ACCT 771: Audit Analytics
Course Instructor:
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Course Website: Canvas
Course Description
This course prepares students to enter a rapidly changing audit environment. The course provides students with current techniques used by accounting and finance professionals to improve audit efficiency and effectiveness through data analytics and other emerging technologies. The topics covered include auditing through information systems, continuous auditing, automated audit procedures, and artificial intelligence to support judgment and decision making. Issues that impact the audit function are also covered, such as blockchain technologies, information assets, and digital currencies.
Course Objectives
- Students will learn which analytics techniques are appropriate for decision making related to the auditing, assurance, forensic accounting, and accounting advisory professions.
- Students will be exposed to how machine learning and automation are used in audit and forensic accounting to help streamline data gathering and reduce time to decision.
- Students will understand the impact of blockchain technology and digital assets on the audit and assurance profession.
- Students will be exposed to emerging audit and accounting issues or topics.
Course Materials and Fees
Readings, cases, and other instructional resources are provided by Dr. Karen and supplemented by materials prepared by the accounting firms, software companies, and other on-line resources. These materials will be described in the weekly learning modules on Canvas and are available at no charge.
Career-path specific technologies used in the course are also available at no charge except for the blockchain application which will cost $80 per student. Details for obtaining the technologies will be provided in the weekly learning modules on Canvas.
Course Schedule
A detailed schedule will be available for each weekly learning module at the beginning of each week. The weekly detailed schedule will describe pre-, in-, and post-class activities and deliverables. Pre-class activities and deliverables are due by 7:00 pm on Tuesdays. Post-class activities and deliverables are due by 11:59 pm on Saturdays.
Speaker Schedule
Many in-class meetings will feature guest lecturers who are accounting and/or financial management professionals and experts on the topics. The speaker schedule is provided below.
| Week | Speaker Topic | Guest Lecturer |
| 1 | Audit workflow management and audit technologies |
KPMG
|
| 2 | Audit technologies: EY Helix and GL Analyzer |
Ernst & Young
|
| 3 | Artificial intelligence and machine learning |
PricewaterhouseCoopers
|
| 4 | Blockchain technologies |
TrueUp.com
|
| 7 | Risk assessment and continuous monitoring |
Kearney & Company
|
Grading
The major grade components for the course are detailed below.
| Major Grade Component | Points |
| Cases | 650 |
| Quizzes | 100 |
| Capstone Competition | 150 |
| Participation | 100 |
Grades will be determined using a straight scale as follows. Any discussions regarding your grade must be done by appointment and not discussed via email. Keep in mind that the grade “A” is reserved for work of excellent quality.
| Points | Grade | Points | Grade |
| 980 - 1000 | A+ | 810 - 869 | B |
| 920 - 979 | A | 800 - 809 | B- |
| 900 - 919 | A- | 700 - 799 | C |
| 870 - 899 | B+ | Below 700 | F |
Deliverables
What you gain from this course will depend on your effort and enthusiasm in completing the course activities and deliverables and attending the in-person class lectures. You will need to be actively participating in the weekly learning modules. This includes pre-class activities designed to add to your knowledge and to prepare you for the in-person class. Instructions and schedules for the readings, quizzes, cases, and other activities will be made available throughout the module. All deliverables in this course are due by the assigned due date and will not be accepted late. Please review the deliverable instructions carefully.
Quizzes: Quizzes will be given that will let Dr. Karen know if you completed the activities assigned. There are no makeup quizzes.
Cases: There will be cases assigned throughout the course. Each case is broken into multiple parts. The due dates for the case assignments will be announced on Canvas at the beginning of the Learning Module. Late submissions will not be graded.
Participation: Five of the in-class meetings will be led by a guest lecturer who will present topics as described on the schedule. Active participation during the lecture is expected. Points will be awarded for engaging the expert with questions and participating in lecture activities. Quality of the participation will be judged by Dr. Karen and will test whether you prepared adequately for the lecture by completing the pre-class readings and other activities. A maximum of twenty points will be awarded for each of the five class meetings.
Capstone team competition: A final case will be assigned for the last class meeting. Information about the case will not be provided until the start of class. Students will first individually identify and answer questions relevant to the case’s overall objective. This will account for 40% of the overall score. Students will then be tasked with continuing and synthesizing their analysis into a team solution and presentation. This portion of the competition accounts for 60% of the overall score and will test the team’s ability to communicate its findings clearly and concisely.
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