Fraud Auditing, Detection, and Prevention Blog
Fraud Analytics: Planning considerations
This blog is the first in a series of seven to explain how to perform fraud data analytics. It introduces a ten-step approach along with explaining the concept of fraud auditing.
For years, auditors, myself included, would launch a fraud detection project by getting the data and playing with the data. At least that was the expression. We hoped to trip across a fraud scheme. We had no specific plan, just a simple goal: Find fraud. We did not know what we were looking for, but we were looking. Eventually, we hoped to find something. But, those days are over.
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.
How you determine which concepts to evaluate to “consider fraud” and how to integrate fraud into your audit program is a challenge that is easily solved if you approach it with the scope and objectives of your audit clearly defined.
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.
I was a Keynote Speaker for the IDEA Innovations Conference in Houston recently exploring Breaking the Code of Fraud. My keynote presentation covered my experience in using data analytics over my 30 years of professional work, with an emphasis on how we as an industry need to move away from experience-based fraud detection to a scientific-based model of fraud detection and prevention.
Every organization and business system has inherent vulnerabilities to fraud. Taking advantage of vast amounts of data is exactly how organizations and individuals are able to conceal a fraud for years. Staying ahead of them is all about how you approach that data and what systems you have in place to identify potential weaknesses. Let’s examine this.