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The Inland Revenue Services is overwhelmed with gigabyte of disk capacitycontaining data about tax payers in the state. The data stored on the databaseincreases in size at an alarming rate. This has resulted in a data rich butinformation poor situation where there is a widening gap between the explosivegrowth of data and its types, and the ability to analyze and interpret iteffectively, hence the need for a new generation of automated and intelligenttools and techniques known as investigative data mining, to look for patternsin data. These patterns can lead to new insights, competitive advantages forbusiness, and tangible benefits for the State Revenue services. This researchwork focuses on designing effective fraud detection and deterring architectureusing investigative data mining technique. The proposed system architecture isdesigned to reason using Artificial Neural Network and Machine learningalgorithm in order to detect and deter fraudulent activities. We recommend thatthe architectural framework be developed using Object Oriented Programming andAgent Oriented Programming Languages.

A framework for detecting fraudulent activities in edo state tax collection system using investigative data mining