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Use data to counter fraud

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Use data to counter fraud

Data is becoming increasingly important in the fight against fraud. In the modern world, data is a huge asset that underpins the vast majority of our interactions.

Data can be used for a variety of purposes, such as helping governments deliver more effective and efficient services to its citizens. It can also help governments protect taxpayer’s money from fraud and safeguard the essential services Australians rely on.

Australian Government entities can use data in a number of ways to improve the integrity of public services, including:

  • streamlining application processes and indicating where information provided may not be accurate
  • for assurance or compliance activities, such as by reviewing anomalies in the application, or post application behaviour
  • sharing it with other entities to find fraud across government programs or improve intelligence and suitability assessments.

Data analytics

Our Fraud Data Analytics Leading Practice Guide provides a framework and principles for implementing leading practice fraud data analytics. This includes:

  • How to embed fraud analytics into organisational processes
  • How to build a Fraud Operating Model that makes effective use of fraud data analytics
  • How data analytics can enable a Fraud Operating Model
  • How to implement a Fraud Data Analytics Capability

Fraud Data Analytics enables more than just better fraud detection. It also enables:

  • Detecting indicators of fraud
  • Population risk assessment and analysis
  • Defining new indicators of fraud
  • Management information and decision support

To complement the Fraud Data Analytics Leading Practice Guide, hawse have also developed a Fraud Data Analytics Catalogue of Techniques. It provides helpful direction on the types of analytics techniques to explore, and some examples of when and how to deploy them.

This catalogue is a working document that we will continue to develop over time with input from fraud analytics teams here in Australia and overseas.

Data sharing

It is critical that we look to share data across the Australian Government and with other sectors, as criminals, scammers and fraudsters look to exploit and take advantage of vulnerable Australians across multiple programs. By exploring and taking full advantage of opportunities to share and match data, entities will be able to better find and combat these fraudsters.

Why pilot arrangements are helpful

A data pilot is a way of designing and implementing a new data sharing arrangement to test whether it helps detect, disrupt or prevent fraud. This can help prove the concept with fewer resources and avoid common mistakes that can contribute to the failure of new projects.

The key aims of a data pilot are to test the quality, performance, reliability and value of the arrangement before increasing its scale or establishing repeatable or automatic data sharing.

How to run a data pilot

Our Data Sharing Pilots Leading Practice Guide describes a five-phase process to running a data sharing pilot to counter fraud:

  • Phase 1: clearly define the fraud problem you want to solve.
  • Phase 2: meet with potential partners to explore ideas and develop the concept, objectives and scope of the pilot.
  • Phase 3: develop a plan, legal frameworks and assessments, and define the data to be shared and the method of exchange.
  • Phase 4: exchange the data, analyse the data and test the results.
  • Phase 5: evaluate the results of the pilot and build on what you have learnt.

This guide reflects leading practice approaches and principles that have been developed and tested through multiple data pilots delivered in the UK and Australia.

Other resource

The Centre's Fraud Data Analytics Catalogue of Techniques provides helpful direction on the types of analytics techniques to explore, and some examples of when and how to deploy them

This guide is designed to help Commonwealth officials who are looking to develop a data sharing pilot to detect, disrupt or prevent fraud. It brings together key learnings and leading practices from data sharing pilots conducted in Australia and the United Kingdom.

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