Presenting a live 110 ‐ minute teleconference with interactive Q&A Sampling Strategies in Financial Statement Audits Devising a Sampling Methodology That Meets AICPA Standards and Fortifies the Auditor's Opinion WEDNESDAY, MARCH 23, 2011 1pm Eastern | 12pm Central | 11am Mountain | 10am Pacific 1pm Eastern | 12pm Central | 11am Mountain | 10am Pacific Today’s faculty features: Jeanne Yamamura, Director of Professional Services, Mark Bailey & Co. , Reno, Nev. Jeanne Yamamura Director of Professional Services Mark Bailey & Co. Reno Nev Laura Schweitzer, Director, PricewaterhouseCoopers , Washington, D.C. Lyn Graham, CPA, PhD., CFE, Bentley University , Waltham, Mass. Trevor Stewart, Senior Research Fellow, Rutgers University , New Brunswick, N.J. Trevor Stewart, Senior Research Fellow, Rutgers University , New Brunswick, N.J. For this program, attendees must listen to the audio over the telephone. Please refer to the instructions emailed to the registrant for the dial-in information. Attendees can still view the presentation slides online. If you have any questions, please contact Customer Service at1-800-926-7926 ext. 10 .
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S Sampling Strategies in Financial li St t i i Fi i l Statement Audits Seminar March 23, 2011 Lyn Graham, CPA, Bentley University Laura Schweitzer, PricewaterhouseCoopers lgrahamcpa@verizon.net laura.schweitzer@us.pwc.com Trevor Stewart, Deloitte (Retired), Jeanne Yamamura, Mark Bailey & Co. yamamura@unr.edu Rutgers University trsny@verizon.net y
Today’s Program Background On Relevant Guidance; Evolution Of Sampling Slide 7 – Slide 16 Techniques [Lyn Graham] Important Sampling Concepts Slide 17 – Slide 23 [Laura S chweit zer] The Audit Risk Model And Its Applicability Slide 24 – Slide 32 [Trevor S t ewart ] Current Sampling Priorities And Best Practices Slide 33 – Slide 49 [Trevor S t ewart , Laura S chweit zer and Jeanne Y amamura] Frequently Faced Issues [All speakers] Frequently Faced Issues [All speakers] Slide 50 Slide 52 Slide 50 – Slide 52
Lyn Graham, CPA, Bentley University BACKGROUND ON RELEVANT BACKGROUND ON RELEVANT GUIDANCE; EVOLUTION OF SAMPLING TECHNIQUES
A di Obj Audit Objective i Gather sufficient evidence to support an audit opinion that I. the financial statements are free of material misstatement the financial statements are free of material misstatement II. Seek a high assurance (or a low risk) g ( ) III. Sampling tests of controls and tests of balances and transactions are important sources of audit evidence. 8
Implications To Entities And Auditors I. Lower-risk entities require less auditor testing, and that can reduce audit costs can reduce audit costs. Entities with reliable controls can reduce audit costs; risks II. ; are “covered” by controls. III. Internal auditors’ attention to controls and financial reporting accuracy will allow external auditors to rely on their work and reduce audit costs. 9
Further Implications I. All public companies must report on the effectiveness of internal controls (SEC requirement – SOX Section 404). A. Some non-public companies also report. II. Auditors of accelerated filers also separately report. III. Tests of controls provide the support for the company assertion re: controls’ effectiveness. IV. Quality testing by entities can reduce auditor testing and reduce auditor costs. 10
R Recent Trends And Implications T d A d I li i I. More attention was given to controls after frauds and business failures such as Enron Worldcom etc failures such as Enron, Worldcom, etc. A. Many studies of fraud and misstatement Improving controls reduces business and audit risks and audit costs Improving controls reduces business and audit risks and audit costs. II II. III. Year one investment costs vs. subsequent returns from improved financial reporting processes IV. Role of the COSO framework V. Separate sampling guidance for compliance audits under OMB A-133 11
Professional Audit Sampling Standards I. Audit sampling (SAS 39, 111, 107) II. AICPA Audit Sampling Guide (2008) A. Sufficiency of sample sizes to meet audit objectives B. Determining sample sizes – tables, formulae C. Evaluating sample results and implications D. Practical application issues 12
A di Ri k M d l Audit Risk Model I. I. Audit risk (AR) is the risk that the auditor may unknowingly Audit risk (AR) is the risk that the auditor may unknowingly fail to appropriately modify his or her opinion on financial statements that are materially misstated [SAS 107, para. 2]. II II. A 5% A 5% audit risk is generally considered low-risk. dit i k i ll id d l i k AR = IR x CR x DR I. Components of audit risk (AR) A. Inherent Risk (IR) Risk Of Material A. Inherent Risk (IR) Risk Of Material Misstatement B. Control Risk (CR) C. Detection Risk (DR) –Analytics and Detail Tests 13
Audit Risk Model: Audit Strategy A di Ri k M d l A di S IR x CR = RMM x DR = AR (5%) Risk of Material Risk of Material Inherent Risk Control Risk Misstatement Detection Risk 100% 100% 100% 100% 100% 100% 100% 100% 5% 5% 5% 5% 80% 50% 50% 40% 12.5% 12.5% 50% 10% 10% 5% N/A N/A 14
di A SAS 111 Appendix A A SAS 15
How Does The Audit Risk Model Aff Affect Audit? A di ? I. Sampling procedures are used in: A. Tests of controls B. Tests of details (substantive) II. Analytical procedures are also substantive tests and are a factor (DR = AP x Substantive Details Test Risk) III III. Tests of controls do not always involve sampling. Tests of controls do not always involve sampling A. Automated controls vs. manual controls B B. Control environment assessments Competence Control environment assessments – Competence IV. Special consideration for small populations 16
Laura Schweitzer, PricewaterhouseCoopers IMPORTANT SAMPLING IMPORTANT SAMPLING CONCEPTS
Sa m p ling Term inology Sa m p ling Term inology Audit sam pling Audit sam pling • Audit sampling is the application of audit procedures to less than 100% of the population. • Audit samples can be non-statistical or statistical (probability based). • Anytime a sample is selected, there is some risk that the estimates derived from the sample do not accurately reflect the population. In p y p p statistical audit sampling, it is customary to quantify this risk using two parameters: • Risk of incorrect acceptance s o co ect accepta ce • Precision Sampling Basics March 2011 PwC 18
Sa m p ling Term inology (Cont ) Sa m p ling Term inology (Cont.) Risk of incorrect acceptance Risk of incorrect acceptance • The risk that the sample supports the conclusion that the population is not materially misstated, when in fact it is misstated (this is the confidence level in statistical terms) confidence level in statistical terms). • Typically, 90% and 95% are used. Precision • The range around the sample estimate • We typically want to be as precise as possible, within time and budget constraints. Sampling Basics March 2011 PwC 19
Sa m p ling Term inology (Cont.) Sa m p ling Term inology (Cont ) Expected m isstatem ent • The expected amount of error in the population Tolerable m isstatem ent • The amount of error in the population that is deemed acceptable to h f i h l i h i d d bl the auditor. The tolerable misstatement should include the expected misstatement and an allowance for sampling risk (precision in statistical terms) statistical terms). Sampling Basics March 2011 PwC 20
Sa m p ling Pa ra m eters Sa m p ling Pa ra m eters The selection of sam pling param eters affects the sam ple size: The selection of sam pling param eters affects the sam ple size: Precision Sample Size Confidence Sample Size Attribute Sample Size Expected Accuracy Rate y Variability Sample Size Sampling Basics March 2011 PwC 21
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