Predicting fraud in mobile money transfer using case-based reasoning

Adedoyin, Adeyinka, Kapetanakis, Stelios, Samakovitis, Georgios and Petridis, Miltos (2017) Predicting fraud in mobile money transfer using case-based reasoning. Artificial Intelligence XXXIV: 37th SGAI International Conference on Artificial Intelligence, AI 2017, Cambridge, UK, December 12-14, 2017, Proceedings. In: SGAI 2017: International Conference on Innovative Techniques and Applications of Artificial Intelligence, 12-14 Dec 2017, Cambridge, United Kingdom. ISBN 9783319710778, e-ISBN 9783319710785. ISSN 0302-9743 (doi:10.1007/978-3-319-71078-5_28)

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Abstract

This paper proposes an improved CBR approach for the identification of money transfer fraud in Mobile Money Transfer (MMT) environments. Standard CBR capability is augmented by machine learning techniques to assign parameter weights in the sample dataset and automate k-value random selection in k-NN classification to improve CBR performance. The CBR system observes users’ transaction behaviour within the MMT service and tries to detect abnormal patterns in the transaction flows. To capture user behaviour effectively, the CBR system classifies the log information into five contexts and then combines them into a single dimension, instead of using the conventional approach where the transaction amount, time dimensions or features dimension are used individually. The applicability of the proposed augmented CBR system is evaluated using simulation data. From the results, both dimensions show good performance with the context of information weighted CBR system outperforming the individual features approach.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published as: Adedoyin A., Kapetanakis S., Samakovitis G., Petridis M. (2017) Predicting Fraud in Mobile Money Transfer Using Case-Based Reasoning. In: Bramer M., Petridis M. (eds) Artificial Intelligence XXXIV. SGAI 2017. Lecture Notes in Computer Science, vol 10630. Springer, Cham
Research Areas: A. > School of Science and Technology > Computer Science > Artificial Intelligence group
Item ID: 23795
Notes on copyright: This is a Author Accepted Manuscript version of an paper published in Artificial Intelligence XXXIV. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-71078-5_28
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Depositing User: Miltos Petridis
Date Deposited: 06 Mar 2018 12:11
Last Modified: 02 Apr 2020 11:10
URI: https://eprints.mdx.ac.uk/id/eprint/23795

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