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

Christer Carlsson; Markku Heikkilä; Xiaolu Wang

doi:10.1109/FUZZ-IEEE.2018.8491498

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

Christer Carlsson, Markku Heikkilä, Xiaolu Wang

Forskningsoutput: Kapitel i bok/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

1 Citeringar (Scopus)

Sammanfattning

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.

Originalspråk	Odefinierat/okänt
Titel på värdpublikation	2018 IEEE International Conference on Fuzzy Systems (FUZZ)
Förlag	The Institute of Electrical and Electronics Engineers
Sidor	–
ISBN (tryckt)	978-1-5090-6020-7
DOI	https://doi.org/10.1109/FUZZ-IEEE.2018.8491498
Status	Publicerad - 2018
MoE-publikationstyp	A4 Artikel i en konferenspublikation
Evenemang	2018 IEEE International Conference on Fuzzy Systems (FUZZ) - 2018 IEEE International Conference on Fuzzy Systems (FUZZ) Varaktighet: 8 juli 2018 → 13 juli 2018

Konferens

Konferens	2018 IEEE International Conference on Fuzzy Systems (FUZZ)
Period	08/07/18 → 13/07/18

Nyckelord

Analytics
Fraud detection
Fuzzy C-Means

Åtkomst av dokument

10.1109/FUZZ-IEEE.2018.8491498

https://ieeexplore.ieee.org/document/8491498

Citera det här

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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.",

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