Navigating Australia’s Data Landscape
Can you connect data users with the right data to answer their questions?
Go to Challenge | 20 teams have entered this challenge.
Team Name:
Govengers
Team Members:
TaxGenie
Project Info
Team Name
Govengers
Team Members
Jiya Sargun , Ho Van Nhi Nguyen , Liam Phung and 5 other members with unpublished profiles.
Project Description
Australians are often faced with confusion and stress when it comes to filing a tax return. This is mainly due to the unclear government resources and expensive accounting fees that customers need to pay. Alongside this, there is also limited awareness of deductions and superannuation benefits, and due to this reason, our team, The Govengers, have designed an app which aims to create a smarter and more personalised tax experience. This will be achieved by leveraging AI and data integration with the current ATO datasets. Some of the key features which our app includes are:
🤖Virtual AI Tax Assistant - Provides customers with 24/7 support through a chatbot that is integrated with ATO data. The integration of data will allow customers to receive personalised responses based on their queries.
🏫Education Hub with multilingual support- A simple and interactive portal of videos, infographics and additional guides, including tutorials. This will be extremely beneficial for the elderly population of Australia, as it will allow them to understand the taxation process in simple language. Alongside this, there will be another feature in which all videos and information can be accessible through multiple languages. This ensures inclusivity for migrants and non-English speakers.
📚Tax Wrapped Summary - This feature incorporates the ATO's full data summary to provide users with the services of knowing how their income is spent in specific categories.
📅 Accountant Option / Booking - TaxGenie gives users the opportunity to book meetings with nearby accountants that are affordable, have years of experience and great ratings.
Data Story
Our app, TaxGenie, incorporates real individual Income Tax Return and Superannuation datasets provided by the Australian Taxation Office (ATO). Through these datasets, the team analysed Individual tax return statistics (2010–2022) to map common deductions and income patterns, Superannuation contributions and membership data. By analysing income brackets, age groups, and contribution patterns, we identified challenges such as:
🧑🎓Younger taxpayers (18–24) often lodge their tax returns incorrectly which leads to unexpected tax debts.
👨🦱Middle-aged taxpayers (35–44) contribute the highest taxable income, but frequently miss eligible deductions.
These insights directly inform TaxGenie’s AI-powered assistant which delivers:
📝Personalised tax nudges (reminders for deductions, lodging deadlines, super contributions).
📖Tailored education modules for groups most at risk of confusion, such as first-time filers and non-English speakers.
⚙️Scenario simulations that let users test “what-if” situations (e.g., making an extra super contribution).
By grounding all insights in trusted government data and presenting it in a clear, human way, TaxGenie turns tax time from a stressful obligation into an empowering, accessible experience. It gives people context on their retirement savings, and Taxation statistics by postcode and occupation to surface trends that matter for local communities and professions.
Team DataSets
Taxation Revenue, Australia
Description of Use Data Points Used: Total taxation revenue, revenue by government level, revenue by tax type, and year-on-year changes. Implementation: TaxGenie overlays national taxation trends to give users context while filing. For example, if income tax collections rise sharply, the app nudges users to review occupation-relevant deductions and offsets. Example: Priya, a retail employee, sees: “Income tax revenue grew 7% last year. Have you checked your work-related deductions to avoid overpaying?” This links her personal filing to national trends, making tax more informed and personalised.
Table 14: Individuals – occupation by sex and taxable income
Description of Use Data Points & Columns Used: Our model cross-references the following columns: Occupation, Number of individuals (The total count of tax filers within that occupation group), Work-related clothing expenses - number (The count of individuals who claimed this deduction), Work-related car expenses - number, Work-related self-education expenses - number, (And all other [Deduction Type] - number columns). Benchmark Calculation: During the pre-analysis phase, the system calculates the Average Deduction Value for each deduction type within each occupation. Average Claim Value = Number of Individuals Claiming Deduction/Total Deduction Amount Claimed. Outlier Flagging: When a user enters a deduction amount, the app compares this value against the calculated statistical benchmark for their occupation. If the user's claim is a significant outlier (e.g., exceeds two standard deviations from the average), the app generates a non-intrusive, helpful warning. Example in Practice: A user working as a "Retail Assistant" enters a claim of $1,500 for 'Work-related clothing expenses'. Our model knows the statistical average claim for this expense in that occupation is approximately $180. Because $1,500 is a significant outlier, TaxGenie displays a message: "This claim is higher than average for your occupation. This is perfectly fine if you have the records to support it, but we recommend you double-check that your expenses meet all ATO guidelines."
Table 23: Superannuation Nudge Engine
Description of Use Data Points & Columns Used: Age range, Taxable income range, Personal superannuation contributions - amount, Personal superannuation contributions - number. Implementation Logic: The system calculates the average voluntary contribution amount for each age and income bracket. It then compares the user's voluntary contributions to this benchmark to provide either a gamified piece of positive reinforcement or a gentle nudge illustrating the potential long-term benefits of small, regular contributions. Example in Practice: A 25-year-old user earns $65,000 and has made no voluntary super contributions. The app identifies that others in their demographic contribute an average of $1,500 per year. TaxGenie presents an insight: "Adding just $30 per week to your super could boost your retirement balance by an estimated $80,000. See how small changes can make a big difference."
Table 14: Individuals – occupation by sex and taxable income range
Description of Use Data Points & Columns Used: oOr model cross-references the following columns: Occupation, Number of individuals (The total count of tax filers within that occupation group), Work-related clothing expenses - number (The count of individuals who claimed this deduction), Work-related car expenses - number, Work-related self-education expenses - number, (And all other [Deduction Type] - number columns). Implementation Logic: The engine is implemented through a two-stage process: 1. Pre-analysis: Our backend system processes the ATO data to calculate a "Claim Probability" for every deduction type within each occupation. Claim Probability (%) = (Total Number of Individuals in Occupation/Number of Individuals Claming Deduction) * 100. 2. Real-time Nudge: When a user sets up their profile and enters their occupation, the app queries our pre-analyzed model. If any deduction type has a high claim probability (e.g., > 70%) for that occupation, the AI delivers a personalized nudge to the user. Example in Practice: A user, Kenneth, enters his occupation as "Barista" (which falls under the broader 'Hospitality Workers' category in the dataset). Our model has pre-calculated that for Hospitality Workers, the Claim Probability for 'Work-related clothing expenses' is 78%. TaxGenie presents a pop-up: "Did you know? 78% of people in your occupation claimed deductions for work-specific clothing. Did you purchase any this year?" This prompts Kenneth to remember and claim a valid expense he had forgotten.
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