Interpretable Online Banking Fraud Detection Based on Hierarchical Attention Mechanism

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

11 Scopus citations

Abstract

Online banking activities are constantly growing and are likely to become even more common as digital banking platforms evolve. One side effect of this trend is the rise in attempted fraud. However, there is very little work in the literature on online banking fraud detection. We propose an attention based architecture for classifying online banking transactions as either fraudulent or genuine. The proposed method allows transparency to its decision by identifying the most important transactions in the sequence and the most informative features in each transaction. Experiments conducted on a large dataset of real online banking data demonstrate the effectiveness of the method in terms of both classification accuracy and interpretability of the results.

Original languageEnglish
Title of host publication2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728108247
DOIs
StatePublished - Oct 2019
Event29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019 - Pittsburgh, United States
Duration: 13 Oct 201916 Oct 2019

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2019-October
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019
Country/TerritoryUnited States
CityPittsburgh
Period13/10/1916/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Online banking
  • attention
  • deep learning
  • fraud detection
  • interpretability

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