 1993. An exploratory study of auditors' evidential planning judgments. Auditing: A Journal of Practice & Theory 12 (2): 39-61.
95. _, and _. 1999. Are audit program plans risk adjusted? Auditing: A Journal of Practice & Theory 18 (1): 55-74.
96. Nieschwietz, R., J. Schultz, Jr., and M. Zimbelman. 2000. Empirical research on external auditors' detection of financial statement fraud. Journal of Accounting Literature 19: 190-246.
97. O'Donnell, E., and J. Schultz. 2003. The influence of business-process-focused audit support software on analytical procedures judgments. Auditing: A Journal of Practice & Theory 22 (2): 265-279.
98. _, and _. 2005. The halo effect in business risk audits: Can strategic risk assessment bias auditor judgment about accounting details? The Accounting Review 80 (3): 921-939.
99. _, J. L. Bierstaker, and J. Schultz. 2005. Strategic-systems auditing: The influence of alternative task structures on auditor sensitivity to risk factors. Working paper, Arizona State University.
100. Ohlson, J. 1980. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 18 (1): 109-131.
101. O'Keefe, T., D. Simunic, and M. Stein. 1994. The production of audit services: Evidence from a major public accounting firm. Journal of Accounting Research 32 (2): 241-261.
102. Pany, K. J., and O. R. Whittington. 2001. Research implications of the Auditing Standards Board's current agenda. Accounting Horizons 15 (4): 401-411.
103. Paradi, J. C., M. Asmild, and P. C. Simak. 2004. Using DEA and worst practice DEA in credit risk evaluation. Journal of Productivity Analysis 21 (2): 153-165.
104. Phillips, F. 1999. Auditor attention to and judgments of aggressive financial reporting. Journal of Accounting Research 37 (1): 167-189.
105. Pincus, K. 1989. The efficacy of a red flags questionnaire for assessing the possibility of fraud. Accounting, Organizations and Society 14: 153-163.
106. Public Company Accounting Oversight Board (PCAOB). 2004. An Audit of Internal Control over Financial Reporting Conducted in Conjunction with an Audit of Financial Statements. Auditing Standard No. 2. Washington, D.C.: PCAOB.
107. _. 2005. Standing Advisory Group Meeting: Risk Assessment in Financial Statement Audits. Washington, D.C.: PCAOB.
108. Public Oversight Board Panel of Audit Effectiveness. 2000. The Panel on Audit Effectiveness Report and Recommendations. New York, NY: AICPA.
109. Sennetti, J. 1990. Toward a more consistent model for audit risk. Auditing: A Journal of Practice & Theory 9 (2): 103-112.
110. Shelton, S., O. R. Whittington, and D. Landsittel. 2001. Auditing firms' fraud risk assessment practices. Accounting Horizons 15 (1): 19-34.
111. Slowinski, R., R. Susmaga, and C. Zopounidis. 1999. Business failure prediction using rough sets. European Journal of Operational Research 114: 263-280.
112. Srivastava, R., and G. Shafer. 1992. Belief-function formulas for audit risk. The Accounting Review 67 (2): 249-283.
113. Strawser, J. R. 1990. Human information processing and the consistency of the audit risk model. Accounting and Business Research 21 (Winter): 67-75.
114. Summers, S., and J. Sweeney. 1998. Fraudulently misstated financial statements and insider trading: An empirical analysis. The Accounting Review 73 (1): 131-146.
115. Tam, K. Y., and M. Y. Kiang. 1992. Managerial applications of neural networks: The case of bank failure predictions. Management Science 38 (7): 926-947.
116. Turner, J. L., T. J. Mock, and D. Manry. 2004. The association of discretionary accruals with pre-audit engagement risk. Working paper, University of Memphis.
117. Waller, W. S. 1993. Auditors' assessments of inherent and control risk in field settings. The Accounting Review 68 (4): 783-803.
118. Wallin, J., and S. Sundgren. 1995. Using linear programming to predict business failure: An empirical study. Available at: http://www.shh.fi/depts/ redovis/research/jwss95/jwssma95.htm.
119. Walo, J. 1995. The effects of client characteristics on audit scope. Auditing: A Journal of Practice & Theory 14 (1): 115-124.
120. West, D., S. Dellana, and J. Qian. 2005. Neural network ensemble strategies for financial decision applications. Computers & Operations Research 32 (10): 2543-2559.
121. Wilks, J., and M. Zimbelman. 2004a. Using game theory and strategic reasoning concepts to prevent and detect fraud. Accounting Horizons 18 (3): 173-184.
122. _, and _. 2004b. Decomposition of fraud risk assessments and auditors' sensitivity to fraud cues. Contemporary Accounting Research 21 (3): 719-745.
123. Wood, L. 2005. The influence of decision aid use on auditor processing of irrelevant information in fraud risk assessment. Working paper, Virginia Polytechnic Institute and State University.
124. Wright, A., and J. Bedard. 2000. Decision processes in audit evidential planning: A multistage investigation. Auditing: A Journal of Practice & Theory 19 (1): 123-143.
125. Wright, S., and A. Wright. 1997. The effect of industry experience on hypothesis generation and audit planning decisions. Behavioral Research in Accounting 9: 273.
126. Zimbelman, M. 1997. The effects of SAS No. 82 on auditors' attention to fraud risk factors and audit planning decisions. Journal of Accounting Research 35 (Supplement): 75-97.
127. _, and W. Waller. 1999. An experimental investigation of auditor-auditee interaction under ambiguity. Journal of Accounting Research 37 (Supplement): 135-155.