Abstract
This paper focuses on mobile banking; very often referred to as “branchless banking” which presents a platform wherein rough set theory algorithms can enhance autonomous machine learning to analyze credit for a purely mobile banking platform. First, the terms “mobile banking” and “ branchless banking” are defined. Next, it reviews the huge impact branchless banking with credit analysis will have on the world and the traditional banking models as it becomes a reality in Africa. Credit Analysis techniques of current branchless banks such as Wonga are then explained and an improvement on their techniques is presented. Finally, experiments taken implementing the author’s neuroscience algorithms and applied with rough SVMs, Variable Precision Rough Set Models and Variable Consistency Dominance-based Rough Set Approach models are performed on financial data sets and their results are presented.
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Lewis, R. (2014). Neuroscience Rough Set Approach for Credit Analysis of Branchless Banking. In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_57
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DOI: https://doi.org/10.1007/978-3-319-08326-1_57
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08325-4
Online ISBN: 978-3-319-08326-1
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