Data Analysis of Consumer Complaints in Banking Industry using Hybrid Clustering

Surbhit Chugani, K. Govinda, S. Ramasubbareddy
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引用次数: 7

Abstract

This paper focus on exploring and analyzing Consumer Finance Complaints data, to find how many similar complaints are there in relation to the same bank or service or product. These datasets fall under the complaints of Credit reporting, Mortgage, Debt Collection, Consumer Loan and Banking Accounting. By using data mining techniques, cluster analysis as well as predictive modeling is applied to obtain valuable information about complaints in certain regions of the Country. The banks that are receiving customer complaints filed against them will analyse the complaint data to provide results on where the most complaints are being filed, what products/ services are producing the most complaints and other useful data. Our model will assist banks in identifying the location and types of errors for resolution, leading to increased customer satisfaction to drive revenue and profitability.
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基于混合聚类的银行业消费者投诉数据分析
本文的重点是对消费者金融投诉数据进行挖掘和分析,以找出有多少类似的投诉涉及到同一家银行或服务或产品。这些数据集属于信用报告、抵押贷款、债务催收、消费者贷款和银行会计的投诉。通过使用数据挖掘技术,应用聚类分析和预测建模来获得有关该国某些区域投诉的宝贵信息。收到客户投诉的银行将分析投诉数据,以提供有关何处投诉最多、哪些产品/服务产生最多投诉以及其他有用数据的结果。我们的模型将帮助银行确定解决错误的位置和类型,从而提高客户满意度,从而推动收入和盈利能力。
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