ACCTG 522 Assessment

Final Project Check-In Meeting

Team Deliverable (10%)

Assessment Overview

Teams will meet one-on-one with the instructor to discuss their proposed use of data analytics to enhance Financial Statement Analysis.

Required Deliverables

Deliverable Due Date Canvas Submission Portal
Project Update Memo (optional) 10/27 Upload to Canvas (one submission per team)

Deliverable Details and Hints

Further details are provided below for each required deliverable.

Required deliverable: A 10 minute meeting with the instructor

  • The goal of the meeting is to obtain feedback from the instructor on the proposed use(s) of data to enhance a financial statement analysis, and clarification on any other questions the teams have at this preliminary stage. This is expected to result in a more effective and efficient use of time and resources for the final project.
  • This project will be graded as complete/incomplete. As long as the proposed use of data meets the minimum expectations for the MPAcc Joint Final Project, the meeting will be considered as complete for grading purposes. The minimum requirements are that teams will enhance their financial statement analysis with at least one data-driven analysis.
  • All projects graded as complete will be awarded 75% (7.5/10). All teams will have the opportunity to earn up to an additional 25% (2.5/10) based on the quality and ambition of the proposed use of data analytics, which will be assessed as completed and/or at the time of the final project, as these can change significantly over the course of the project.
  • An optional memo one submission per team documenting the proposed direction of the Common Final MPAcc Project. This memo is optional, but is useful to help document the Q&A undertaken in the meeting and any additional comments or questions. Satisfactory progress on the project as determined at the meeting will have students awarded 7.5/10 (complete/incomplete grading) adding this memo will help in the assessment of potential awards of the remaining 2.5/10.

  • The meeting should cover the first thoughts of the team on how they will enhance their FSA using data analytics. These proposed ideas do not need to be started, but a plan of what data is required for these ides should be written down in a bullet point style list. Teams will not be expected to stick to these proposals and can scale back or scale up their use of data in the project over time.
  • Teams should also consider using this meeting time to resolve any questions relating to expected data extraction and analysis plans, including any analysis or extraction that may be new to the team.
  • The possibility of using data analytics to enhance the financial statement analysis is broad, and teams should consider a wide variety of potential uses of data analytics, including relating to information collection and quality (screening, custom ratio analysis), descriptive analytics (visualizations), predictive analytics (forecasts), and scenario analysis related tasks to the valuation model inputs and/or forecasts.
  • There is no correct set of enhancements that teams can propose, there is also no limit to the scope or data used, other than the equity requirement that the data is publicly available or available via subscription to all UW students. The goal of this meeting is to ensure that teams are on the right track to successfully complete the final project within their time limitations by obtaining feedback on the feasibility of the proposals.

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.