Fraud Auditing, Detection, and Prevention Blog

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:

As per the commonly understood fraud triangle there, are three typical components why someone would commit a fraud scheme: motive, opportunity, and rationalization. While motive and rationalization are factors that could be limited to individual behavior and psychology, opportunity is something your internal controls can and should account for.

Within rationalization, anyone involved in a fraud scheme needs to have the ability and opportunity to conceal and commit fraud. There is a direct relationship between having the opportunity to commit fraud and having the ability to conceal the fraud and so auditors need to consider both opportunity and the ability to conceal in the design of an audit plan.

Knowing which common concealment strategies might have been deployed by any given fraud scheme is an essential part of any approach to preventing and detecting fraud. Let’s examine concealment in more detail.

It is often the case that fraud scenarios are not as simple as meets the eye. Most financial executives are aware of the importance of having internal controls in fraud prevention and detection, yet few realize the potential fraud-based approaches have in finding complex fraud that is often overlooked by basic evidence of control measures. 

While no fraud methodology is entirely foolproof, taking a systematic and methodical approach to data by finding hidden patterns is a proven and robust approach to detection. This is something we’ve been specializing in for over 30 years at Fraud Auditing Inc., and so we have a wealth of experience in the area.

 Here’s a look at how you can implement a fraud-based approach in your organization and its advantages.

When it comes to fraud, no audit plan is going to fit all. Each fraud scenario in your audit scope needs its own fraud data analytics plan and fraud audit procedures. An audit plan isn’t just a document to create and file away as a matter of process – any auditor involved in future detection and prevention will need this to have the correct information that will allow them to find and reveal which fraud scenario is occurring. In short, your fraud audit plan is an essential component in detecting and preventing fraud. 

Once an auditor has a plan and the fraud scenarios at their disposal, the best approach to building a fraud data profile involves using fraud data analytics. For many, this might feel like no more than a buzzword in the industry that adds little value to conversations on fraud but when we talk about fraud data analytics at Fraud Auditing Inc., we mean something very specific, with measurable outcomes, which are essential to any fraud-based approach. 

Here’s a look at the best practices when it comes to data analytics and how it can add real value to your approach.