Team Name:
Presolvent
Team Members:
Presolvent
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.
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.
Description of Use We used to train our machine learning prediction model which assesses risk of non-compliance with debt agreement.
Description of Use We used to profile individual types that are solvent, compared to individual types that are insolvent in AFSA's dataset
Description of Use For more data points into the insolvency prediction model.
Eligibility: Must use this dataset: https://data.gov.au/dataset/non-compliance-personal-insolvencies
Go to Challenge | 13 teams have entered this challenge.
Go to Challenge | 13 teams have entered this challenge.
Go to Challenge | 11 teams have entered this challenge.
Go to Challenge | 19 teams have entered this challenge.
Go to Challenge | 15 teams have entered this challenge.
