Fuzzy C-Means for Fraud Detection in Large Transaction Data Sets

A4 Conference proceedings


Internal Authors/Editors


Publication Details

List of Authors: Carlsson Christer, Markku Heikkilä, Xiaolu Wang
Place: Rio de Janeiro, Brazil
Publication year: 2018
Publisher: The Institute of Electrical and Electronics Engineers
Book title: 2018 IEEE International Conference on Fuzzy Systems (FUZZ)
ISBN: 978-1-5090-6020-7


Abstract

Large, multinational corporations typically have to process hundreds and
thousands of vendor invoices every day coming from hundreds or
thousands of vendors in different currencies from many countries. This
has forced the adoption of simple and fast rules of thumb to accept and
pay invoices below a chosen value cutoff without further scrutiny. This
has invited fraud, which is hard to detect and react to, as the
transaction data sets are very large. Fuzzy C-means has turned out to be
effective to identify and sort out transactions, which are similar to
typical fraud cases.


Keywords

Analytics, Fraud detection, Fuzzy C-Means

Last updated on 2019-17-08 at 05:02