  The Inland Revenue Services is overwhelmed with gigabyte of disk capacity
containing data about tax payers in the state. The data stored on the database
increases in size at an alarming rate. This has resulted in a data rich but
information poor situation where there is a widening gap between the explosive
growth of data and its types, and the ability to analyze and interpret it
effectively, hence the need for a new generation of automated and intelligent
tools and techniques known as investigative data mining, to look for patterns
in data. These patterns can lead to new insights, competitive advantages for
business, and tangible benefits for the State Revenue services. This research
work focuses on designing effective fraud detection and deterring architecture
using investigative data mining technique. The proposed system architecture is
designed to reason using Artificial Neural Network and Machine learning
algorithm in order to detect and deter fraudulent activities. We recommend that
the architectural framework be developed using Object Oriented Programming and
Agent Oriented Programming Languages.
