University of Technology Sydney

22789 Financial Analytics

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2025 is available in the Archives.

UTS: Business: Accounting
Credit points: 6 cp

Subject level:

Postgraduate

Result type: Grade and marks

Requisite(s): 26776 Foundations of Business Analytics OR 22804 Business Analytics Foundations
These requisites may not apply to students in certain courses. See access conditions.
Anti-requisite(s): 220789 Financial Analytics

Description

Financial Analytics focuses on the application of data analytics techniques to derive valuable insights and drive decision-making utilising financial data. This subject equips students with the skills and knowledge necessary to analyse financial and non-financial data, uncovering patterns, trends, and relationships that can inform strategic business decisions. Throughout the subject, students explore the IMPACT analytics framework as a structured approach to conducting financial analytics projects. They learn to identify relevant business questions, preprocess and analyse large datasets, apply appropriate analytical techniques, and effectively communicate insights to stakeholders. They gain hands-on experience with data analytics tools and techniques, including data visualisation and statistical analysis to tackle real-world financial analytics problems.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:
1. Explain the purpose of data analytics and how it can create value for accountants
2. Apply the IMPACT model to address common accounting issues faced by accountants
3. Demonstrate proficiency in using multiple software tools to manage data, perform test analyses, and communicate findings through text, tables and visualisations
4. Explain how data analytics can be used in accounting, auditing, managerial accounting and financial accounting to find patterns, errors, and anomalies and find insights useful to decision making
5. Formulate and apply different types of test approaches to gather insights in decision making

Contribution to the development of graduate attributes

The subject introduces students to accounting analytics, and provides a framework for data driven decision making. Students are required to formulate questions and critically analyse and interpret data availability and structures, so as to provide solutions to identified problems. In doing so, it is important that students are cognisant of multiple points of view. The learning activities are designed to reflect real-world practice and, thus, enable students to develop and apply analytical and presentation skills in complex and diverse contexts. This subject is therefore aligned with the following Graduate Attributes:

  • Intellectual rigour and innovative problem solving
  • Professional and technical competence

Teaching and learning strategies

The subject involves an understanding of Isson and Harriott’s IMPACT analytics framework (Identify the Question, Master the Data, Perform Test Plan, Address and Refine Results, Communicate Insights, and Track Outcomes). However, the subject is also practical, with a highly interactive hands-on approach.

Students work through and are assessed on several activities using data from real business enterprises. Across the entire set of lab exercises, students are expected to formulate questions, determine what data is relevant, extract, transform and analyse that data, draw conclusions and communicate their findings. By being required to formulate questions, students are encouraged to become active engaged learners. Indeed, students must master the process of questioning for the remainder of the IMPACT process to be meaningful.

Most of the work done by students will be problem-based. In-class activities are to be done collaboratively in small groups, whereby students learn from each other.

Assessment is authentic as most activities are practice-oriented and replicate tasks which would be done in a real-world set of analytics assignments (essentially simulating work integrated learning).

Pre-class work: Students are required to work through learning content presented on the UTS Learning Management System or other platforms before attending their classes.

In-class activities: The classes will enable students to engage in hands-on activities with their peers, get assistance with problems they are experiencing and engage in discussions around accounting within the context of data and technology and providing information for decision-making.

Feedback: Students will have access to feedback in various forms, including automated feedback through learning activities on the UTS LMS (Canvas), feedback from educators and peers in class, and markers/assessors on their assessment submissions.

Content (topics)

  • Introduction to Data Analytics for Accounting
  • Identifying Key Problems and Questions
  • Data Management
  • Types of Analytics: Predictive, Diagnostic,
  • Communicating Results and Visualisation
  • Enterprise Data Management and Accounting
  • Analytics Applications for Accounting

Assessment

Assessment task 1: Learning Portfolio (20% Individual and 10% Group)*

Intent:

Part A (20%) - Online quizzes (Individual)
Part B (10%) - Tutorial problem (Group)
Objective(s):

This addresses subject learning objective(s):

1, 2, 3 and 4

Weight: 30%
Length:

Varies; production and communication of analysis

Criteria:

Part A - Online quizzes (Individual)*

  • Understanding of theoretical concepts
  • Application of theoretical concepts to problems

Part B - Tutorial problem (Group)*

  • Ability to explain theoretical concepts and apply them to the problems;
  • Ability to work together in a group, convey information and engage in class discussion.

Students will receive feedback for their quizzes via Canvas and for the tutorial problems, within the tutorials and via Canvas.

*Note: Late submission of the assessment task will not be marked and awarded a mark of zero.

Students who do not attend one half of the total number of quizzes will have the weighting of that assessment added to the final examination conditional on the students submitting, receiving approval and complying with the requirements of special consideration in accordance with the UTS rules. If the composite mark for the final exam totals more than 50 percent and the student is in the final subject of their degree, the UTS rules on borderline results (range of 45-49) shall apply whereby students will be allowed to undertake a supplementary final examination. Where a student completes and passes a supplementary examination, the maximum mark awarded for the subject will be 50 Pass.

Assessment task 2: Project/Assignment (Group)

Objective(s):

This addresses subject learning objective(s):

2, 3 and 5

Weight: 20%
Length:

3,000 - 5,000 words (including tables, graphs and references)
Criteria:
  • Ability to apply relevant tools and techniques to the problem;
  • The extent to which students can develop a solution to the problem;
  • The quality of students’ written work in conveying the techniques used and the solution developed to various stakeholders.

Students will receive feedback via a marking rubric and comments.

Assessment task 3: Final Exam (Individual)

Objective(s):

This addresses subject learning objective(s):

1, 2, 3 and 4

Weight: 50%
Length:

2 hours closed book exam
Criteria:
  • Demonstrate application of knowledge and skills acquired during the subject in one or more accounting sub-domains

Minimum requirements

Students must achieve at least 50% of the subject’s total marks.

Required texts

  • Richardson, e 19), electronic copy, Data Analytics for Accounting, 3rd ed, McGraw Hill
  • Connect Online Learning Plattform (McGraw Hill)
    To purchase the McGraw Hill materials, please use the following link https://connect.mheducation.com/class/m-ossimitz-autumn-2023

Non-texts, but also required:

References

CIMA Research (2012), Improving Decision Making in Organisations – Unlocking Business Intelligence, January, New York/London (www.cgma.org).

  • Camm, Jeffrey, Cochran, James J., Fry, Michael J., Ohlmann, Jeffrey W., Anderson, David R. and Sweeney, Dennis J. (2021). Business Analytics, Cengage

  • Jaggia, Kelly, Lertwachara and Chen. (2021) Business Analytics: Communicating with Numbers, McGraw Hill

  • Richardson, V, Chang, C. and Smith, R. (2021), Accounting Information Systems, 3rd ed, New York, McGraw-Hill.

Other resources

Software

  • MS Excel incl. Analytical Tool
  • tableau (installed in the teaching lab)

Internet resources:

  • LinkedIn Learning: www.lib.uts.edu.au --> LinkedIn Learning
  • Microsoft (current version)

MAIN TEACHING LAB (CB08.02.01)

The main teaching lab in the new Dr Chau Chak Wing Building has been designed for teaching hands-on subjects such as 22574 Accounting Intelligence. It is located on the ground floor (Level 2) of Building 8.
Special lab rules apply which are displayed inside the lab. Failure to comply with those rules might result in the application of the procedures lined out in the Acceptable Use of IT - Policy and other UTS-Policies related to the use of UTS equipment.

Note that the use of mobile phones and non-UTS websites in class is unacceptable!

Other ITD-labs

Many general-purpose ITD labs at UTS can be accessed 24 hours a day (unless otherwise indicated) and MS Excel is installed in all of these labs.