dc.contributor.author | Guo, Chaonian | |
dc.contributor.author | Wang, Hao | |
dc.contributor.author | Dai, Hong-Ning | |
dc.contributor.author | Cheng, Shuhan | |
dc.contributor.author | Wang, Tongsen | |
dc.date.accessioned | 2019-05-03T08:42:41Z | |
dc.date.available | 2019-05-03T08:42:41Z | |
dc.date.created | 2019-01-17T19:00:01Z | |
dc.date.issued | 2018 | |
dc.identifier.isbn | 978-1-5386-7519-9 | |
dc.identifier.uri | http://hdl.handle.net/11250/2596417 | |
dc.description.abstract | Fraudulent e-banking transactions have caused great economic loss every year. Thus, it is important for financial institutions to make the e-banking system more secure, and improve the fraud detection system. Researches for the fraud risk monitoring are mainly focused on score rules and data driven model. The score rule is based on expertise, which is vulnerable to new patterns of frauds. Data driven model is based on machine learning classifiers, and usually has to handle the imbalanced classification problem. In this paper, we propose a novel fraud risk monitoring system for e-banking transactions. Model of score rules for online real-time transactions and offline historical transactions are combined together for the fraud detection. Parallel big data framework: Kafka, Spark and MPP Gbase which integrated with a machine learning algorithm is presented to handle offline massive transaction logs. Experimental results show the effectiveness of our proposed scheme over a real massive dataset of e-banking transactions. This evaluation leads us to identify research gaps and challenges to consider in future research endeavors. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | nb_NO |
dc.relation.ispartof | 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) | |
dc.title | Fraud Risk Monitoring System for E-Banking Transactions | nb_NO |
dc.type | Chapter | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 100-105 | nb_NO |
dc.identifier.doi | 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00030 | |
dc.identifier.cristin | 1659761 | |
dc.description.localcode | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | nb_NO |
cristin.unitcode | 194,63,55,0 | |
cristin.unitname | Institutt for IKT og realfag | |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
cristin.qualitycode | 1 | |