Bank Fraud Detection Using Support Vector Machine

Nana Kwame Gyamfi, Jamal Deen Abdulai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

43 Citations (Scopus)

Abstract

With the significant development of communications and computing, bank fraud is growing in its forms and amounts. In this paper, we analyze the various forms of fraud to which are exposed banks d data mining tools allowing its early detection data already accumulated in a bank. We use supervised learning methods Support Vector Machines with Spark (SVM-S) to build models representing normal and abnormal customer behavior and then use it to evaluate validity of new transactions. The results obtained from databases of credit card transactions show that these techniques are effective in the fight against banking fraud in big data. Experiment result from the study show that SVM-S have better prediction performance than Back Propagation Networks (BPN). Besides the average prediction, accuracy reaches a maximum when training the data ratio arrives at 0.8.

Original languageEnglish
Title of host publication2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018
EditorsSatyajit Chakrabarti, Himadri Nath Saha
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages37-41
Number of pages5
ISBN (Electronic)9781538672662
DOIs
Publication statusPublished - 2 Jul 2018
Event9th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018 - Vancouver
Duration: 1 Nov 20183 Nov 2018

Publication series

Name2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018

Conference

Conference9th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018
Country/TerritoryCanada
CityVancouver
Period1/11/183/11/18

Keywords

  • Abnormal and Normal customer's behavior
  • Bank fraud detection
  • Malware detectors
  • Mobile Phone
  • Signature based
  • Spark Malware
  • Support Vector Machine

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