Analyzing the Effects of Big Data on the Banking Industry Operations

This research project investigates the consequences of applying big data within the banking sector, focusing on issues stemming from traditional banking systems. The primary aim is to understand big data's impact on the banking sector, enabling banks to identify customers, gather feedback, and improve operations. The project analyzes the effects of big data on fraud detection, marketing, and credit risk management, utilizing primary data collected through online surveys. The research employs a deductive approach and descriptive research design, with a timeline outlining the study's progression. The study focuses on the banking sector employees to understand the issues they face and the impact of big data on their work environment. The findings will benefit banking organizations by improving customer relationship management, and the project adheres to ethical considerations, ensuring data security and transparency. The research involves a quantitative analysis using a Likert scale and presents results through charts and graphs.

Running head: BIG DATA IN BANKING INDUSTRY
Big data in Banking Industry
Name of the Student
Name of the University

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BIG DATA IN BANKING INDUSTRY
Table of Contents
Abstract............................................................................................................................................3
Client, Audience and Motivation.....................................................................................................3
Research Plan...................................................................................................................................4
Timeline...........................................................................................................................................6
References........................................................................................................................................8

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BIG DATA IN BANKING INDUSTRY
Abstract
This research project is focused on the consequences of application of big data in the
nominated banking sector. Issues faced by banking system due to traditional banking system has
been focused. The primary aim of the project is to understand the impact of big data specifically
in banking sector. The banking sector will be able to identify new and existing customers and
receive their feedback to improve the business operation through increasing services as well as
productivity. The concept of big data is also explained details in the research paper. Banking
sector has been facing issues with collection and analysis of data provided by customers and
employees. The major impact of big data on banking system has been focusing in fraud
detection, marketing and credit risk management. In this research, primary data collection
method has been used for collecting data and information. Online surveys will be created using
Google forms and sent to participants. Integration of appropriate design for research has been
helping in revealing lot of pattern and sources of data and information required for completing
the study. In order to prevent unwanted users to access data from the server suing big data
application is much crucial. Since 2 to 3 years the banking sector is operating along with the big
data environment.
Client, Audience and Motivation
This project is based on the effect of big data application mainly in the banking sector.
Banking sector has been an important sector for the economy of any country. The traditional
approach of the banking sector has been facing a lot of problems regarding database management
and human error (Alalwan, Dwivedi and Rana 2017). Therefore, there has been a need of
technological impact in the banking sector. Big data has been one of the important technology

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that might help in minimizing these issues in the banking sector. This project has been focused in
the employees of the bank and issues faced by them in their work environment.
The primary aim of the project has been understanding the effect of big data in the
banking sector. The banking sector have been able to identify their key customers and receive
their feedback for improving business operation by increasing their productivity and services
(Amado et al. 2018). The main research question for this project has been as follows:
How big data impacts the business operations in banking system?
The primary problem due to which project has been initiated has been related to problems
faced by the banks in data management and error management. The feedbacks of employee and
customers have been collected and analysed for fixing challenges faced by them in the banking
sector. The project will focus in the customer satisfaction level with the services provided by the
bank (Barrdear and Kumhof 2016). The increase in use of big data in various fields have been a
significant effect on the market. This project will look forward for analyzing the negative impact
of big data mainly in the banking sector.
The perception of big data management is focused on management as well as analysis of
huge amount data or information. There has been increase in incoming of data and information
from various sources including both online and offline (Brinkmann et al. 2018). The findings if
the project will benefit the banking organization and employees working in the organization.
The customer relationship management might get increase with the outcomes of the project.
Research Plan
Research plan has been focused on scientific approach for completing the research study.
There are various subsection of the research that is comprises of research methods, research

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BIG DATA IN BANKING INDUSTRY
approach, research design, data collection, sampling size, analysis and ethical consideration
(Panneerselvam 2014). A timeline will be provided at the end of the research that will limit the
time and date of research to be completed within. A quantitative method will be selected for the
progression of the research. The research approach deals two types including deductive and
inductive approach (Carneiro, Figueira and Costa 2017). The primary aim of the deductive
method has been focused on testing the existing theory and provide proper analyses of the
research. The major disadvantage of the approach has been inflexible nature and holistic
approach. The inductive approach has been more flexible and open ended (Brinkmann 2014). It
helps in allowing theory in developing and providing consideration for concerning and opinions
related to people in the study. This research will select deductive approach as it helps in
providing researches with an argument required to observe both data regarding big data.
Research design is responsible to provide help in the developing the project structure that
is required for data collection as well as data analysis. Three different types of research design
are there which include explanatory, exploratory and descriptive research design (Flick 2015).
Exploratory research design focuses on exploring concept and data related to the research topic.
The main aim of the explanatory research design has been defining all factors that are related to
the research topic. Descriptive research design refers to capability of describing events that have
been occurred during accounting timeline and impact of the topic. However, only descriptive
research design help in providing conclusion to the study (George, Haas and Pentland 2014).
Therefore, the descriptive research design will be selected for the research study on impact of big
data in the banking industry.
Data collection is an important part for the project research. There are two different types
of data collection available such as primary and secondary used to conduct a research. The

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primary data collection method includes collecting raw data related to the impact of big data ion
banking sector (Ledford and Gast 2018). The primary method of data collection include survey,
experiments and observation. On the other hand, secondary data collection method include data
collection from online journals, books, articles and governmental databases. In this research,
primary data collection method has been used for collecting data and information. Online
surveys will be created using Google forms and sent to participants (Glesne 2015). There will be
close ended questions added in the survey questionnaire and sent to the participants using
internet. The population taken for the research will be 220 employees of banks. The sample size
taken for the research will be 100 employees of different banks. The sampling has been using
random non-probability sampling method. There will be 10 close ended questions added in the
survey questionnaire.
Data analysis has been of two types including qualitative and quantitative analysis
methods. For this research work the quantitative research method is nominated and applied for
the primary data collection. The data analysis is completed using Likert scale that is ranging
from 1 to 5 (Lewis 2015). Therefore, outcomes and results have been shown using charts, tables
and graphs. This has been helping in proper representation of outcomes and results.
This project will follow all the ethical consideration under the academic research rules
and regulations. Data and information will be kept secured using Data Protection Act 1998. All
private information of the survey participants have been kept safe (Mackey and Gass 2015).
There have been no tampering of data and information during data analysis method.
Transparency during the data analysis method has been kept in mind.

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BIG DATA IN BANKING INDUSTRY
Timeline
Task Name Duration Start Finish
Impact of Big data in bank
industry 91 days Wed 3/27/19 Wed 7/31/19
Study Initiation 10 days Wed 3/27/19 Tue 4/9/19
Selection of the research topic
with respective study analysis 2 days Wed 3/27/19 Thu 3/28/19
Taking approval from the
supervisor for the research topic 2 days Fri 3/29/19 Mon 4/1/19
Research plan documentation 2 days Tue 4/2/19 Wed 4/3/19
Developing research framework 2 days Thu 4/4/19 Fri 4/5/19
Submission of draft for the
research proposal 2 days Mon 4/8/19 Tue 4/9/19
Research Planning phase 35 days Wed 4/10/19 Tue 5/28/19
Assigning the research team 2 days Wed 4/10/19 Thu 4/11/19
Research resource analysis 2 days Fri 4/12/19 Mon 4/15/19
Forming research questions 2 days Tue 4/16/19 Wed 4/17/19
Developing research scope 5 days Thu 4/18/19 Wed 4/24/19
Assigning detail research
timeline 7 days Thu 4/25/19 Fri 5/3/19
Resource allocation for the 8 days Mon 5/6/19 Wed 5/15/19
Initiation of Research activities 9 days Thu 5/16/19 Tue 5/28/19
Research Development phase 7 days Wed 5/29/19 Thu 6/6/19
Identification of the research
problem 1 day Wed 5/29/19 Wed 5/29/19
Necessary media accessing 1 day Thu 5/30/19 Thu 5/30/19
Getting online library access 1 day Fri 5/31/19 Fri 5/31/19
Selection of research sources 1 day Mon 6/3/19 Mon 6/3/19
Conducting literature review 1 day Tue 6/4/19 Tue 6/4/19
Collecting needful data 1 day Wed 6/5/19 Wed 6/5/19
Secondary data collection 1 day Thu 6/6/19 Thu 6/6/19
Research data Analysis period 2 days Fri 6/7/19 Mon 6/10/19
Primary data analysis 1 day Fri 6/7/19 Fri 6/7/19
Secondary data analysis 1 day Mon 6/10/19 Mon 6/10/19
Research data evaluation phase 4 days Tue 6/11/19 Fri 6/14/19
Data evaluation 1 day Tue 6/11/19 Tue 6/11/19
Reflecting on research phases 1 day Wed 6/12/19 Wed 6/12/19
Finding and data analysis 1 day Thu 6/13/19 Thu 6/13/19
Future challenge identification 1 day Fri 6/14/19 Fri 6/14/19
Research Closure phase 4 days Mon 6/17/19 Thu 6/20/19

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Completion of research
activities 1 day Mon 6/17/19 Mon 6/17/19
Research verification 1 day Tue 6/18/19 Tue 6/18/19
Research validation 1 day Wed 6/19/19 Wed 6/19/19
Research team signoff 1 day Thu 6/20/19 Thu 6/20/19
Study Initiation 5 days Fri 6/21/19 Thu 6/27/19
Details study of the 1 day Fri 6/21/19 Fri 6/21/19
Approval of Research Topic
from Supervisor 1 day Mon 6/24/19 Mon 6/24/19
Development of Research Plan
Charter / Document 1 day Tue 6/25/19 Tue 6/25/19
Development of Research
Framework 1 day Wed 6/26/19 Wed 6/26/19
Prepare Draft Research Proposal 1 day Thu 6/27/19 Thu 6/27/19
Research Planning 7 days Fri 6/28/19 Mon 7/8/19
Formation of Research Team 1 day Fri 6/28/19 Fri 6/28/19
Analysis of Research
Requirement 1 day Mon 7/1/19 Mon 7/1/19
Identification of Research
Questions 1 day Tue 7/2/19 Tue 7/2/19
Identify Scope of Research 1 day Wed 7/3/19 Wed 7/3/19
Estimate Research Timeline 1 day Thu 7/4/19 Thu 7/4/19
Allocation of Resources and
Time for the Research 1 day Fri 7/5/19 Fri 7/5/19
Initiation of Research 1 day Mon 7/8/19 Mon 7/8/19
Research Development 7 days Tue 7/9/19 Wed 7/17/19
Determination of Research
Problems 1 day Tue 7/9/19 Tue 7/9/19
Access to Necessary Media 1 day Wed 7/10/19 Wed 7/10/19
Access to Online Library 1 day Thu 7/11/19 Thu 7/11/19
Selection of Literary Sources 1 day Fri 7/12/19 Fri 7/12/19
Literature Review 1 day Mon 7/15/19 Mon 7/15/19
Collection of Necessary Data 1 day Tue 7/16/19 Tue 7/16/19
Collection of Secondary Data 1 day Wed 7/17/19 Wed 7/17/19
Data Analysis 2 days Thu 7/18/19 Fri 7/19/19
Analysis of Primary Data 1 day Thu 7/18/19 Thu 7/18/19
Analysis of Secondary Data 1 day Fri 7/19/19 Fri 7/19/19
Research Evaluation 4 days Mon 7/22/19 Thu 7/25/19
Evaluation of Data 1 day Mon 7/22/19 Mon 7/22/19
Reflection on Research
Undertaken 1 day Tue 7/23/19 Tue 7/23/19
Documentation of Learning 1 day Wed 7/24/19 Wed 7/24/19

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Outcomes
Issues Identification and Future
Planning 1 day Thu 7/25/19 Thu 7/25/19
Research Closure 4 days Fri 7/26/19 Wed 7/31/19
Complete All Activities in
Research 1 day Fri 7/26/19 Fri 7/26/19
Documentation of Entire
Research 1 day Mon 7/29/19 Mon 7/29/19
Validation of the Research and
Learning 1 day Tue 7/30/19 Tue 7/30/19
Team Sign Off 1 day Wed 7/31/19 Wed 7/31/19
Table 1: Timeline
(Source: Created by Author)

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References
Alalwan, A.A., Dwivedi, Y.K. and Rana, N.P., 2017. Factors influencing adoption of mobile
banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of
Information Management, 37(3), pp.99-110.
Amado, A., Cortez, P., Rita, P. and Moro, S., 2018. Research trends on Big Data in Marketing: A
text mining and topic modeling based literature analysis. European Research on Management
and Business Economics, 24(1), pp.1-7.
Barrdear, J. and Kumhof, M., 2016. The macroeconomics of central bank issued digital
currencies.
Brinkmann, S., 2014. Interview. In Encyclopedia of critical psychology (pp. 1008-1010).
Springer New York.
Buchak, G., Matvos, G., Piskorski, T. and Seru, A., 2018. Fintech, regulatory arbitrage, and the
rise of shadow banks. Journal of Financial Economics, 130(3), pp.453-483.
Carneiro, N., Figueira, G. and Costa, M., 2017. A data mining based system for credit-card fraud
detection in e-tail. Decision Support Systems, 95, pp.91-101.
Corbae, D. and D'Erasmo, P., 2019. Capital requirements in a quantitative model of banking
industry dynamics (No. w25424). National Bureau of Economic Research.
Flick, U., 2015. Introducing research methodology: A beginner's guide to doing a research
project. Sage.
George, G., Haas, M.R. and Pentland, A., 2014. Big data and management.

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Glesne, C., 2015. Becoming qualitative researchers: An introduction. Pearson.
Ledford, J.R. and Gast, D.L., 2018. Single case research methodology: Applications in special
education and behavioral sciences. Routledge.
Lewis, S., 2015. Qualitative inquiry and research design: Choosing among five
approaches. Health promotion practice, 16(4), pp.473-475.
Mackey, A. and Gass, S.M., 2015. Second language research: Methodology and design.
Routledge.
Panneerselvam, R., 2014. Research methodology. PHI Learning Pvt. Ltd..
Smith, J.A. ed., 2015. Qualitative psychology: A practical guide to research methods. Sage.