Predicting fraud in mobile money transfer using case-based reasoning

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

Predicting fraud in mobile money transfer using case-based reasoning

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

Mobile money SMS classification and text analysis: Exploring possibilities for enhanced financial inclusion

Applied project submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, May 2020In the past two decades, financial technology (fintech) has grown to become one of the most significant economic drivers in developing countries especially in sub-Saharan Africa. Despite the prevalence of these fintech mostly in the form of mobile money platforms, the number of unbanked populations across developing countries has remained high. This applied project presents a human-centered approach in the innovator-side exploration of the integration between the banking sector and fintech. Such innovations should ask nothing more from the user than they already have, should adopt a fluid digital footprint, and the services offered by integrated platforms should be dynamic. To that end, the paper presents a system that classifies mobile money SMSs and use them to prepare a secure financial statement that might enhance the Know-Your-Customer requirements for the unbanked and also ensure that they can easily transfer their mobile money credit record and easily access services in the banking sector.Ashesi Universit