The ACL Audit Analytic Capability Model: Leveraging analytics in the fight against fraud


Today’s business climate has heightened overall awareness of occupational fraud and abuse and its impact on organisations of all types and sizes. It has also increased management’s expectations of fraud examiners and other assurance providers to be more vigilant and more proactive in the early identification – if not prevention – of fraud. Not only are assurance providers being asked to become more efficient and effective in performing this critical role but expectations are heightened to also assess the risk of fraud in their organisations – a challenge that is amplified by declining or, at best, flat budgets, increasing complexities around regulatory compliance and fewer staff.

For more than 20 years, ACL Services, Ltd. has worked closely with more than 14,700 customer organisations worldwide to develop audit analytic solutions to assist fraud examiners and assurance professionals to leverage the power of data in providing higher levels of insight into their organisation’s business activities. During this time, ACL collected in-depth intelligence from customers who have benefited from using audit analytics, as well as valuable evidence from those who have not.

Based on this knowledge, the Analytic Capability Model was developed as a framework for assessing different levels of analytic techniques and associated benefits. The model illustrates five progressive levels through which fraud examiners should be looking to evolve their use of analytics, and outlines the fundamental building blocks, in terms of people, process and technology that must be in place to optimise benefits.

This model was developed to help organisations more clearly evaluate their use of analytics and to better understand, plan and communicate what needs to be done to achieve and increase benefits and success in fraud detection and prevention. This paper provides an introduction to the Analytic Capability Model and help organisations build a roadmap for increasing analytics testing throughout their fraud detection activities.

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Tuesday, July 16, 2013 In: Whitepapers Comments (None)

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