Insolvency, Facts vs Spin

Team Name

Altis Canberra

Team Members

Project Description

We trained random forest and neural network classification models to do predictive modelling on cases of insolvency non-compliance. From these models were also able to extract useful indicators for potential non-compliance. We then combined the AFSA insolvency data set with 7 other datasets to explore a range of potential additional correlations.

Data Story

Beginning this project, we found that it was a relatively easy task to simply mash together a few datasets and generate visualisations that suggest one thing or another. However, we realised there wasn’t necessarily an immediate basis for these claims, so we took a step back and decided to take a more scientific approach.
By leveraging random forests and a deep learning algorithm based on a triple layered neural network, we were able to train our system to recognise the key correlating factors that contributed toward non-compliance prediction and potential causation factors for personal insolvency.
Only once we found these correlated contributing factors, we chose linking fields associated with these factors to find relationships. We are not claiming to have found perfect causation factors for non-compliance or insolvency, but we have built a robust system to identify where potential causality could lie in order to assist with identifying venues for further research.