BAFN311 - Emerging Technologies in Finance

Year

Credit points

Campus offering

Prerequisites

BAFN200 Principles of Finance

Unit rationale, description and aim

This unit is the foundation for understanding the growing importance of emerging technologies in finance. Emerging technologies have given firms a low-cost platform to create ‘convenient, data-intuitive product and services’. Artificial intelligence, blockchain, Internet of Things (IoT) and big data analytics are the among top ten emerging technologies for the financial services industry. Students need to understand the concepts of blockchain, IoT, and big data analytics and will be able to apply these to real-life cases. The unit aims at providing students with the necessary knowledge and skills needed to apply Blockchain, IoT and big data analytics for a financial analyst career.

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 - Demonstrate the understanding of blockchain, Internet of Things (IoT) and big data analytics in the finance industry (GA4, GA5) 

LO2 - Identify and analyse blockchain, IoT and big data analytics in the finance industry (GA5, GA6) 

LO3 - Assess the application of blockchain, IoT and big data analytics in an ethical perspective (i.e., respecting ethical principles and values) (GA3, GA5)

LO4 - Apply big data analytics using publicly available big data sources (e.g., World Bank, IMF, OECD, government departments) (GA5, GA8)

LO5 - Evaluate the blockchain, IoT and Big Data analytics and assess how the application of those technologies can contribute to the better environment and society (GA2, GA5)

Graduate attributes

GA2 - recognise their responsibility to the common good, the environment and society 

GA3 - apply ethical perspectives in informed decision making

GA4 - think critically and reflectively 

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

GA6 - solve problems in a variety of settings taking local and international perspectives into account

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

Content

Topics will include:

• history of blockchain
• blockchain: how it works?
• application of Blockchain in the finance industry (e.g., in cross-border transactions, trade finance platforms, clearing and settlements, digital identity verification and credit reporting)
• evolution of IoT
• IoT landscape
• application of IoT in the finance industry (e.g., enhanced security system, upgraded ATMs, improved credit card experiences)
• introduction to big data analytics
• application of big data analytics in finance (e.g., real-time stock market insights, customer analytics, risk management, fraud detection)
• ethical perspectives in the application of blockchain, IoT and big data analytics.

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, the constructing of 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 in turn 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, 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 you 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. This unit is structured with required upfront preparation before workshops, 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 you 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 you 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 which 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 are required to achieve an overall score of at least 50% and attempt all assessment items. Using constructive alignment, the assessment tasks are designed for students to demonstrate their achievement of each learning outcome.

Assessments are the same regardless of whether teaching mode is attendance, blended, or online. This is indicated in overview of assessment table below.

Overview of assessments

Representative texts and references

CAHILL, D., G. BAUR, D., LIU, Z. & W. YANG, J. 2020. I am a blockchain too: How does the market respond to companies’ interest in blockchain? Journal of banking & finance, 113, 105740.

CAI, C. W. 2018. Disruption of financial intermediation by FinTech: a review on crowdfunding and blockchain. Accounting and finance (Parkville), 58, 965-992.

CAI, C. W. 2021. Triple‐entry accounting with blockchain: How far have we come? Accounting and finance (Parkville), 61, 71-93.

CHAKRAVARTY, S. & SARKAR, P. 2020. An Introduction to Algorithmic Finance, Algorithmic Trading and Blockchain, Bingley, Bingley: Emerald Publishing Limited.

KOWALSKI, M., LEE, Z. W. Y. & CHAN, T. K. H. 2021. Blockchain technology and trust relationships in trade finance. Technological forecasting & social change, 166, 120641.

LYNN, T., MOONEY, J. G., ROSATI, P. & CUMMINS, M. 2019. Disrupting Finance FinTech and Strategy in the 21st Century, Cham, Cham: Springer International Publishing: Imprint: Palgrave Pivot.

PHADKE, S. 2020. FinTech Future: the digital DNA of finance, Mathura Road, Mathura Road: SAGE Publications Pvt Ltd.

XU, M., CHEN, X. & KOU, G. 2019. A systematic review of blockchain. Financial innovation (Heidelberg), 5, 1-14.