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.