Entries to To bankruptcy or not to bankruptcy, keeping the process real.

Catalogue | Table

The Four Musketeers

Unleash the power of machine learning to predict the causal factors of noncompliance. The team delivered learning Algorithm model to predict the risk of noncompliance on case by case basis.

Go to Project | The Four Musketeers

Insolvency, Facts vs Spin

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 ex...

Go to Project | Altis Canberra

Insolvit

Over 110,000 people in Australia are bankrupt. Whether they share it or not, each one of us knows someone who has been affected…Life doesn’t always go to plan. So we pulled together a team of data scientists, lawyers, coders and engineers to research the problem and create a solution which we cal...

Go to Project | Team insolvit

Insolved

##Problem Statement Every year more than 30,000 Australians become insolvent, owing creditors more than 7 billion dollars. Insolvency grew 7% in the 2017-18 financial year - double the rate of bankruptcies. The reasons for insolvency are complex and varied, but impact everyone in our communi...

Go to Project | Hard Pivot

Insolvency Risk Profiler

**We built an AI-based platform that uses information about an individual to quantity their relative insolvency risk.** Their relative risk is expressed as a number which indicates how many times less/more likely than average a given individual is to become insolvent. We also used this mode...

Go to Project | Insolvency Oracle

inSOLVEncy

Using ML to identify which individuals will commit insolvency by creating a compliance risk model and visualizing the results.

Go to Project | Dmitry's Angels

DON'T PANIC!

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

Go to Project | Team 42

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 appro...

Go to Project | Presolvent

InSolve the Insolvable

Our product is called InSOLVE. We used machine learning and/or artificial intelligence, to predict non-compliance during personal insolvency/bankruptcy. We trained a neural network to sort through the data that we used, and was able to create a website that can showcase basic insolvency dat...

Go to Project | InSolve the Insolvable

Bubbles

Bubbles creates algorithmically generated, data driven narratives to pop the information and empathy bubbles that exist within today’s digital, atomised society by generating interactive, game-ified, empathy creating narratives.

Go to Project | Team TeamTeam

New age Business

An application for people on how to create or grow their small business at easy and stress free.

Go to Project | 0pt1c N3t

Machine Learning

Hi, we are the pengunz and our project encompasses three Govhack challenges relating to the implementation of machine learning algorithms into data-sets to better achieve results. Machine learning is the use of a variety of algorithms to conduct in-depth analysis of data-sets to fit specific r...

Go to Project | Pengunz

Having a go

GovHack 2018 ·       Intro and overview o   A high performing cross functional taskforce including members from Accenture, sass, and Cloudera has been assembled to aid the government in better understanding the problem of insolvency in Australia. Insolvency costs the Australian economy in e...

Go to Project | First Timers

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