Project Info

Team 42 thumbnail

Team Name

Team 42

Team Members

1 member with an unpublished profile.

Project Description

Let's muck around in AFSA's insolvency data to see if we can find anything to predict non-compliance.

Data Story

The data yielded a few interesting features after some squeezing and ringing. Males are more likely to breach their insolvency conditions than females. People who give common reasons for insolvency are less likely to breach than those who give unclear reasons or don't give reasons. Business related insolvencies yield about double the proportion of non-compliance cases. Curiously, when an applicant states doesn't state their family situation as part of the initial insolvency, they are much more likely to breach its conditions.

Evidence of Work



Project Image

Team DataSets

Non-compliance in personal insolvencies

Description of Use Primary data set used to predict non-compliance.

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.