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Interpretable Online Banking Fraud Detection based on Hierarchical Attention Mechanism

Citation Author(s):
Sarit Kraus, Jacob Goldberger
Submitted by:
Idan Achituve
Last updated:
25 October 2019 - 9:32am
Document Type:
Poster
Document Year:
2019
Event:
Presenters Name:
Idan Achituve
Paper Code:
31

Abstract 

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.

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Interpretable Online Banking Fraud Detection Based on Hierarchical Attention Mechanism-poster.pdf

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