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

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dc.contributor.authorMagagula, Sihle
dc.date.accessioned2021-03-19T11:48:44Z
dc.date.available2021-03-19T11:48:44Z
dc.date.issued2020-05
dc.descriptionApplied 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 2020en_US
dc.description.abstractIn 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.en_US
dc.description.sponsorshipAshesi Universityen_US
dc.identifier.urihttp://hdl.handle.net/20.500.11988/630
dc.language.isoenen_US
dc.subjecttext analysisen_US
dc.subjectmachine learningen_US
dc.subjectmobile moneyen_US
dc.subjectSMSen_US
dc.subjectEswatini (formerly Swaziland)en_US
dc.subjectunbankeden_US
dc.titleMobile money SMS classification and text analysis: Exploring possibilities for enhanced financial inclusionen_US
dc.typeApplied projecten_US

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