Analysis of Grievances in the Banking Sector through Big Data

Analysis of Grievances in the Banking Sector through Big Data

Arushi Jain (Ambedkar Institute of Advanced Communication Technology and Research, New Delhi, India) and Vishal Bhatnagar (Department of Computer Science and Engineering, Ambedkar Institute of Advanced Communication Technologies and Research, New Delhi, India)
DOI: 10.4018/IJSSMET.2016100102
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Abstract

Banking sector is one of the sectors which face the problem because of the huge amounts of data coming in at a rapid pace. In this paper the authors analyzed grievances in the banking sector through big data. The customers may have a wide variety of complaints and suggestions regarding the working style and other procedural methods which are followed by the banks for effective customer dealing and satisfaction. Quick rectification of complaints and working on customer suggestions can help banks gain a good name in the market, win customers' loyalties and attract new customers. To accomplish this gargantuan task, banks must maintain a complaints database which stores the information on customer complaints across the above mentioned categories along with the response to the complaints by the relevant employees which is collected over a period of time can reveal crucial and critical information for effective CRM in the organizations. In this paper, authors had analyzed the grievances to uncover the valuable suggestions of the customer to provide them a higher level of satisfaction
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Introduction

In recent years, there is a proliferation in the amount of data generated. Voluminous complex data is generated from various sources that cannot be processed by traditional tools (Azar & Hassanien, 2014; Hassanien et al., 2015). Big Data is a current and hot topic of IT industry. Harnessing such huge amount of data is a tough job and thus it requires business intelligence (BI) or analytics. Business Intelligence is required to explore new knowledge, relationships and patterns among different data elements (Labrinidis and Jagadish 2012). The banks have to make perpetual customer centric decisions and make sure they are well informed about all the risks and stakes. One of the best ways to reach out to the customers is to constantly communicate with them. This is obviously difficult as the banks have thousands of branches and more than a million customers in countries which are populous and have a huge number of customers. Therefore, the use of Big Data is really important here. By asking the customers for feedback about products and services, banks can come to know about the customer’s needs and preferences. In this paper, we focused on analyzing all the complaints from the customers that can help banks make well informed decisions in optimizing the services and business process along with region or time of the year specific schemes and offers which may attract more customers.

Big Data is diverse in nature; it contains different data types delivered at different speeds in the numerous forms (Chen et al., 2014). Big Data is not just about being big in size. It has various characteristics given by IBM that are worldwide renowned as 3 Vs of Big Data. These 3 attributes have cleared the myth that size do matters but variety and velocity too are important in the context of big data:

  • 1.

    Volume: Size, (web log files, audio, video, text files, records etc.);

  • 2.

    Variety: Data types (Structured, Unstructured, Semi structured, and mix of three);

  • 3.

    Velocity: Arriving of data, speed (Batch, Real time, Streams).

Hadoop is an excellent and robust analytics platform for Big Data which can process huge data sets fastly and provides scalability. It can manage all aspects of Big Data such as volume, velocity and variety by storing and processing the data over the cluster of nodes. MapReduce is a programming paradigm which can do parallel processing on nodes in a cluster. It takes input and gives the output in form of key-value pairs. MapReduce is able to achieve all this by simply dividing each problem into two broad phases, the map phase and the reduce phase (Al-Jarrah et al., 2015). Pig is a Hadoop project which was initially developed by Yahoo! and is now open source and free to use under Apache. Its scripting language is called as Pig Latin (Chen et al, 2014). The script consists of data flow instructions which are converted to MapReduce instructions by its framework and used to process data.

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