Why study Assurance Services?
Advanced Cases in Assurance Services explores fundamental and emerging issues in assurance services, focusing on evolving regulations, technologies, and business practices. Students will learn to assess audit risks, understand regulatory objectives, and leverage data analytics to enhance assurance and adapt to the changing landscape. This course uses case and data analysis along with discussion to cover fundamental and emerging issues in assurance services. The assurance field is constantly evolving due to changing regulation, new technologies, and changes in business practice. This means it is critical to not only understand the foundation of assurance techniques, but also be able to articulate current and potential business and audit risks, understand regulators’ objectives and adapt to the changing environment, make data-driven decisions, and identify techniques to improve assurance using data analytics and technologies.
How this course fits into the MPAcc program:
Advanced Cases in Assurance Services (ACCTG 521) is a required course in the MPAcc and concentrates on the study of the analytical mindset through data-driven case studies in audit and assurance practices. Students will also enhance their adaptability and resilience mindset by examining different assurance settings and questions, using multiple software packages, and incorporating multiple data types and quality. Students will work in teams and communicate questions and conclusions drawn from data analytics, using verbal communication, written communication, and data visualization. Students will use the questioning and innovation mindsets when identifying the underlying purpose of assurance-related services, areas for judgment, and areas of opportunity for integrating data analytics and other emerging technology. The skills and tools from Data Analytics for Professional Accountants (ACCTG 522) will provide a foundation for data exercises in this class.
Key learning objectives:
- Understand complex assurance issues, and propose and defend analytical solutions incorporating judgment.
- By undertaking data analytics such as regression analysis and t-tests.
- By examining data on various audit and regulation based data including internal control, misstatement and other data.
- Understand the benefits and issues brought by data analytics in assurance services, and how to implement them.
- By undertaking data analytics using audit specific tools.
- By conducting a simulated control walk through based on findings from data analysis.
- By undertaking an audit of cryptocurrencies.
- Understand and communicate innovative audit, compliance, and assurance-related topics.
- By undertaking risk analysis in real time using APIs.
- By undertaking an audit of cryptocurrencies
- By undertaking cybersecurity controls and compliance testing
- By understanding and undertaking Gen-AI governance related data integrity checks.
- Communicate complex assurance-related topics using supporting evidence and effective visualizations in both written documentation and verbal presentations.
- By undertaking a major project focused on using data to draw inferences about a public company in real time.
Prerequisites, required texts, materials, and software:
- Students are required to be enrolled in the MPAcc program. There are no other formal prerequisites for this course, however, students are encouraged to complete the Foster Microsoft Excel for Business online (or equivalent) course prior to starting, or during, the Autumn quarter.
- There is no required text for this course. If you wish to explore a topic in greater depth, please ask your instructor for recommendations.
- Materials outlining the required deliverables, templates, sample code/solutions, background readings and/or cases are accessed via Canvas.
- Students will be using specialized software in this course all of which will be made available on the Foster remote labs, an open-source or cloud-based setting, or from a subscription.
Required Deliverables:
Assessment in this course is focused on providing you with feedback on how well you can undertake and communicate analysis in audit cases, with an increased weight on the use of data analytics. You will be assessed on both written and verbal communication as well as the ability to effectively work in your teams and as an individual. Deliverable submission portals and grades are all maintained on the ACCTG521 Canvas page. The deliverables page provides submission links. A summary of the components of the deliverables used to determine your grade are below, detail for each assessment follows:
| Assessment | Assessment Type | Deliverables | Due Date | Grade Percentage |
|---|---|---|---|---|
| Professionalism | Individual | Polls; Verbal and Written Communication | All quarter | 50% |
| Peer Assessments | Individual | Assessed by Team Members | End of quarter | 5% |
| Client Interviews | Team | Memos and meeting | October 28 and 31 | 30% |
| Final Project Check-In Meeting: Audit Plan Appendix | Team | Meeting | 10/27 | 5% |
| MPAcc Joint Capstone Project | Team | Presentation, Report and Materials | TDB Coming Soon | 10% |
Professionalism: An individual assessment of student professionalism throughout the quarter. Students are expected to maintain a professional approach to work and approach all classes as professional engagements. Part of this grade is determined via deliverables relating to pollEverywhere engagement, written responses to cases and verbal communication in class.
Peer Assessments: An individual assessment of student professionalism undertaken by their peers throughout the quarter. Students are expected to maintain a professional approach to work and approach all team activities as professional engagements. The grade is awarded by the other members of their Assessment Team.
Client Interviews: Teams will meet with the instructor and conduct a simulated Client Meeting in class. Teams will present only to the instructor, and are required to only attend their time slot. A pre-meeting memo is due before the meeting and a post-meeting memo is due after the meeting.
Final Project Check-In Meeting: Audit Plan Appendix: Teams will meet one-on-one with the instructor.
MPAcc Joint Capstone Project: The Common Final Project which is a team based presentation focusing on the use of real-time data to support financial statement analysis for the initiation of a pairs trading strategy (one long position and one short position) for two chosen public companies. Teams select how they will narrow their analysis to two firms through the use of a screening analysis and other preliminary analysis. Teams will present in the Thursday MPAcc classes in the final week of the course. All teams are required to attend all presentations on both days. More details can be found at the MPAcc Common Final Project page.
Administrative Matters:
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Instructor: |
Asher Curtis, PhD. Herbert O. Whitten Endowed Associate Professor of Accounting. |
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Class Times: |
Tuesdays and Thursdays at 1:30PM to 3:20PM. |
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Location: |
PACCAR 394 |
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Office Hours: |
PACCAR 414 after class or by appointment; Zoom by appointment. |
Religious Accommodations:
Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.
MPAcc Policies and Further Questions:
Questions about the content of this course should first be directed to the instructor. Please see the MPAcc orientation materials for important administrative details regarding the program that apply to all courses in the MPAcc program and the UW https://registrar.washington.edu/staffandfaculty/syllabi-guidelines/ for important university policy and guidelines. If you have any additional questions, please contact the MPAcc Office (mpacc@uw.edu).
Generative AI Policy
This policy outlines expectations for the responsible and ethical use of generative AI technologies, including large language models (LLMs) such as ChatGPT, in this course. These tools can significantly enhance learning, productivity, and creativity–but must be used transparently and professionally to support a respectful and effective learning environment.
Permitted Use:
Generative AI may be used to assist with idea generation, research, document drafting, programming, editing, and other academic work, provided the output is critically reviewed, refined, and understood by the student or team. Use of AI is encouraged when it enhances the learning process.
Student Responsibility:
Students are responsible for the accuracy, relevance, and integrity of any work submitted, including content influenced or generated by AI tools. Errors introduced by generative AI–factual, analytical, or interpretive–will be treated as student errors and may result in reduced grades.
Disclosure & Ethics:
Students may be asked to disclose when and how they used generative AI tools in individual or team assignments. In cases where the use of AI significantly contributes to the submission (e.g., coding assistance, text drafting), students should include a brief statement describing the use.
Unacceptable Use:
Submitting AI–generated content without understanding it, using AI to bypass individual learning (e.g., for comprehension–based quizzes or in–class polls), or allowing AI to make up sources or misrepresent work is a violation of course expectations and academic integrity.
This policy may be updated as the role of AI in education continues to evolve.
Tentative Course Schedule
| Class | Date | Day | Topic |
|---|---|---|---|
| Class 1 | Thursday, September 25, 2025 | Thursday | Introduction to Advanced Cases in Assurance Services |
| Class 2 | Tuesday, September 30, 2025 | Tuesday | Economics of Disclosure, Audit, and Assurance |
| Class 3 | Thursday, October 2, 2025 | Thursday | Audit Risk and Materiality |
| Class 4 | Tuesday, October 7, 2025 | Tuesday | Misstatement Risk |
| Class 5 | Thursday, October 9, 2025 | Thursday | Audit Risk Conclusion and Transaction Analysis 1 |
| Class 6 | Tuesday, October 14, 2025 | Tuesday | Transaction Analysis 2 |
| Class 7 | Thursday, October 16, 2025 | Thursday | Transaction Analysis 3 (PCard Case Conclusion) |
| Class 8 | Tuesday, October 21, 2025 | Tuesday | Audit Analytics 1 |
| Class 9 | Thursday, October 23, 2025 | Thursday | Audit Analytics 2 |
| Class 10 | Tuesday, October 28, 2025 | Tuesday | Client Interviews |
| Class 11 | Thursday, October 30, 2025 | Thursday | AI Assisted Audit Analysis 1 |
| Class 12 | Tuesday, November 4, 2025 | Tuesday | AI Assisted Audit Analysis 2 |
| Class 13 | Thursday, November 6, 2025 | Thursday | Generative AI 1 |
| Class 14 | Tuesday, November 11, 2025 | Tuesday | Veterans Day (No Class) |
| Class 15 | Thursday, November 13, 2025 | Thursday | Generative AI 2 |
| Class 16 | Tuesday, November 18, 2025 | Tuesday | Cybersecurity 1 |
| Class 17 | Thursday, November 20, 2025 | Thursday | Cybersecurity 2 |
| Class 18 | Tuesday, November 25, 2025 | Tuesday | Blockchain Audits |
| Class 19 | Thursday, November 27, 2025 | Thursday | Thanksgiving Break (No Class) |
| Class 20 | Tuesday, December 2, 2025 | Tuesday | Course conclusion and Team Project Workshop Day |
| Class 21 | Thursday, December 4, 2025 | Thursday | Final Group Project Presentations |