Fraud risk monitoring system for e-banking transactions

Chaonian GUO, Hao WANG*, Hong Ning DAI, Shuhan CHENG, Tongsen WANG

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

6 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages106-113
Number of pages8
ISBN (Electronic)9781538675182
DOIs
Publication statusPublished - 26 Oct 2018
Externally publishedYes
Event16th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018 - Athens, Greece
Duration: 12 Aug 201815 Aug 2018

Publication series

NameProceedings - IEEE 16th International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018

Conference

Conference16th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 16th International Conference on Pervasive Intelligence and Computing, IEEE 4th International Conference on Big Data Intelligence and Computing and IEEE 3rd Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2018
Country/TerritoryGreece
CityAthens
Period12/08/1815/08/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Big data
  • E-banking
  • Fraud
  • Machine learning
  • Risk Monitoring

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