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

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)

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.

Original languageUndefined/Unknown
Title of host publication2018 IEEE International Conference on Fuzzy Systems (FUZZ)
PublisherThe Institute of Electrical and Electronics Engineers
Pages
ISBN (Print)978-1-5090-6020-7
DOIs
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
Event2018 IEEE International Conference on Fuzzy Systems (FUZZ) - 2018 IEEE International Conference on Fuzzy Systems (FUZZ)
Duration: 8 Jul 201813 Jul 2018

Conference

Conference2018 IEEE International Conference on Fuzzy Systems (FUZZ)
Period08/07/1813/07/18

Keywords

  • Analytics
  • Fraud detection
  • Fuzzy C-Means

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