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

TL;DR

This paper proposes an investigative data mining framework utilizing neural networks and machine learning to detect and prevent tax fraud in Edo State's revenue system, addressing the challenge of analyzing large, complex datasets.

Contribution

It introduces a novel architecture combining AI techniques and object-oriented programming for effective tax fraud detection in a data-rich environment.

Findings

Effective fraud detection architecture designed

Use of neural networks and machine learning algorithms

Recommendations for implementation using OOP and AOP

Abstract

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.