Unit rationale, description and aim

Accounting information systems (AIS), big data analytics and artificial intelligence (AI) play essential roles in today’s business. Artificial intelligence, blockchain, Internet of Things (IoT) and big data analytics are among the top ten emerging technologies in accounting. These emerging technologies have given firms a low-cost platform to create convenient, data-intuitive product and services including AI. Accounting information systems allow for smart accounting utilised by a wide variety of businesses. The unit takes an extensive view of accounting information systems, data analytics and the application of artificial intelligence (AI) and machine learning (ML) that emphasise the accountants’ roles in the use, management, design, and evaluation of systems. This unit provides students with the skills to use accounting software for financial transactions as well as how to apply AI, ML, Blockchain, IoT and big data analytics to real-life cases. This unit provides students with a variety of technological skills to advance all members of the society including the poor and vulnerable. The unit is built on accounting foundation units.

The aim of this unit is to ensure that students will benefit from knowing about information technology and information systems relevant to accounting for a successful accounting career.

2026 10

Campus offering

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  • Term Mode
  • Semester 1Campus Attendance
  • Term Mode
  • Semester 1Campus Attendance
  • Term Mode
  • Semester 1Campus Attendance
  • Term Mode
  • ACU Term 3Online Unscheduled
  • Term Mode
  • Semester 1Campus Attendance

Prerequisites

ACCT100 - Introduction to Accounting OR ACCT210 Accounting Foundations

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

Examine how advances in accounting technology, alo...

Learning Outcome 01

Examine how advances in accounting technology, along with data protection and privacy rules, help ensure responsible data use and support everyone in society, including the poor and vulnerable.
Relevant Graduate Capabilities: GC1, GC6

Analyse the use of artificial intelligence (AI) an...

Learning Outcome 02

Analyse the use of artificial intelligence (AI) and machine learning (ML) for business
Relevant Graduate Capabilities: GC2, GC8

Apply ML algorithms to accounting information to p...

Learning Outcome 03

Apply ML algorithms to accounting information to predict product / business segment / organisational performance
Relevant Graduate Capabilities: GC2, GC10

Apply emerging technologies such as Blockchain, Io...

Learning Outcome 04

Apply emerging technologies such as Blockchain, IoT or big data analytics in business decision-making
Relevant Graduate Capabilities: GC2

Evaluate how data visualisation tools enhance comm...

Learning Outcome 05

Evaluate how data visualisation tools enhance communication in accounting contexts
Relevant Graduate Capabilities: GC2, GC9

Generate accounting transactions and financial sta...

Learning Outcome 06

Generate accounting transactions and financial statements using accounting software
Relevant Graduate Capabilities: GC2

Content

Topics will include:

  • accounting technological advancements
  • Artificial Intelligence
  • Machine Learning and its algorithms including decision trees, random forests, neural networks. bagging and boosting ensemble techniques
  • Blockchain technologies and application
  • Internet of Things (IoT) evolution, landscape and application
  • Big data analytics
  • accounting information systems and business process
  • Accounting software
  • Using accounting software for business transactions and reporting

Assessment strategy and rationale

Assessments are used primarily to foster learning. ACU adopts a constructivist approach to learning that seeks alignment between the fundamental purpose of each unit, the learning outcomes, teaching and learning strategy, assessment and the learning environment. In order to pass this unit, students must demonstrate competence in all learning outcomes and achieve an overall score of at least 50% . Using constructive alignment, the assessment tasks are designed for students to demonstrate their achievement of each learning outcome. If learning mode is online, Assessment will be conducted online.

Students must comply with the university’s Student Academic Integrity and Misconduct Policy. This includes avoiding unauthorised or undisclosed use of artificial intelligence, such as using generative AI, paraphrasing tools, or translation software, unless explicitly authorised in the assessment requirements and properly acknowledged. Breaches of academic integrity will be addressed in accordance with university procedures.

Overview of assessments

Overview of Assessments

Assessment Task 1: Analytical Report This task req...

Assessment Task 1: Analytical Report

This task requires students to write an analytical report how the application of artificial intelligence and machine learning influencing the accounting profession in the context of data protection and privacy regulations.

Submission Type: Individual

Assessment Method: Analytical Report

Weighting

30%

Learning Outcomes LO1, LO2
Graduate Capabilities GC1, GC2, GC6, GC8

Assessment Task 2: Practical Accounting Software ...

Assessment Task 2: Practical Accounting Software application

Students are required to demonstrate effective use of accounting software to record accounting transactions and prepare financial statements.

Submission Type: Individual

Assessment Method: Employment of cloud-based Accounting software

Artefact: Accounting software

Weighting

30%

Learning Outcomes LO5, LO6
Graduate Capabilities GC2, GC9

Assessment Task 3: Final Exam This task requires...

Assessment Task 3: Final Exam

This task requires students to analyse Blockchain and IoT and to apply Machine Learning and Big Data analytical tools. Students also utilise relevant communications tools to enhance the efficiency and effectiveness of their output.

Submission Type: Individual

Assessment method: Laboratory exam

Artefact: Technological and communication output (equivalent 2000 words).

Weighting

40%

Learning Outcomes LO3, LO4, LO5
Graduate Capabilities GC2, GC9, GC10

Learning and teaching strategy and rationale

ACU’s teaching approach focuses on achieving learning outcomes by engaging students as active participants in constructing knowledge. Learning involves both independent inquiry and collaboration with others, allowing students to critically engage with material, apply higher-order thinking, and develop real-world problem-solving skills. The unit follows an experiential learning model, encouraging students to apply accounting concepts in practical scenarios. Learning activities are scaffolded, building skills progressively to support student development and professional readiness.

Mode of Delivery:

The unit is offered in three modes—Attendance, Multi, and ACU Online—to accommodate diverse learning needs and increase accessibility.

Attendance Mode: It is delivered in a flipped classroom format with face-to-face lectures and workshops. Students complete preparatory work before attending, followed by revision and practice. Online resources supplement in-class learning through interactive content.

Multi-Mode: It combines online learning with scheduled face-to-face workshops. Students engage with recorded lectures, complete online tasks, and apply knowledge during in-person sessions.

ACU Online: It is delivered fully online through asynchronous learning. Students explore content independently, contribute to online discussions, and apply learning to real-world situations. Ongoing feedback supports their progress and achievement of learning outcomes.

Representative texts and references

Beutel, J., List, S. & Von Schweinitz, G. 2019. Does machine learning help us predict banking crises? Journal of financial stability, 45, 100693.

Carmona, P., Climent, F. & momparler, A. 2019. Predicting failure in the U.S. banking sector: An extreme gradient boosting approach. International review of economics & finance, 61, 304-323.

Guida, T. 2019. Big Data and Machine Learning in Quantitative Investment, Newark, UK, John Wiley & Sons, Incorporated. Available from: ProQuest Ebook Central.

Hull, J. 2020. Machine learning and finance. Journal of Risk Management in Financial Institutions, 13, 104-105.

Jagtiani, J. & Lemieux, C. 2019. The roles of alternative data and machine learning in fintech lending: Evidence from the LendingClub consumer platform. Financial management, 48, 1009-1029.

Lopez De Prado, M. 2018. Advances in Financial Machine Learning, Newark, USA, John Wiley & Sons, Incorporated.

Parkes A, Considine B, Olesen K & Blount Y 2018, Accounting Information Systems, 5th edn, John Wiley & Sons, Australia, Milton, Qld. ISBN: 978-0-730-36913-4 

Polyzos, S., Samitas, A. & Katsaiti, M.-S. 2020. Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability. International review of financial analysis, 72.

Romney MB., Steinbart, PJ., 2020, Accounting Information Systems, Global Edition, 15th edn, Pearson, USA. ISBN 9781292353364.

Russell, S & Norvig, P 2017, Artificial Intelligence: a modern approach, 3rd edn, Pearson Education

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