This is part two of a blog post series examining a worked exampled using fraud data analytics on a complex fraud scheme.
In this post we will look at how to use fraud data analytics designed to uncover the complex fraud scheme and the fraud audit procedures designed to provide creditable evidence that the scheme is being perpetrated by the budget owner.
In our fraud risk registers, we have identified over 100 procurement fraud schemes and over 100 overbilling fraud schemes. When these kinds of numbers are involved the idea of finding complex fraud schemes in your core business systems may seem overwhelming. However, fraud data analytics can simplify and improve the process.
In this blog we have selected a complex corruption scheme and a complex overbilling scheme to illustrate how fraud auditing can detect even the most complex schemes. The starting point is to identify the fraud risk statement and then understand how and where the scheme can occur in your organization.
There tends to be a fair amount of confusion when it comes to a fraud risk identification approach versus an experience-based approach – in no small part because within the industry it’s not uncommon to see terms used interchangeably – but here we set out to create a list of universal definitions intended to clarify how and why you might use this approach.
Shell companies are a common fraud scheme you might come across when carrying out a fraud audit. Let’s take a look at how you can implement fraud data analytics into your audit when approaching shell company schemes in particular as a worked example.
In order to fully appreciate how a traditional audit can differ from a fraud audit, it is necessary to grasp how the two types of audit are similar. Bridging the gap between the two can help you minimize the fraud risk for your company and better deploy the programs that are going to work within your organization. While the two audit types can produce vastly different results, they both have similar groundings and structure:
