At the June 2019 ACFE conference I spoke about Fraud Data Analytics (FDA): How to Locate Complex Vendor Overbilling Fraud Risk Statements. My sessions were sold out, so for those unable to hear my presentation I am writing three blogs in which I will explain three fraud statements I covered:
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 nine and ten:
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 seven and eight:
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:
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.
