Mobile money SMS classification and text analysis: Exploring possibilities for enhanced financial inclusion
In 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.
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 2020
text analysis, machine learning, mobile money, SMS, Eswatini (formerly Swaziland), unbanked