InSolve the Insolvable

Project Info

Team Name

InSolve the Insolvable

Team Members

3 members with unpublished profiles.

Project Description

Our product is called InSOLVE.

We used machine learning and/or artificial intelligence, to predict non-compliance during personal insolvency/bankruptcy.

We trained a neural network to sort through the data that we used, and was able to create a website that can showcase basic insolvency data via database lookups, but also more technical temperature matrices using Mathematica, which has been programmed via MATLAB neural network matrices.

The Australian Financial Security Authority (AFSA) would find this information important in investigating people after they have filed for bankruptcy.

Regional communities like Rockhampton can improve their economic resilience through using Artificial Intelligence like Neural Networks to make sure that integrity is kept. While it is not illegal to go into bankruptcy itself, not complying with insolvency orders afterwards is an issue. Rockhampton and Yeppoon can improve a lot by making sure that at risk members of their community can get the extra help that they need, and the indices that we calculated can help do this ahead of time.

Try putting in your details and see if you are at risk of non-compliance in the case you ever become bankrupt?

Data Story

We found common triggers among non-compliant records, for example location, job, family status were all variables.

In the bankruptcy dataset, the criteria that we looked at was;
*Calendar Year of Insolvency
*SA3 Code of Debtor
*Sex of Debtor Code
*Debtor Occupation Code (ANZSCO)
*Cause for Bankruptcy
*Business Related Insolvency Code
*Debtor Income Level
*Primary Income Source Data
*Unsecured Debts Levels Value of Assets Levels

Using data from Australian Financial Security Authority (AFSA), we sought out to see if there were common triggers in these cases. For example, those who are living in Rockhampton who are single & without dependants are a large group of insolvencies, while those who are living in Rockhampton while being a couple and having dependants are more likely to not comply with their insolvency notices compared to others in Rockhampton.

We have compared census data from the 2016 ABS dataset which includes Rockhampton's populations with respect to age and sex.

We utilised MATLAB packages trained a neural network using our datasets, then the resultant code was implemented in Mathematica.

Evidence of Work


Project Image

Team DataSets

SA3 Census Data

Description of Use The census data obtained was used as a means to compare against the insolvency data obtained from the main dataset.

Data Set

Non-compliance in personal insolvencies

Description of Use The corresponding dataset has been utilised to provide complementary visualisations of the data in a realistic and presentable format. The data has also being processed to outline what potential indicators are evident.

Data Set

Challenge Entries

To bankruptcy or not to bankruptcy, keeping the process real.

Helping predict non-compliance in the personal insolvency system. How can Artificial Intelligence and Machine Learning assist us in the future?

Go to Challenge | 13 teams have entered this challenge.

Government Services Challenge

How might we better understand citizens' transaction preference and behaviours to make Queensland Government services easier to use?

Go to Challenge | 9 teams have entered this challenge.

Out of the Box - New take on data for regional development

Use an existing data set outside its normal context to both display and encourage innovate solutions to regional problems and promote and foster regional economic development.

Go to Challenge | 11 teams have entered this challenge.

Bounty: Is seeing truely believing?

How can we tell a story with visualisations, that speaks the truest representation of our data?

Go to Challenge | 28 teams have entered this challenge.