Data Mining for Fraud Detection

View Sample PDF

Author(s): Roberto Marmo (Universita' Pavia, Italy)
Copyright: 2021
Pages: 13
Source title: Encyclopedia of Organizational Knowledge, Administration, and Technology
Source Author(s)/Editor(s): Mehdi Khosrow-Pour D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-7998-3473-1.ch079

Keywords: Administration & Leadership / Business & Management / Business and Organizational Research / Business Science Reference

Purchase

View Data Mining for Fraud Detection on the publisher's website for pricing and purchasing information.

Abstract

With the increased use online and electronic resources both by the companies and the customers the problem of fraud has been rising in the last decade. Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data, that may signify interesting patterns, including those related to fraud. This chapter aims to introduces to the concepts of fraud, processes and tools involved in data mining techniques, as well as the importance, challenges, and use cases.

The IRMA Community

Research IRM

Data Mining for Fraud Detection

Purchase

Abstract

Related Content

IRMA Sponsors