ACCT605 - Mastering Accounting Information Systems

Year

Credit points

Campus offering

Prerequisites

ACCT600 Accounting for Decision Making

Teaching organisation

150 hours over a twelve-week semester or equivalent study period

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 the among 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 comprehensive 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. Students will gain an understanding of the impact of accounting information systems on business practices and also on the risks that are created by this reliance on information systems.

The aim of this unit is to provide students with an overview of the use and ways in which accounting information systems, data and artificial intelligence have changed the way in which entities function. 

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.

On successful completion of this unit, students should be able to:

LO1 - Evaluate the development of accounting technological advancements and how the role of the responsible custodian of data and producer of useful information, data protection and privacy regulations advance and support all members of society including the poor and vulnerable (GA1, GA5).

LO2 - Examine the use of artificial intelligence (AI) and machine learning (ML) for business (GA4, GA5).

LO3 - Implement ML algorithms to accounting information to predict product / business segment / organisational performance (GA5, GA10).

LO4 - Analyse and validate Blockchain, IoT and big data analytics in the business (GA5, GA8).

LO5 - Prove how technology advancements are used to enhance the efficiency and effectiveness of communication including the use of graphs, system flowcharts, data flow diagrams and dashboards and entity relationship diagrams (GA5, GA9).

LO6 - Process accounting transactions and financial statements using accounting software (GA5).

Graduate attributes

GA1 - demonstrate respect for the dignity of each individual and for human diversity 

GA4 - think critically and reflectively 

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA8 - locate, organise, analyse, synthesise and evaluate information

GA10 - utilise information and communication and other relevant technologies effectively.

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

Learning and teaching strategy and rationale

ACU’s teaching policy focuses on learning outcomes for students. Our teaching aims to engage students as active participants in the learning process while acknowledging that all learning must involve a complex interplay of active and receptive processes, constructing meaning for oneself, and learning from others. ACU promotes and facilitates learning that is autonomous and self-motivated, is characterised by the individual taking satisfaction in the mastering of content and skills and is critical, looking beneath the surface level of information for the meaning and significance of what is being studied.

The schedule of the workshop is designed in such a way that students can achieve intended learning outcomes sequentially. Teaching and learning activities will apply the experiential learning model, which encourages students to apply higher-order thinking. The unit ensures that learning activities involve real-world scenarios that assist with ‘real-world’ preparedness. The unit also uses a scaffolding technique that builds a student’s skills and prepares them for the next phase of the learning process.

This unit is structured with required upfront preparation before workshops, and most students report that they spend an average of one hour preparing before the workshop and one or more hours after the workshop practicing and revising what was covered. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for students to prepare and revise. It is up to individual students to ensure that the out-of-class study is adequate for the optimal learning outcomes and successes.

Mode of delivery: This unit is offered in different modes. These are: “Attendance” mode, “Blended” mode and “Online” mode. This unit is offered in three modes to cater to the learning needs and preferences of a range of participants and maximise effective participation for isolated and/or marginalised groups.

Attendance Mode

In a weekly attendance mode, students will require face-to-face attendance in specific physical location/s. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for students to prepare and revise.

Blended Mode

In a blended mode, students will require intermittent face-to-face attendance determined by the School. Students will have face-to-face interactions with lecturer(s) to further their achievement of the learning outcomes. This unit is structured with required upfront preparation before workshops. The online learning platforms used in this unit provide multiple forms of preparatory and practice opportunities for students to prepare and revise. 

Online Mode

In an Online mode, students are given the opportunity to attend facilitated synchronous online seminar classes with other students and participate in the construction and synthesis of knowledge, while developing their knowledge. Students are required to participate in a series of online interactive workshops which include activities, knowledge checks, discussion and interactive sessions. This approach allows flexibility for students and facilitates learning and participation for students with a preference for virtual learning.

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 are required to 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.

Each of these assessment pieces has been designed to empower students, lead to greater equity and deepen students’ skillsets by virtue of their design. They are assessments that are constructed to integrate the unit’s instruction and curriculum. If learning mode is online, Assessment will be conducted online.

Overview of assessments

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.

Gelinas, UJ, Dull, RB Wheeler, P. & Hill, MC. 2017 Accounting information systems, 11th edn, Cengage Learning, Mason, OH.

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, Summers, SL, Wood, DA. 2020 Accounting Information Systems, Global Edition15th edition, Pearson, USA.