Outline

Overview

This postgraduate-level unit provides students with advanced financial and technological competencies essential for today's competitive market. Through lectures, tutorials, individual assignments, and group projects, students will critically engage with AI-driven analytics methods addressing real-world financial problems. Emphasis will be placed on practical implementation, critical evaluation, and innovative problem-solving skills within asset management and financial analytics.

Unit details and rules

Academic unit	Finance
Credit points	6
Prerequisites ?	FINC5001
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Yancheng Qiu, yancheng.qiu@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Contribution	Class Participation Contribution to tutorials and lectures through discussion and activities	10%	Ongoing	Ongoing across the semester	AI allowed
Outcomes assessed:
Written work	Group Report Written report on an AI-enabled finance project with data, models, and evaluation	30%	STUVAC Due date: 07 Jun 2026 at 23:59	3,000 words	AI allowed
Outcomes assessed:
Written work	Combining Data from Multiple Sources Data integration, feature engineering, and ML evaluation using financial data	25%	Week 05 Due date: 29 Mar 2026 at 23:59	1,500–2,500 words	AI allowed
Outcomes assessed:
Written work	Portfolio Construction and Backtesting ML-based portfolio construction and walk-forward backtesting with risk evaluation	25%	Week 10 Due date: 10 May 2026 at 23:59	1,500–2,500 words	AI allowed
Outcomes assessed:
Presentation	Hedge Fund / FinTech AI Pitch Group pitch presentation on an AI-enabled finance project	10%	Week 13	10 Minutes	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

To be completed by coordinator.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

To be completed by coordinator.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to AI and Financial Data	Lecture (2 hr)
Week 02	ML Foundations for Finance	Lecture (2 hr)
Week 02	Introduction to AI and Financial Data	Tutorial (1 hr)
Week 03	Tree-Based Models	Lecture (2 hr)
Week 03	ML Foundations for Finance	Tutorial (1 hr)
Week 04	Unsupervised Learning	Lecture (2 hr)
Week 04	Tree-Based Models	Tutorial (1 hr)
Week 05	Financial Data Handling	Lecture (2 hr)
Week 05	Unsupervised Learning	Tutorial (1 hr)
Week 06	Neural Networks	Lecture (2 hr)
Week 06	Financial Data Handling	Tutorial (1 hr)
Week 07	Textual Analysis I	Lecture (2 hr)
Week 07	Neural Networks	Tutorial (1 hr)
Week 08	Textual Analysis II	Lecture (2 hr)
Week 08	Textual Analysis I	Tutorial (1 hr)
Week 09	ML-Driven Portfolio Construction I	Lecture (2 hr)
Week 09	Textual Analysis II	Tutorial (1 hr)
Week 10	ML-Driven Portfolio Construction II	Lecture (2 hr)
Week 10	ML-Driven Portfolio Construction I	Tutorial (1 hr)
Week 11	AI in Risk Management	Lecture (2 hr)
Week 11	ML-Driven Portfolio Construction II	Tutorial (1 hr)
Week 12	Group Project Workshop	Lecture (2 hr)
Week 12	AI in Risk Management	Tutorial (1 hr)
Week 13	Group Presentations	Lecture (2 hr)
Week 13	Group Presentations	Tutorial (1 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. compare major AI models and algorithms specifically tailored for financial applications
LO2. apply selected AI techniques to solve traditional financial challenges such as portfolio optimisation, default prediction, factor-based investment strategies, option pricing, fraud detection, and risk management
LO3. design and implement AI-enabled solutions in financial contexts and critically assess their effectiveness
LO4. critically evaluate AI-driven analytics methods, developing the capability to apply these techniques to innovative financial scenarios and practices.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

This unit is being offered for the first time in 2026.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

FINC6028: Artificial Intelligence in Finance

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links