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Optimized Deep Neural Network Based Predictive Model for Customer Attrition Analysis in the Banking Sector

机译：基于深度神经网络基于深度神经网络的顾客磨损分析预测模型

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摘要

Background: In recent time with the growth of the technology and the business model,customer attrition analysis is considered as a very important metric which decides the revenues andprofitability of the organization. It is applicable for all the business domains irrespective of the sizeof the business even including the start-ups. Because about 65% revenue for the organizationcomes from the existing customer. The goal of the customer attrition analysis is to predict the customerwho is likely to exit or churn from the current business organization. In this research work,the literature review is carried out to explore the related work which has been already carried out inthe field of customer attrition analysis. The literature review also focuses on some of the patentswhich are issued in the area of customer attrition or churn analysis. The goal of the research paperis to predict accurately the customer attrition rate in the Banking Sector.Objective: The main objective of this paper is to predict accurately the attrition rate in the Bankingsector using an optimized deep feed-forward neural network.Methods: In the proposed work the predictive machine learning model is implemented using theoptimized deep feed-forward neural network having five hidden layers in it. The model is trained usingAdam optimizer algorithm to obtain the optimal accuracy. The Banking Churn data set is passedas input to the Optimized Deep Feed Forward Neural Network Model. In order to perform the comparativeanalysis, the same data set is passed as input to the other machine learning algorithm such asDecision Tree, Logistic Regression, Gaussian Naïve Bayes, and Artificial Neural Network.Results: The test results indicate that the proposed optimized deep feedforward neural Networkmodel performed better in accuracy compared to existing machine learning techniques.Conclusion: The proposed optimized deep neural network model is an accurate model for customerattrition analysis in the Banking sector compared to the existing machine learning techniques.
机译：背景：近来，随着技术的增长和业务模式，客户的磨损分析被认为是一个非常重要的指标，决定组织的收入和适当性。它适用于所有业务域，而甚至包括业务尺寸，甚至包括初创企业。由于现有客户的组织组织约为65％的收入。客户磨损分析的目标是预测客户谁可能退出或从当前的商业组织中搅拌。在这项研究工作中，进行了文献综述，以探索相关的工作已经进行了客户磨损分析领域。文献综述还侧重于在客户磨损或搅拌分析领域发布的一些专利。研究隶属于准确预测银行业的客户磨损率。这些论文的主要目的是使用优化的深馈神经网络预测银行局的磨损率。方法：在建议的工作使用预测机器学习模型使用其中的优化深馈神经网络具有五个隐藏层。使用ADAM Optimizer算法训练该模型，以获得最佳精度。银行流失数据集是通过优化的深向前馈神经网络模型输入的传递输入。为了执行比较，相同的数据集被传递为另一个机器学习算法的输入，例如，逻辑树，逻辑回归，高斯天真贝叶斯和人工神经网络。结果表明，所提出的优化深馈通道神经网络与现有机器学习技术相比，NetworkModel的准确性更好。结论：建议优化的深度神经网络模型是银行业在银行业的准确模型与现有的机器学习技术相比。

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