Addressing cybersecurity issues in Australia with open data

Project Info

Team Name

Scammer Block Plus

Team Members

Stephen , Andy , Kyle , An

Project Description


According to the latest Targeting Scams report by ACCC, Australia experienced a staggering 80% surge in monetary losses to scams in 2022, totaling slightly over AUD$3 billion. This has spurred increased collaboration between government authorities and private sector entities to enhance efforts against rapidly advancing scam techniques.

As part of 2023 GovHack One Step Ahead challenge, our team, Scammer Block Plus, is pleased to introduce our comprehensive digital solution to tackle current online scams in Australia, and prepare Australians for future trends in scams which may involve the use of advanced AI technologies such as deepfake.

Project description

The first step in building such a solution involves processing and analysing scam data sets that are publicly available. We utilise two open data sets from trusted government authorities, namely the Australian Bureau of Statistics, and the Australian Competition & Consumer Commission, in this challenge.

Based on the insights generated from these two open data sources, we developed a comprehensive digital solution named Safeguard AI Scam Scanner which utilises AI, big data, blockchain and machine learning technologies. This comprehensive digital solution also includes extensive training to educate individuals and organisations on current and future trends and issues in cybersecurity in general, and online scams to be specific.

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#opendata #scamstatistics #scamtactics #ai #socialengineering #deepfake

Data Story

Using two open data sets from ACCC and ABS to help identify characteristics of targets and victims of scams, and patterns of scams.

The insights extracted from these two open data sets help us identify:
- Who is more exposed to scams and their characteristics
- Who is more likely to fall for scams and their characteristics
- What time of the year do scams happen more frequently
- What are the most common scam delivery methods

Evidence of Work


Team DataSets

ABS Personal Fraud data set

Description of Use Analysing targets and victims of scams in Australia

Data Set

ACCC Scam Statistics

Description of Use Analysing scam delivery methods

Data Set

Challenge Entries

Staying one step ahead

How might we use open data to provide insights, identify online intervention points and/or possible digital solutions that could prevent people from falling for any type of online scam, whether that be at home or at work?

Go to Challenge | 7 teams have entered this challenge.