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Neuroscience Rough Set Approach for Credit Analysis of Branchless Banking

机译:用于无网点银行信用分析的神经科学粗糙集方法

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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.
机译:本文重点介绍移动银行业务。通常被称为“无分支银行”,它提供了一个平台,其中粗糙集理论算法可以增强自主机器学习,以分析纯移动银行平台的信用。首先,定义了术语“移动银行”和“无分支银行”。接下来,它回顾了随着信用分析的发展,无网点银行业务将对世界以及传统银行业务模式在非洲的巨大影响。然后解释了诸如Wonga这样的现有无网点银行的信用分析技术,并提出了对其技术的改进。最后,对金融数据集进行了实验,以实现作者的神经科学算法并应用于粗糙支持向量机,可变精度粗糙集模型和基于可变一致性优势的粗糙集方法模型。

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