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 Data analytics

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Summary

Data is collected and analysed to improve processes and payment accuracy, and to prevent non-compliance and fraud.

Examples

Some examples of this type of countermeasure include:

  • Profiling fraud methodologies against programme data.
  • Risk scoring based on recipient characteristics and payment types.
  • Analysing trends and patterns in programme data, for example, increased fraudulent behaviour via online channels.
  • Spatial analysis to identify claiming patterns and anomalies, for example, claims for disaster relief payments outside affected areas.
  • Washing large data sets together to identify suspicious activity.

Purpose of this countermeasure

Failing to collect and analyse data to improve processes and payment accuracy can:

  • lead to dysfunctional and obscure processes,
  • cloud the visibility of fraud and corruption risks, and
  • inhibit the action needed to ty to prevent, detect and respond to fraud and corruption.

Fraudsters can take advantage of this environment by exploiting weaknesses and avoiding exposure.

Dependencies

This type of control is supported by:

How do I know if my countermeasures are effective?

You can apply the following methods to measure the effectiveness of these types of countermeasures:

  • Consult subject matter experts on the data analytics that is performed.
  • Review the methodology used to analyse the data.
  • Review how data is used to analyse processes.
  • Confirm sufficient data is collected to effectively analyse compliance, payment accuracy and potential fraud.
  • System or process walkthrough – have staff show you how data is collected and analysed.
  • Review how often data analytics is performed.
  • Confirm the data is:
    • reliable
    • current, and
    • meaningful.
  • Check if and how the results are used to improve processes and controls.

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