Coronavirus (COVID-19) has caused management to rightly focus on their organisation’s core operations, however 77 percent of Australian companies that contributed to KPMG’s COVID-19 Fraud Survey reported that the resulting lack of focus on controls exposed their companies to exploitation.

We consider how organisations can quickly regain focus and reduce risk by effectively deploying data analytics.

Using data to reduce the risk

As traditional frameworks designed to preserve operational integrity are compromised in the COVID-19 environment, there is a need to find solutions that effectively monitor risks and deliver insights, using less resources.

The use of data presents an opportunity to get ahead of the curve by proactively understanding, investigating and managing these risks.

Leveraging data and equipping your people with analytical skills will help reduce manual effort and deliver deeper insights, faster. By deploying analytics tools, within tried and tested frameworks, data can be extracted, interrogated and reported on, remotely and in real-time.

Data allows an organisation to see how it’s performing and its vulnerabilities. Data can also provide early insight to opportunities to tighten controls, grow and improve. Forensic data analytics provides a means for tracing through investigations, emerging issues, remediation challenges and recovery pathways – to provide more weight and credibility to findings.

Deploying analytics is easy; deploying analytics effectively is not

The key to deploying analytics effectively is understanding how a fraud or corrupt event would appear in the data. Consideration also needs to be given to how fraudsters are currently exploiting organisations or how corrupt payments are typically paid.

Fraudsters are opportunistic and quick to exploit opportunities – data analytics needs to move just as fast. Deploying a series of standard tests resulting in many pages of data exceptions will not only result in missed detection opportunities, it may in fact increase the fraud and corruption risk by distracting the organisation.     

Data integrity is critical building block for effective analytics. It is critical to first properly assess the underlying integrity of data and test assumptions, ensuring that testing is effectively risk-based and tailored to the underlying control environment. Failure to ensure the integrity of the data further increases the risk of a poor outcome and a business distracted from the real threats.

Consider how your organisation currently relies on insights from data analytics to inform decisions.  Complete, accurate and appropriate data and trusted analytics is crucial, and access to high quality data analytics to overcome resource intensive work will become the new norm.

Data analytics roadblocks

As with any technology, there are challenges in using analytics effectively. Primarily, these challenges revolve around the quality and availability of data.

  • Functional requirements: Organisations cannot predict the type or granularity of data they will need on a certain aspect of calculation. Many systems and databases are not designed with fraud detection in mind. By maintaining data systems and complying with best practices regarding data collection and retention, organisations can be better prepared to meet unforeseen requirements.
  • Accessibility: Depending on an organisation's data systems and suppliers or third parties involved in a potential fraud, it can be difficult to pinpoint where relevant case data lives. In other cases, extracting and collecting that data can be difficult, either due to system limitations or because the data resides on a third party's system.
  • An incomplete story: In contractual disputes arising from alleged fraud there is certain data that cannot, or should not, be divulged to the other party. As a result, one side might not have complete data and the resulting analysis might not reflect all of the circumstances in a given matter.
  • Duplicate records: There can sometimes be multiple versions of 'the truth' during a dispute process because a client uses multiple systems that are not synchronised. The multiplicity of systems can result from business acquisitions or system upgrades, thereby leading to questions over which version of the data set is correct.

Keys to successful data analytics

When planning a data driven fraud and corruption program there are a number of things that can be done to maximise the benefits and minimise the roadblocks.

  • Consider your fraud risk exposure in a disrupted operating environment and how data analytics can effectively support fraud control efforts.
  • Understand how fraudsters are attacking organisations in the coronavirus (COVID-19) era working conditions and how leading indicators of these frauds could be found in your data.
  • Where necessary, undertake a gap analysis and risk assessment of fraud and corruption risks.
  • Understand what data you have, where it is stored and its underlying quality.
  • Bring together disparate data sources – real insights are unlikely to be found from just one system. For example considering tender and procurement systems alongside travel and expense systems might provide valuable insights into potential corruption.
  • Leverage proven analytics tools that are already readily available.
  • Create baseline solutions that have utility for the organisation (i.e. targeting high risk areas) and use this as a pilot which can always be expanded.


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