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Data analytics underused as anti-fraud tool 

By Ken Tysiac 
May 02 2014

Data analytics has the potential to be a powerful tool for organisations to use to combat fraud and other financial crimes. But many organisations are failing to effectively use data analytics for this purpose.

Almost two-thirds (63%) of 466 senior executives at leading companies around the world say they need to do more to improve their anti-fraud and anti-bribery procedures, including the use of forensic data analytics, according to a recent EY survey.

And despite the proliferation of uses for Big Data in recent years, 21% of 2,100 professionals taking part in a recent Deloitte webcast survey said their organisations have no plans to use Big Data to manage the risk of financial crimes including bribery, corruption and money laundering.

“Today, regulators expect organisations to have holistic enterprise fraud and misuse management programmes spanning all business units and international borders,” Tony DeSantis, a principal in the Deloitte Transactions and Business Analytics LLP data analytics practice, said in a news release. “However, many organisations are unsure where to begin and how to effectively apply analytics.”

A majority of senior executives recognise the potential of data analytics as a tool to fight fraud, according to the EY survey report. Almost three-fourths (72%) of respondents said emerging Big Data technologies can play a key role in fraud prevention and detection.

Nine in ten said forensic data analytics will enhance the risk-assessment process, and 82% said forensic data analytics will lead to earlier detection of misconduct. But just 2% said they are leveraging Big Data technologies, and 11% said they are using statistical analysis and data-mining tools to fight fraud.

“While companies may be doing some forms of [forensic data analytics], many could be missing important opportunities to improve their anti-fraud and anti-bribery efforts,” David Remnitz, global forensic technology and discovery services leader with EY, said in a news release. “By combining multiple data sources and leveraging advanced [forensic data analytics] tools, companies are now able to gain new and important insights from their business data.”

EY’s report provides five tips for successful forensic data analytics integration:

  • Start with a high-priority, high-return project. The first project will require significant set-up costs, so it is important to begin with a project that will lead to tangible returns, according to EY.
  • Go beyond “rule-based”, descriptive analytics. Companies need to advance beyond rule-based spreadsheets and database applications to use structured and unstructured data sources that consider the use of data visualisation, text-mining and statistical analysis tools, the report says.
  • Communicate. Sharing information across multiple departments about early success will build momentum, according to the report.
  • Keep it simple and intuitive. Organisations should avoid cramming too much information into one report and should invest in professionals with the skills to develop and sustain successful data analytics efforts, EY said.
  • Exercise patience. Although quick-hit projects may be completed in four to six weeks, full integration can take a year or more, the report says. And the programme will need to be refined as business activities and risks change.

Ken Tysiac (ktysiac@aicpa.org) is a CGMA Magazine senior editor.

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