Neuroscience Rough Set Approach for Credit Analysis of Branchless Banking

  • Rory Lewis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)


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.


Domain Adaptation Credit Union Source Domain Fuzzy Support Vector Machine Intelligent Information System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rory Lewis
    • 1
  1. 1.Department of Computer ScienceUniversity of Colorado at Colorado SpringsColorado SpringsUSA

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