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APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions

APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions
APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions
In the last decade, the ease of online payment has opened up many new opportunities for e-commerce, lowering the geographical boundaries for retail. While e-commerce is still gaining popularity, it is also the playground of fraudsters who try to misuse the transparency of online purchases and the transfer of credit card records. This paper proposes APATE, a novel approach to detect fraudulent credit card transactions conducted in online stores. Our approach combines (1) intrinsic features derived from the characteristics of incoming transactions and the customer spending history using the fundamentals of RFM (Recency - Frequency - Monetary); and (2) network-based features by exploiting the network of credit card holders and merchants and deriving a time-dependent suspiciousness score for each network object. Our results show that both intrinsic and network-based features are two strongly intertwined sides of the same picture. The combination of these two types of features leads to the best performing models which reach AUC-scores higher than 0.98.
credit card transaction, fraud, network, analysis, bipartite graphs, supervised learning
0167-9236
38-48
Van Vlasselaer, Veronique
80a16e2b-f1d0-4d27-bec5-fe3669d9477c
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Caelen, Olivier
9fd3ccc4-bd9a-4305-997e-2605bd35561a
Eliassi-Rad, Tina
f7b2b811-0cbc-43d6-8005-49570f41018b
Akoglu, Leman
68ea2210-3c84-4bc5-b56d-7b72d46f5029
Snoeck, Monique
9aee96bc-8a57-4c37-bcd7-e83f0b173ee1
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Van Vlasselaer, Veronique
80a16e2b-f1d0-4d27-bec5-fe3669d9477c
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Caelen, Olivier
9fd3ccc4-bd9a-4305-997e-2605bd35561a
Eliassi-Rad, Tina
f7b2b811-0cbc-43d6-8005-49570f41018b
Akoglu, Leman
68ea2210-3c84-4bc5-b56d-7b72d46f5029
Snoeck, Monique
9aee96bc-8a57-4c37-bcd7-e83f0b173ee1
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

Van Vlasselaer, Veronique, Bravo, Cristian, Caelen, Olivier, Eliassi-Rad, Tina, Akoglu, Leman, Snoeck, Monique and Baesens, Bart (2015) APATE: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 75 (1), 38-48. (doi:10.1016/j.dss.2015.04.013).

Record type: Article

Abstract

In the last decade, the ease of online payment has opened up many new opportunities for e-commerce, lowering the geographical boundaries for retail. While e-commerce is still gaining popularity, it is also the playground of fraudsters who try to misuse the transparency of online purchases and the transfer of credit card records. This paper proposes APATE, a novel approach to detect fraudulent credit card transactions conducted in online stores. Our approach combines (1) intrinsic features derived from the characteristics of incoming transactions and the customer spending history using the fundamentals of RFM (Recency - Frequency - Monetary); and (2) network-based features by exploiting the network of credit card holders and merchants and deriving a time-dependent suspiciousness score for each network object. Our results show that both intrinsic and network-based features are two strongly intertwined sides of the same picture. The combination of these two types of features leads to the best performing models which reach AUC-scores higher than 0.98.

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Van Vlasselaer APATE.pdf - Accepted Manuscript
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More information

Accepted/In Press date: 30 April 2015
e-pub ahead of print date: 8 May 2015
Published date: 1 July 2015
Keywords: credit card transaction, fraud, network, analysis, bipartite graphs, supervised learning
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 376989
URI: http://eprints.soton.ac.uk/id/eprint/376989
ISSN: 0167-9236
PURE UUID: 88aeb966-8a11-49dd-bb07-85b890f36ff9
ORCID for Cristian Bravo: ORCID iD orcid.org/0000-0003-1579-1565
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 13 May 2015 10:39
Last modified: 28 Apr 2022 05:58

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Contributors

Author: Veronique Van Vlasselaer
Author: Cristian Bravo ORCID iD
Author: Olivier Caelen
Author: Tina Eliassi-Rad
Author: Leman Akoglu
Author: Monique Snoeck
Author: Bart Baesens ORCID iD

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