Data analysis can play a critical role in identifying indicators of fraud in most business process areas. By implementing risk and control data analytics to regularly monitor business transactions – and integrating them into an overall risk and control process—management can identify and respond quickly to red flags, and reduce the risk of fraud escalation.
In this whitepaper on Automating Fraud Detection, you will learn how to achieve results by applying data analysis software in key business areas through a discussion of typical frauds, detection processes and tests. Jump ahead to download this whitepaper here now.
Integrating Fraud Detection through Audit, Risk Management, and Compliance
One of the first issues to consider in implementing a fraud detection program is more of a strategic one: Ownership. Is the organizational objective to integrate fraud detection analytical testing processes into those of overall risk management and control, or is it instead to perform them within a standalone function? The specific technical use of data analysis will not vary much in either case, but the people and process aspects will usually require different considerations.
Data analysis, often in the form of continuous monitoring of transactions and controls, is increasingly used as a key component of risk management and audit processes overall. For many organizations it makes sense to integrate fraud detection objectives into risk management and audit processes, since the risk of fraud is simply one among many risks that an organization faces and should be considered within the full spectrum of risks. In other organizations, there may be a more specific functional area focus on fraud, which necessitates different considerations be given to the practical aspects of implementing data analysis approaches.
To continue reading, you can download the full 6-page whitepaper on “The Essential Guide to Automating Fraud Detection” here.