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Conference Paper: P2P lending fraud detection: a big data approach

TitleP2P lending fraud detection: a big data approach
Authors
KeywordsP2P lending
Loan request fraud
Financial fraud detection
Big data approach
Issue Date2015
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 2015 Pacific Asia Workshop on Intelligence and Security Informatics (PAISI 2015), Ho Chi Minh City, Vietnam, 19 May 2015. In Lecture Notes in Computer Science, 2015, v. 9074, p. 71-81 How to Cite?
AbstractP2P lending directly connects borrowers and lenders without a financial institution as the intermediary. This new form of crowdfunding brings lenders more investment opportunities, but also poses unprecedented risks of default and fraud. This research-in-progress paper focuses on a specific type of fraud, loan request fraud, which may be unique to lenders on Chinese P2P lending sites due to the lack of nationwide credit rating systems in China. We propose research questions surrounding the problem of loan request fraud (its types, features, and detection methods) and present our research methodology and project plans. Specifically, we plan to develop data mining based methods and employ a big data approach to address our research questions. With the help of large volumes of data from a variety of sources, we will be able to find ways to leverage rich datasets about user behaviors and transaction histories to detect loan request fraud more effectively and efficiently.
DescriptionLNCS v. 9074 entitled: Intelligence and Security Informatics: Pacific Asia Workshop, PAISI 2015 ... Proceedings
Persistent Identifierhttp://hdl.handle.net/10722/211082
ISBN
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorXu, JJ-
dc.contributor.authorLu, Y-
dc.contributor.authorChau, M-
dc.date.accessioned2015-07-07T02:23:58Z-
dc.date.available2015-07-07T02:23:58Z-
dc.date.issued2015-
dc.identifier.citationThe 2015 Pacific Asia Workshop on Intelligence and Security Informatics (PAISI 2015), Ho Chi Minh City, Vietnam, 19 May 2015. In Lecture Notes in Computer Science, 2015, v. 9074, p. 71-81-
dc.identifier.isbn978-3-319-18454-8-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/211082-
dc.descriptionLNCS v. 9074 entitled: Intelligence and Security Informatics: Pacific Asia Workshop, PAISI 2015 ... Proceedings-
dc.description.abstractP2P lending directly connects borrowers and lenders without a financial institution as the intermediary. This new form of crowdfunding brings lenders more investment opportunities, but also poses unprecedented risks of default and fraud. This research-in-progress paper focuses on a specific type of fraud, loan request fraud, which may be unique to lenders on Chinese P2P lending sites due to the lack of nationwide credit rating systems in China. We propose research questions surrounding the problem of loan request fraud (its types, features, and detection methods) and present our research methodology and project plans. Specifically, we plan to develop data mining based methods and employ a big data approach to address our research questions. With the help of large volumes of data from a variety of sources, we will be able to find ways to leverage rich datasets about user behaviors and transaction histories to detect loan request fraud more effectively and efficiently.-
dc.languageeng-
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/-
dc.relation.ispartofLecture Notes in Computer Science-
dc.rightsThe final publication is available at Springer via http://dx.doi.org/[insert DOI]-
dc.subjectP2P lending-
dc.subjectLoan request fraud-
dc.subjectFinancial fraud detection-
dc.subjectBig data approach-
dc.titleP2P lending fraud detection: a big data approach-
dc.typeConference_Paper-
dc.identifier.emailChau, M: mchau@business.hku.hk-
dc.identifier.authorityChau, M=rp01051-
dc.identifier.doi10.1007/978-3-319-18455-5_5-
dc.identifier.hkuros244526-
dc.identifier.volume9074-
dc.identifier.spage71-
dc.identifier.epage81-
dc.publisher.placeGermany-
dc.customcontrol.immutablesml 150707-

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