Even the world's best auditor using the world's best audit program cannot detect fraud unless their sample includes a fraudulent transaction. That is why fraud data analytics is so essential to the auditing profession.

Fraud auditing is a methodology tool used to respond to the risk of fraud in core business systems. The methodology must start with the fraud risk identification. Fraud data analytics is about searching for a fraud scenario versus a data anomaly. I have often referred to fraud data analytics as code breaking. The fraud auditor is studying millions of transactions in the attempt to find the needle in the haystack, called the fraud scenario. It is my hope that my years of professional experience in using fraud data analytics will move the auditing profession to become the number‐one reason for fraud detection.

This book is about the science of fraud data analytics. It is a systematic study of fraud scenarios and their relationship to data. Like all scientific principles, the continual study of the science and the practical application of the science are both necessary for success in the discovery of fraud scenarios that are hiding in all core business systems.

The methodology described in the book is intended to provide a step‐by‐step process for building the fraud data analytics plan for your company. The first five chapters explain each phase of the process. Later chapters illustrate how to implement the methodology in asset misappropriation schemes, corruption schemes, and financial reporting schemes.

The practitioner will learn that fraud data analytics is both a science and an art. In baseball, there is a science to hitting a baseball. The mechanics of swinging a bat is taught to players of all ages. However, you can read all the books in the world about swinging a bat, but unless you actually stand in the batting box and swing the bat, you will never truly learn the art of hitting a baseball. Likewise, the fraud auditor needs to learn to analyze data and to employ the tools to do so in order to be able to find fraud scenarios hiding in your data systems.