سبد خرید

A Comprehensive Survey of Data Mining-based Fraud Detection Research

تومان

موجودی: در انبار

This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries.

تعداد:
مقایسه

ABSTRACT

This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains

INTRODUCTION

Data mining is about finding insights which are statistically reliable, unknown previously, and actionable from data (Elkan, 2001). This data must be available, relevant, adequate, and clean. Also, the data mining problem must be well defined, cannot be solved by query and reporting tools, and guided by a data mining process model (Lavrac et al, 2004)

The term fraud here refers to the abuse of a profit organisation’s system without necessarily leading to direct legal consequences. In a competitive environment, fraud can become a business critical problem if it is very prevalent and if the prevention procedures are not fail-safe. Fraud detection, being part of the overall fraud control, automates and helps reduce the manual parts of a screening/checking process This area has become one of the most established industry/government data mining applications. It is impossible to be absolutely certain about the legitimacy of and intention behind an application or transaction. Given the reality, the best cost effective option is to tease out possible
evidences of fraud from the available data using mathematical algorithms.

Year : 2010

Publisher : IEEE

By : CLIFTON PHUA, VINCENT LEE , KATE SMITH & ROSS GAYLER

File Information : English Language / 14 Page / Size : 129 KB

Download : click

سال : 2010

ناشر : IEEE

کاری از : CLIFTON PHUA, VINCENT LEE , KATE SMITH & ROSS GAYLER

اطلاعات فایل : زبان انگلیسی / 14 صفحه / حجم : 129 KB

لینک دانلود : روی همین لینک کلیک کنید

دیدگاهها

هیچ دیدگاهی برای این محصول نوشته نشده است.

اولین نفری باشید که دیدگاهی را ارسال می کنید برای “A Comprehensive Survey of Data Mining-based Fraud Detection Research”
درحال بارگذاری ...