Central Bank of Malta: Big Data Application, Security & Policies

This report explores the application of big data within the Central Bank of Malta, highlighting the need for improved managerial and functional services. It details the bank's background, existing data flow, and proposed changes with the implementation of big data tools, specifically Hadoop. The report suggests a suitable platform, addresses security and privacy concerns, defines data management policies, and proposes big data analytics and virtualization methods to enhance the bank's operational efficiency and data security. It emphasizes the importance of data backup, disaster recovery, and robust security measures to protect financial and confidential data. The analysis also includes recommendations for addressing security challenges, establishing data management protocols, and implementing virtualization policies and digital auditing.

Running head: BIG DATA APPLICATION
Big Data Application: Central bank of Malta
Name of the student:
Name of the university:

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Table of Contents
1. Overview......................................................................................................................................2
1.1 Background of the finance industry: Central Bank of Malta.................................................2
1.2 Describing the data flow in the company..............................................................................3
1.3 Selection of suitable platform for big data application..........................................................5
1.4 Developing suggestion for addressing security and privacy issues.......................................5
1.5 Defining the data management policies.................................................................................5
1.6 Suggestion for big data analytics and virtualization method to be used................................6
References........................................................................................................................................7

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1. Overview
This report depicts the importance of implementing big data application in the business
industry. Application of big data is helpful in different industries including hospitality, banking,
finance etc. It helps the organization to gain effective and measurable revenue from the
competitive marketplace. Proper understanding of consumer’s desire is one of the most
important factors that are to be analyzed (Inukollu, Arsi and Ravuri 2014). With the help of big
data tools the customers would be able to make predictive analysis on big data and its application
in the financial industries. In order to structure this particular paper the financial rather banking
sector that has been nominated is “Central bank of Malta”.
During analysis of the existing managerial and functional services it has been found that
the operation served by the bank is very weak and those are needed to be improved. With the
help of Big data tool this particular application could be developed. The existing data flow of the
company and after the implementation of the Big Data tools in the finance sector the changes
those might raise are elaborated in this report. In order to implement big data tools proper
platform is also required to address and resole the security and privacy issues. Proper data
management policies and virtual methods that has to be applied are elaborated in this paper.
1.1 Background of the finance industry: Central Bank of Malta
Considering the central bank of Malta Act, in the year of 1968 this bank was established
in Malta but in 2004, this bank develops its partnership with Europe System of Central Bank. In
2008, this bank become a part of the Eurosystem however, the bank serves their service in Malta
and even out of Malta as well.

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The leading objective of the finance industry is to maintain the stability of price
appropriately (Zicari 2014). In addition to this, the additional missions for the central bank of
Malta are to deliver a secured, on time and satisfactory services to the consumers of Malta.
However, due to lack of technical applications and functionalities this industry is facing
challenges from the existing service providers. Thus, those applications are needed to be done
properly to resolve the continuously occurring issues. Different big data tools are available in the
market and based on the type of business details the company is required to incorporate proper
big data tool or their organization. In traditional days, this bank used to have manual information
management which is not at all satisfactory (Liu, Liu and Ansari 2014). The rate of security and
privacy issues were increasing eventually with big data application tools. In this approach the
data holding capacity of the server was seemed to be very weak that is needed to be improved
accordingly. After experiencing the big data discovery the operation of the company is expected
to be improved. For this particular finance sector the big data tool that has been selected is the
Hadoop tool.
1.2 Describing the data flow in the company
The traditional data flow systems used by the finance sectors are very much weak from
the security perspectives. That time the finance sectors use paper written documents to develop
and fulfill the customer centric purposes. In traditional banking the customer are allowed to open
bank accounts in the banks and they had power to make savings by depositing money in the local
banks (Chen and Zhang 2014). Through checks and counter payments the consumers could have
withdrawal money from their accounts. Due to lack of security surveillances many consumers
have to compromise with this system. The services were not open for 24X4 hours and regardless
of time and location of the consumers, they are unable to provide services to their consumers.

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However, after the implementation of the E-banking approaches the additional changes
those have been incorporated are as follows:
E-commerce is divided in two different parts such as E-finance and E-money. Again the
process of E-finance is divided into two subparts such as E-banking and other financial services
lie online brokering, insurance etc (Sagiroglu and Sinanc 2013,). On the other hand the E-
banking process in divided in three subsections such as internet banking, telephone banking and
electronic delivery channels. The customers are allowed to withdraw money and access different
information from their banking service providers with the help of big data (Grover et al. 2015).
The additional operational excellences those are incorporated to the finance system after
implementing big data solution to it are as follows:
Data backup: In case if due to lack of technical support if any of the information get
destroyed from the information server then with the help of big data tool (Suthaharan 2014). Big
data tools are capable to recover important information from the server whenever required.
Disaster recovery: Big data provides wide storage to the consumers to serve the
operational requirements to their consumers. Based upon the I/O channel capacity the data
backup and information restoration capacity would be building up accordingly.
Security: The system would serve high level security in terms of system authentication
and authorization which means that without permission none of the unwanted users would be
able to access financial and other confidential data from the server (Inukollu, Arsi and Ravuri
2014).

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1.3 Selection of suitable platform for big data application
For this particular company Hadoop big data tool is suggested to be used to get more
direction for searching security and service patterns. In order to expand the system functionalities
for deriving values out of the big data the technologies has developed Hadoop tool. The benefits
of Hadoop platform are as follows:
 High scalability
 Fast service providing ability
 Faster system processing
 Resilient on failure
1.4 Developing suggestion for addressing security and privacy issues
I order t resolve the security challenges the set of suggestions those have been developed
are as follows:
 Each potential harmful action like discrimination concerns should be addressed
 The uneven policy environment must be highlighted
 Proper encryption algorithm should be incorporated to avoid security issues
 De-identification and re-identification is needed to protect healthy data set
1.5 Defining the data management policies
Proper policy framework for big data application is needed to be addressed to avoid
security level issues. The policies are as follows:
 Data sources should be identified
 Accurate data management protocols are required to be established

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 Virtualization policies, digital auditing and data analytics must be used
(Suthaharan 2014).
 Infrastructure, change management and capacity building are needed
1.6 Suggestion for big data analytics and virtualization method to be used
 Suitable visual noise reducer should be incorporated to the system
 Proper authentication methods are needed to be used
 File encryption must be used accurately (Zicari 2014)
 Key management system and secured communication is needed to be
implemented to control server access from the external attackers.

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References
Assunço, M.D., Calheiros, R.N., Bianchi, S., Netto, M.A. and Buyya, R., 2015. Big Data
computing and clouds: Trends and future directions. Journal of Parallel and Distributed
Computing, 79, pp.3-15.
Augustine, D.P., 2014. Leveraging Big Data analytics and Hadoop in developing India’s
healthcare services. International Journal of Computer Applications, 89(16), pp.44-50.
Chen, C.P. and Zhang, C.Y., 2014. Data-intensive applications, challenges, techniques and
technologies: A survey on Big Data. Information Sciences, 275, pp.314-347.
Grover, M., Malaska, T., Seidman, J. and Shapira, G., 2015. Hadoop Application Architectures:
Designing Real-World Big Data Applications. " O'Reilly Media, Inc.".
Inukollu, V.N., Arsi, S. and Ravuri, S.R., 2014. Security issues associated with big data in cloud
computing. International Journal of Network Security & Its Applications, 6(3), p.45.
Kim, G.H., Trimi, S. and Chung, J.H., 2014. Big-data applications in the government
sector. Communications of the ACM, 57(3), pp.78-85.
Liu, J., Liu, F. and Ansari, N., 2014. Monitoring and analyzing big traffic data of a large-scale
cellular network with Hadoop. IEEE network, 28(4), pp.32-39.
Sagiroglu, S. and Sinanc, D., 2013, May. Big data: A review. In Collaboration Technologies and
Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.
Shang, W., Jiang, Z.M., Hemmati, H., Adams, B., Hassan, A.E. and Martin, P., 2013, May.
Assisting developers of big data analytics applications when deploying on hadoop clouds.

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In Proceedings of the 2013 International Conference on Software Engineering (pp. 402-411).
IEEE Press.
Suthaharan, S., 2014. Big data classification: Problems and challenges in network intrusion
prediction with machine learning. ACM SIGMETRICS Performance Evaluation Review, 41(4),
pp.70-73.
Zicari, R.V., 2014. Big data: Challenges and opportunities. Big data computing, 564.