使用调查性数据采掘检测江户州税收系统中欺诈活动的框架
A framework for detecting fraudulent activities in edo state tax collection system using investigative data mining
摘要：
国内税务局被包含该州纳税人数据的千兆字节磁盘容量所淹没。存储在数据库中的数据以惊人的速度增长。这导致了数据丰富但信息贫乏的情况，其中在数据及其类型的爆炸性增长与有效分析和解释它的能力之间存在越来越大的差距，因此需要称为调查数据采掘的新一代自动化和智能工具和技术来寻找数据中的模式。这些模式可以带来新的见解，为企业带来竞争优势，并为国家税收服务带来切实利益。本研究工作的重点是利用调查性数据采掘技术设计有效的欺诈检测和威慑体系结构。所提出的系统架构旨在使用人工神经网络和机器学习算法进行推理，以检测和阻止欺诈活动。我们建议使用面向对象编程和面向代理的程序设计语言开发架构框架。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
