An Evolutionary Framework for Real-Time Fraudulent Credit Detection

Mohebali, B; Karbaschi, G; Tahmassebi, A; Meyer-Baese, A; Gandomi, AH

An Evolutionary Framework for Real-Time Fraudulent Credit Detection

Mohebali, B Karbaschi, G Tahmassebi, A Meyer-Baese, A Gandomi, AH

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 IEEE Congress on Evolutionary Computation (CEC), 2021, 00, pp. 1999-2006
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Mohebali, B
dc.contributor.author	Karbaschi, G
dc.contributor.author	Tahmassebi, A
dc.contributor.author	Meyer-Baese, A
dc.contributor.author	Gandomi, AH
dc.date	2021-06-28
dc.date.accessioned	2022-05-24T06:58:18Z
dc.date.available	2022-05-24T06:58:18Z
dc.date.issued	2021
dc.identifier.citation	2021 IEEE Congress on Evolutionary Computation (CEC), 2021, 00, pp. 1999-2006
dc.identifier.isbn	978-1-7281-8393-0
dc.identifier.uri	http://hdl.handle.net/10453/157671
dc.description.abstract	Fraud has been a worldwide issue that is facing the major economies of the world. Within an economical system, undetected and unpunished fraudulent activities can erode the public trust in law enforcement institutions and even incentivize more fraud. Therefore, detection of fraudulent activities and prosecution of responsible entities is of utmost importance for financial regulatory bodies around the globe. Of the challenges rising with this task is the scarcity of detection resources (auditors) and the fraudsters constantly adapting to the new circumstances of the market. To address these issues, this paper proposes an evolutionary framework for credit fraud detection with the ability to incorporate (and adapt to) the incoming data in real-time. The goal of the framework is to identify the entities with high a risk of fraud for efficient targeting of the scarce resources. The data that is generated as a result of the audits are fed into the framework for further training.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 IEEE Congress on Evolutionary Computation (CEC)
dc.relation.ispartof	IEEE Congress on Evolutionary Computation
dc.relation.ispartofseries	IEEE Congress on Evolutionary Computation
dc.relation.isbasedon	10.1109/cec45853.2021.9504729
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	An Evolutionary Framework for Real-Time Fraudulent Credit Detection
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Kraków, Poland
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-05-24T06:58:17Z
pubs.finish-date	2021-07-01
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-06-28
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Fraud has been a worldwide issue that is facing the major economies of the world. Within an economical system, undetected and unpunished fraudulent activities can erode the public trust in law enforcement institutions and even incentivize more fraud. Therefore, detection of fraudulent activities and prosecution of responsible entities is of utmost importance for financial regulatory bodies around the globe. Of the challenges rising with this task is the scarcity of detection resources (auditors) and the fraudsters constantly adapting to the new circumstances of the market. To address these issues, this paper proposes an evolutionary framework for credit fraud detection with the ability to incorporate (and adapt to) the incoming data in real-time. The goal of the framework is to identify the entities with high a risk of fraud for efficient targeting of the scarce resources. The data that is generated as a result of the audits are fed into the framework for further training.

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http://hdl.handle.net/10453/157671