The book is written with the objective of automating the audit decision in detecting variations or exceptional data between the current and preceding or penultimate month in payroll processing, using an Expert System.

McCarthy (2000) at Stanford University defines Expert System as: “A ‘knowledge engineer’ interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task.” Expert systems, unlike conventional computer programs, are knowledge-based systems. They are problem-solving programs that mimic the way human expert reasons. The inference engine is the nucleus of an operational expert system. It is the vehicle by which the facts and rules in the said knowledge base are applied to a problem and it gives an expert system its ability to reason. This explains why inference is to computers what reasoning is to humans. Expert systems are in the category of Artificial Intelligence (AI) application; necessary steps to the development of an expert system have therefore been explained in this book.

 The book begins in Chapter one with an introduction. Chapter two is a review of the expert system theory and empirical literature on its use in business applications while Chapter three presents the methodology of research. Chapter Four dwells on the design and development of the expert system software for payroll audit. Finally, Chapter Five concludes with a summary and recommendations.

In this new revision of the previous e-Book published with the different title of “How Auditors or Accountants Can Detect and Prevent Payroll Fraud through an Expert System”, additional information has been provided and the overall quality of presentation improved upon throughout all the chapters.

The payroll audit decision expert system is therefore highly commended to end-users such as internal or external auditors, accountants, fraud examiners, risk consultants and enthusiastic readers seeking to detect and prevent Payroll fraud through an Expert System. The book is also written for the consumption of interested Expert System researchers.