Our financial future
How might we use publicly available data to safeguard citizens’ financial future?
Go to Challenge | 11 teams have entered this challenge.
FinVenger - Secure Your Future!
Project Info
Team Name
Team FinVenger
Team Members
Shubham , Tarun , Anthony and 3 other members with unpublished profiles.
Project Description
FinVenger will change the way Australian youth plan their HELP debt repayment and secure their future by efficiently planning their investment strategies towards their financial goals.
From the dataset; we have observed the presence of defaulters even in the age group of 60+ years. This can be inferred as an outcome of limited financial knowledge and lack of awareness with regards to financial planning in a person’s early stages of their career.
There is a definite need for a digital platform, where by using predictive and adaptive analysis of a person’s financial history, they are able to obtain an overview of their overall financial health in terms of assets, debts, spend figures, investment potential and their own risk appetite that is derived using multiple parameters.
Our solution - FinVenger; is an omni-channel digital solution that leverages the historically available datasets and provides a detailed personalized solution that enables today’s Youth to not only invest smartly, but also see a timeline view of the projected financial figures along with the risk analyzers.
AI/ML based saving and investment features will enable the user to start repaying the debt voluntarily even before they reach the payable slab, so that the time required to repay the debt is reduced and the user becomes debt-free earlier.
We also provide access to a team of highly recommended financial mentors who are available to guide the user throughout their journey.
So, start saving - start investing and SECURE YOUR FUTURE!!!
Data Story
- 10% of outstanding HELP debtors are above the age of 50 with an average outstanding debt of approximately $13,000- (HELP Data Statistics)
- 70% of debtors have an outstanding debt in the range of $1,000-$30,000 and most of them are in the 30-40 age bracket- (HELP Data Statistics)
- Based on the average income analysis by age group; people in the age bracket of 25-30 earn $55,000, however, around 40% of HELP Debtors were beyond 30- (HELP and Tax Data Statistics)
- Based on the 2019-20 HELP repayment data; it can be observed that people still prefer compulsory repayments rather than going for voluntary payments HELP and Tax Data Statistics- (HELP and Tax Data Statistics)
- 67% of the overall population still files their taxes with the help of agents
- The age group of 25-45 has very less superannuation contribution compared to people above 50 – (Tax Data Statistics)
- On an average, only 10% of people below 40 tend to make personal super contributions- (Tax Data Statistics)
- During 2019-20, an average of $25,088 was either lost or unclaimed supers across Australia (Lost Super Data)
- Based on the Average Expenditure and Income report; a person in their 30s can save an average of almost $15,000 a year. This shows potential for more investment and data mitigation planning- (Expenditure Data)
Team DataSets
Household Expenditure Survey, Australia: Summary of Results
Description of Use This data gives us details on day-to-day person’s expense which will help application to understand person expenses in relationship with his incomes. With this application will run various AL/ML based algorithms to provide person with suitable strategies to plan your future
Tax Statistics Individuals - Table 23
Description of Use These data helped in understanding at what age people in Australia tend to put personalized contributions in the Supers
Tax Statistics Individuals - Table 7
Description of Use This data helped in understanding the geography wise income distribution and taxes.
Tax Statistics Individuals - Table 3
Description of Use This data has the information related to people income by age which helped in identifying on average how much money they earn yearly. This will be crucial part of data in the application to put person in right category based on the details shared.
Tax Statistics Individuals - Table 2
Description of Use This data helped in understanding in last year what methods people adopted to lodge their taxes.
Lost and Unclaimed Super June 2020
Description of Use This data was used to see lost and unclaimed supers. With help of this, root cause was identified for these lost like youth showing interest in early days of their careers because of which they lose track of their super accounts
Higher Education Loan Program (HELP)
Description of Use This data helped in understanding the HELP outstanding debt amount and debtors. Through this data various information relating to the age range, income range, outstanding debt range is analysed to see what segment needs more help in their finances and clear out their debts early.
Taxation Statistics 2018-19
Description of Use Based on this data, algorithms will be built which will do predictive and adaptive analysis not just considering historical data for individual, it will use data of all individual having same circumstances such as age, income bracket, job area, spending, debt and various other factors which determine person’s financial health. Based their actions and past data it will suggest suitable strategies which a person can adopt.