In our first blog in a series on fraud data analytics, we identified a ten-step methodology for conducting a fraud data analytics project. In this blog, we will discuss steps one and two.
Fraud Auditing, Detection, and Prevention Blog
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.
Ghost employee schemes are a common fraud scheme during which there are people on the payroll who don’t work for the company in question but do collect a salary or remuneration.
Let’s take a closer look at how you can use fraud data analytics when creating an executing an audit program for ghost employees:
The fraud triangle, authored by Dr. Cressey, has been adopted by the auditing profession as a tool to help detect and deter occupational fraud.
The model has many applications in the fraud audit. The primary purpose is in the planning stage to help identify where and why your organization would be vulnerable to someone committing a fraud risk scenario.
Here’s a closer look at the fraud triangle and how you can implement this model.
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.
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: