Project Info

Team Name


Team Members

2 members with unpublished profiles.

Project Description

Presolvent uses data from the Australian Financial Security Authority, in combination with ATO data to help predict when non-compliance might occur.

High-risk individuals identified by Presolvent can be given additional support and guidance about managing their finances, agreeing only to appropriate debt and insolvency agreements, so they can better meet their obligations.

Presolvent uses Machine Learning which is trained on limited datasets provided by the relevant Australian government bodies, it makes predictions of insolvency risk, but Presolvent is only a tool to be used in conjunction with human-assessed insolvency risk factors.

Data Story

We have combined a number of data sets into our application, including:
- The Australian Financial Security Authorities insolvency dataset that includes over 300,000 insolvency matters to train our machine learning prediction model
- ATO Tax return data set to normalise distinguish individual types from insolvent individual profile types.

Evidence of Work


Team DataSets

Non-compliance in personal insolvencies

Description of Use We used to train our machine learning prediction model which assesses risk of non-compliance with debt agreement.

Data Set

ATO Dataset: Gov Hack 2018

Description of Use We used to profile individual types that are solvent, compared to individual types that are insolvent in AFSA's dataset

Data Set

Insolvency and Trustee Service Australia FOI Disclosure Log

Description of Use For more data points into the insolvency prediction model.

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.

Show Us The Numbers

How can we use open finance data to turn numbers into stories?

Go to Challenge | 13 teams have entered this challenge.

Chatbots are the Future

How can we effectively engage with open data using Chatbots?

Go to Challenge | 11 teams have entered this challenge.


How can open data be used to make a social impact, contributing to the betterment of society? How can we improve prospects for children, and education, using open data? What sort of impact can be made on homelessness, mental health outcomes, or the environment, using open data?

Go to Challenge | 19 teams have entered this challenge.

The Friendly ATO

How can the ATO use artificial intelligence or machine learning to better understand and develop ways to engage with our clients?

Go to Challenge | 15 teams have entered this challenge.