MA630 - Accounting Artificial Intelligence

Credit points: 15 credit points

Workload: 10 hours

Prerequisite: None

Co-requisite: N/A

Aims & objectives

This is an elective unit for the Master of Professional Accounting. This unit exposes students to the latest approaches for identifying and proposing artificial intelligence approaches to solving accounting data analysis problems. It considers automation and visualisation processes that augment human decision-making, the risks ethics and policy implications of applying this technology and a system thinking approach for identifying appropriate AI approaches for analysing accounting data and reporting. Course Learning Outcomes and further information relating to the Master of Business Research are located at the website: http://www.mit.edu.au/study-with-us/programs.

This unit will cover the following topics:

  • Machine learning, data mining and visualization for accounting processes
  • Natural Language analysis of accounting data to support decision making
  • Automation of accounting decision-making
  • Developing AI solutions for accounting business problems using systems thinking
  • Future trends in AI and ethics and risk management

Learning outcomes

Course Learning Outcomes
The Course Learning Outcomes applicable to this unit are listed on the Melbourne Institute of Technology’s website: http://www.mit.edu.au/study-with-us/programs/(once the course is approved).

Unit Learning Outcomes
At the completion of this unit students should be able to:
a. Evaluate the functional appropriateness of AI solutions for accounting business processes.
b. Identify the required outcomes and stakeholder needs and select the most appropriate AI applications.
c. Explain the benefits, risks and scope of artificial intelligence decision-making options for different accounting functions.
d. Appraise and select techniques for artificial intelligence accounting applications for specific organisational contexts.

Assessment

Assessment Task Due Date Release Date Weighting Unit Learning Outcomes
1. Formative assessment Week 2 Week 1 6% a
2. Individual emergent data intelligence technology project report [2500 words] Week 3 Week 1 24% a-c
3. Group data intelligence application project [4000 words] Week 4 Week 1 40% b-d
4. Individual data intelligence application project presentation [2000 words equivalent] Week 5 Week 1 30% a,c,d
TOTALS     100%  

Task Type: Type A: unsupervised, Type B: supervised.

Teaching Methods

NOTE: All School of Business units 3-hour workshops Flipped Classroom Mode.

Textbook and reference materials

Note: Students are required to access prescribed textbooks through the MIT library O’Reilly textbook subscription.

Prescribed Textbook

  • Naqvi, A. (2020) Artificial Intelligence for Audit, Forensic Accounting, and Valuation, Wiley (in O’Reilly) Rothman, D. (2018) Artificial Intelligence By Example, Packt: Birmingham (in O’Reilly).

Journals and Business publications

  • Bakarich, K. M., & O'Brien, P. E. (2021). The Robots are Coming ... But Aren't Here Yet: The Use of Artificial Intelligence Technologies in the Public Accounting Profession. Journal of Emerging Technologies in Accounting, 18(1).
  • Gotthardt, M. A. X., KoıV ulaakso, D. A. N., Paksoy, O., Saramo, C. U., Martikainen, M., & Lehner, O. M.(2019). Current State and Challenges in the Implementation of Robotic Process Automation and Artificial Intelligence in Accounting and Auditing. ACRN Oxford Journal of Finance & Risk Perspectives, 8.
  • Losbichler, H., & Lehner, O. M. (2021). Limits of artificial intelligence in controlling and the ways forward: a call for future accounting research. Journal of Applied Accounting Research, 22(2).

Adopted Reference Style: APA can be found in MIT library referencing

Graduate attributes

MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:

Ability to communicate Independent and lifelong learning Ethics Analytical and problem solving Cultural and global awareness Team work Specialist knowledge of a field of study

Legend

Levels of attainment Extent covered
The attribute is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations.
The attribute is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations.
The attribute is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations.
The attribute is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials.
The attribute is not considered, there is no theory or practice or activities associated with this attribute.