with an unpublished profile.
Let's muck around in AFSA's insolvency data to see if we can find anything to predict non-compliance.
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.
Non-compliance in personal insolvencies
Description of Use
Primary data set used to predict non-compliance.
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?
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