A Review Paper on the Application of Big Data by Banking
Institutions and Related Ethical Issues and Responses
Victor Chang
, Lina Xiao
, Qianwen Xu
and Mitra Arami
School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, U.K.
IBSS, Xi’an Jiaotong-Liverpool University, Suzhou, China
PARDIS Ltd, London, U.K.
Keywords: Big Data, IoT, Banks, Ethical Issues of Big Data, Suggested Solutions.
Abstract: Nowadays, Big Data and the Internet of Things (IoT) are one of the most popular topics. This review paper
demonstrates an overview of the application of Big Data and IoT in banking institutions. In the beginning, a
brief definition of Big data and IoT is provided and the integration of technologies by banks is illustrated.
Then, this paper explains the potential sources where banks could generate Big Data. Next, the major works
that banking institutions use Big Data are listed. In the final two parts, some acute ethical concerns are raised
and appropriate solutions are suggested for the banking industry and other organizations.
Due to the financial crisis and its impacts between
2007 and 2009, the public has realized the importance
of corporate to disclose financial and non-financial
data regularly and adequately. Central banks and
regulators should take responsibility to make
objective evaluations and implement strict
monitoring. This practice indicates for many parties
consisting of firms, central banks, regulators, and so
on, they have to deal with massive and complicated
data in a limited time. The era of Big Data enables a
sharp increase in different forms of data such as client
data, trade figures, health data, management data, etc.
From the picture below, Big Data has been widely
used in all walks of life, such as marketing, HR,
healthcare, supply chain, agriculture, finance, and so
on (Invested Development, 2015; Marr, 2015;
Stackowiak et al., 2015). Therefore, it is essential for
businesses to know how to develop Big Data to
improve their business operations, processes,
communications and opportunities.
Figure 1: The era of Big Data employed.
It is a universal truth that Big Data is deemed as a
competitive advantage and organizations could
benefit a lot from these advanced technologies. For
example, for the meteorological department and
agricultural sector, Big Data can be utilized for
disaster prediction. For firms, they may make use of
Big Data to understand consumer behavior and
increase their sales revenue. For banking institutions,
they harness the ability of Big Data to avoid risk and
better serve their customers. It is worth noting that
Big Data itself is neutral with no harm. However, the
negative impacts of Big Data may come from the
Chang, V., Xiao, L., Xu, Q. and Arami, M.
A Review Paper on the Application of Big Data by Banking Institutions and Related Ethical Issues and Responses.
DOI: 10.5220/0009427701150121
In Proceedings of the 2nd International Conference on Finance, Economics, Management and IT Business (FEMIB 2020), pages 115-121
ISBN: 978-989-758-422-0
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
intentions, methods and people who use it. Overall,
this paper discusses the role of Big Data and IoT in
banking industries and highlights some ethical issues
later. Several responses from different perspectives
are also put forward within the context to address
those concerns.
In recent years, firms, governments and other sectors
have been deployed Big Data partly due to the
inherent limitation of traditional analytical tools. That
is, traditional data processing methods are inadequate
to process pretty large and complex data sets. Grable
and Lyons (2018) stated that “when analyzed
computationally, Big Data can provide more precise
insights into hidden patterns, trends, and associations,
especially in the context of human decision making”
(p.17). In the early 2000s, Doug Laney (2001) defined
Big Data, including three concepts: volume, variety,
velocity. Based on his original work, other concepts
have been complemented: veracity and value (Grable
and Lyons, 2018). Bholat (2015) claimed that even in
the banks, data are generated of high volume, high
velocity and diverse in form.
Generally, IoT is considered as the ‘third
industrial revolution’. The term was explained as “a
conceptual framework, which involves embedding
connectivity and intelligence across a wide range of
devices over the cloud” (Dutta and Ghosh, 2018, p.
1). Saxena and Ali Said Mansour Al-Tamimi (2017)
argued that it enables better monitoring and
interaction. The following picture vividly depicts a
relationship between Big Data and IoT in terms of the
business model maturity index. Briefly, IoT produces
large volumes and complex, diversified data and Big
Data assistants IoT. Additionally, Big Data cannot do
without cloud computing. Therefore, IoT, Big Data
and cloud computing are the basis of each other.
Figure 2: Big Data/IoT Business Model Maturity Index.
According to Saxena and Al-Tamimi (2017), the
banking industry may combine Big Data and IoT
technologies altogether to establish a more robust
framework. IoT technologies have promoted the
innovation of financial service, improve interaction
with clients, help optimize the structure of
organizations and even assist in designing better
business models. Therefore, IoT could be tapped by
banks to improve their customer relationship
management. In case of theft or misplace of
customers’ plastic cards, IoT technologies come in
handy to find lost objects. Big Data can be generated
by IoT technologies in their own turn and data sharing
could be facilitated by IoT. Overall, banks’ efficiency
can be significantly improved and customer trust can
be built through the application of Big Data analytics
and IoT technologies.
Figure 3: Three potential sources of Big Data for banks.
Big Data can be generated by banks in many ways.
As shown in the picture, offline and online channels
and social banking activities are the main sources of
Big Data for banks. Generally, offline banking mode
is the most common method to manage customer
relationships, such as customers visiting the bank
physically. Except for the offline channel, banks
could also transact with customers via online modes
such as internet banking, telephone banking, ATM,
WAP-banking and other means (Cheng et al., 2006).
Besides, customer relationships may be forged
through banking activities on social networking
media like Wechat or Weibo, as Ghazinoory et al.
(2016) said that “social banking” allows customers to
be more involved and their behavioral features can be
identified through insights from social data. For
instance, a Wechat Subscription called “ShanRong”,
the e-commerce platform of CCB, releases the news
of the latest and popular promotion for users. Data
analysts investigate user behavior characteristics
FEMIB 2020 - 2nd International Conference on Finance, Economics, Management and IT Business
based on massive data and finally, three outcomes can
be achieved. Firstly, operators could make timely
optimization decisions by evaluating the
attractiveness of functions and marketing activities
introduced by ShanRong subscription to users.
Secondly, it can implement real-time monitoring,
active forewarning for online fraud, and timely
detection of inhuman ticket brushing and intrusion.
Thirdly, formulate personalized online marketing
activities and push individual notifications through
the precise clustering of customers. Thus, these social
banking activities are again a potent source of Big
Data, in the sense that banks are appraised of the
needs and requirements as well as feedback and
grievances regarding various issues.
Banks utilize Big Data mainly to intensify their risk
management frameworks to become more transparent
and auditable (Srivastava and Gopalkrishnan, 2015).
According to The Economist (2012), high-
performance hardware and software are used by
financial services to investigate complicated patterns
of fraud within unstructured data. Nunan and
Domenico (2013) argued that the application of Big
Data "has enabled the cost-effective provision of
financial services in areas that would previously have
been regarded as too risky to be sustainable". For
instance, before a statement is issued, credit card
issuers usually apply Big Data analytics to detect a
cardholder's fraudulent behavior. Moreover,
Srivastava and Gopalkrishnan (2015) demonstrated
that data analysis is useful to identify and assess
finance crime management solution rules by
detecting the correlation between financial crimes
and transactions' attributes in advance. The
followings are specific ways.
Figure 4: Ways to manage risk by utilizing Big Data.
In addition, by using Big Data, banks are able to
improve their marketing strategies. The data helps
them to profile and categorize their customers and
then to learn what the customer’s need and predict
their behavior. In this way, it becomes easier for
banks to identify their potential customers or provide
better service for their existing customers (Hassani,
Huang & Silva, 2018).
Due to the immense commercial and social value that
Big Data may bring, today, people consider Big Data
as a competitive advantage for financial institutions
and other sectors. However, a range of ethical
concerns may arise as a matter of time when using
and dealing with Big Data technologies. More
specifically, as Bratu (2018) said that Big Data
analytics covers a series of processes, including data
acquisition, storage, distribution, evaluation, and
information implementation. Hence, the concerns
relate not only to the gathering, retention of data and
its security, but also with its analysis and
interpretation by data scientists, and with the
commercial trade of personal data. This paper is
going to investigate, depict, and assess several ethical
concerns, and then relevant suggestions may be
proposed later.
5.1 Privacy and Consent
Bratu (2018) argued that breaching and invading
privacy and information suppliers’ approval are the
most commonplace ethical topics at a challenging
level. The right to privacy attracts much attention and
debate from the public regarding the usage of Big
Data. Privacy is quite vital to be protected as a human
right because it is tremendously beneficial to
individuals and society as a whole (Nersessian, 2018).
When ones data is to be harvested and stored,
notification should be explicitly conveyed and the
informed consent should be obtained. Fuller (2017)
found that in practice, most service providers offer
information, generally in a written text form, to users
to obtain their consent. Then, a form needs signing,
or a box needs to be ticked by users to confirm that
they understand and accept all the terms represented
to them.
In practice, however, Wilbanks (2014) pointed
out that this is a process intentionally limiting the
liabilities of the parties who obtain users’ information
rather than sincerely notify the data subjects.
Consequently, the ability of users whose data are
gathered tends to be imperceptibly minimized. To
conclude, there are three main practical difficulties in
managing privacy and acquire informed consent
(Solove, 2013). Firstly, for those who are not aware
A Review Paper on the Application of Big Data by Banking Institutions and Related Ethical Issues and Responses
of the importance of privacy, they tend to ignore
relevant policies. Secondly, some people
acknowledge the significance and read the terms but
do not understand them. Thirdly, although some
people understand the privacy policies literally, they
may make unwise choices due to insufficient
background knowledge.
5.2 Security Problem
Since the banking industry has gradually achieved the
network, banks have always been the coveted objects
by hackers as they are capital-intensive areas.
Landwehr (2014) emphasized that customers’ data
should be kept complete and be protected from
accidental or intentional threats. Accordingly, he
proposed strategies to handle such threats through
prevention, detection, reply and recovery. There is no
doubt that it is data handlers’ responsibility to prevent
the data from exposure, misuse, or cyber-attack.
However, establishing a robust system is equally vital
because not every disaster may be foreseeable. These
systems can assist in detecting attacks and responding
to them timely and appropriately. Besides, other
suitable new safeguards should be in place if
Nevertheless, Fuller (2017) mentioned that
whatever means are taken, hacked computers and
leakage of confidential data have frequently been
happening, which indicates that breaches of security
will always be a tricky problem. Recently, HSBC was
exposed that hackers attacked its customer accounts
between 4 October and 14 October in 2018 (Davis,
2018). Consequently, around 1% of American
customer personal information was leaked out.
Besides, in May 2016, managers and employees of
Agricultural and Commercial Bank and China CITIC
Bank illegally sold customers’ personal credit reports,
involving 2.57 million personal information items.
5.3 Commercial Usage of Customer
Big Data may possess commercial usefulness in
various ways. For example, Fuller (2017) claimed
that a website or a store card could intelligently record
information about a user’s previous purchase. With
the footprint left by consumers, firms may carry out
precision marketing, target promoting and persuade
consumers to purchase further. Likewise, banks also
make commercial use of Big Data to recommend
appropriate wealth management products to specific
customer groups. On the one hand, many people
acknowledge that the commercial usage of data helps
them to access the products they might want and like
in a more convenient way. However, on the other
hand, as people’s data generate a huge financial gain,
there is a tendency for firms to sell personal data for
profit, even illegally. It is necessary and urgent to
have some effective regulations around us in case
personal data is used for evil and illegal purposes.
Nevertheless, Kitchin (2014) pointed out that
currently, few particular laws and codes have been
formulated to regulate data brokers and their power
has not been limited to a reasonable level yet. Thus,
regard to this field, effective and timely actions
should be taken, such as imposing relevant ethical or
legal restrictions upon data brokers before this
problem is more acute and severe.
5.4 Unfairness
Big Data has been widely used by financial
institutions such as banks, trust companies in
assessing people's suitability for loans. As O'Neil
(2016) investigated that this may aggravate the
existing social inequalities. For instance, for an
individual who has a poor credit record and lives in a
poor neighborhood, it is probable for institutions to
judge that person as an inappropriate candidate for a
loan application or charge him or her higher
premiums of insurance. This practice may deprive the
opportunities of the poor and reinforces their existing
poverty. Therefore, it is unreasonable and inequitable
for banks to use Big Data to categorize and sort
people as this may further deepen social unfairness to
a new level.
It is worth investigating the Big Data’s role in
accessing financial services in China. Compared with
developed countries, China's financial services
penetration rate is lower (Kshetri, 2016). Kshetri
(2016) also highlighted that, especially for low-
income families and small and midsize enterprises
(SMEs), the low penetration problem is more acute
and severe than for high-income households and large
firms. Klein and Cukier (2009) claimed that SMEs in
China contribute to 70% of GDP, but only 20% of
financial resources are available to them. What is
more, around 89% of Chinese SMEs face barriers in
meeting banks’ requirements when applying for loans
(Jing, 2014). Two main reasons are explainable for
this situation. Firstly, borrowing loans to poor people
and micro-enterprises will bring traditional banks
higher transaction costs and inconvenience of
processes, so banks are usually reluctant to serve
these borrowers. Secondly, information opacity is
another reason to interpret why the weak and small
FEMIB 2020 - 2nd International Conference on Finance, Economics, Management and IT Business
businesses have difficulty in reaching financial
services and other favorable policies.
Kshetri (2014) claimed that information opacity is
partly since Chinese credit rating agencies could not
provide adequate and complete information on SMEs'
creditworthiness. This practice may perhaps be
because certified audited financial statements are not
compulsory for those micro firms. However, it has
become crucial for banks to minimize their lending
risks based on reliable and transparent credit
information. Kunt and Maksimovic (2004) found that
as foreign banks grasp less credit information of
Chinese people and SMEs than banks in China, they
are more cautious even reject those "opaque"
borrowers' loan service applications. Big Data may
help increase chances for Chinese low-income
families and micro-enterprises to obtain financial
services via evaluation and analysis of potential
borrowers’ creditworthiness and reduction of
transaction costs. Nevertheless, the unfairness cannot
be eliminated in a short time.
5.5 Data Cleaning, Analysis and
Presentation of Results
Except for the ethical concerns discussed above,
another issue also captures people’s attention that is
the data clean, analysis and presentation of the
outcomes by data analysts. As Fuller (2017) argued
that data expert s’ one role is to be taleteller. That is,
before presenting persuasive final results, data
scientists need to carry on a series of data processing,
including data cleaning. In people’s minds, Big Data
is objective and just “is”, but the data processing may
embed various biases and human factors’
interference. First, each data does not exist without
ground. It could be obtained in different ways such as
data acquisition, data sharing even cyber-attack
illegally. Biases may be introduced in these ways at
the same time. Second, data cleaning is usually
implemented before detailed analysis conducted.
Data experts have discourse power to determine the
approach to handle the missing data, such as imputing
missing variables, transforming variables and
removing outliers. However, data cleaning and
analysis processes are rarely documented. The
experts’ choices on the approaches may influence the
analysis, interpretation and presentation in the next
steps. The influence is long-term and it is not limited
to the one-step but the whole progress (Borgman,
2015). Third, the specific tools adopted in the data
analysis process may incorporate preconceptions.
The algorithms employed to carry out the analysis of
the data are given specific values and are embodied in
specific scientific methods (Kitchin, 2016). Fourth,
aside from the tools and algorithms used to analyze
the data, biases may also result from data scientists
themselves. In order to take responsibility for the
interpretation result and try to minimize the biases,
the analysts have to identify the influence of their
analyzing experience and opinions in their
interpretation (Boyd and Crawford, 2012). Last but
not least, apart from the biases resulting from the
analysis and interpretation, the presentation of final
results from analysis deserves attention as well. In the
presentation, analysts commonly use tables, graphs,
charts, diagrams, etc. to realize data visualization.
However, some conscious or unconscious biases may
result from these visualizations, which might
encourage readers to see and understand results in
particular ways. According to Gitelman and Jackson
(2013), using different visualization methods in the
presentation may produce different effects.
Additionally, in that the decision-makers will make
decisions on the presentation of the analysis results,
the presenters need to present them properly.
The ethical issues discussed above are somewhat
complicated and overlapping. Each of them should be
treated seriously by taking appropriate actions.
Broadly, solutions are proposed from three
perspectives: technical, legal and ethical.
6.1 Technical Solutions
Regard to some ethical issues raised before, like
security problems, technological development and
innovation may assist in addressing. That is,
continuously update high-performance hardware and
software systems that store and process Big Data. For
instance, in terms of frequent cyber-attacks of banks,
as advised by Fuller (2017), the security holes can
partly be handled through continuous oversight and
strengthening infrastructure construction and
development. Overall, integration of experienced
data handlers and a robust framework can mitigate the
security problems to a great extent. As data science is
an advanced domain with rapid changes, so relevant
techniques to improve security will change and
develop correspondingly. Similarly, for those who
deliberately damage security will transform means
and find new coping ways as well. Ultimately, at
least, notifications of the risks from technical
limitations, failures and security breaches should be
clearly conveyed to people who may be influenced.
A Review Paper on the Application of Big Data by Banking Institutions and Related Ethical Issues and Responses
Besides, acquiring interested parties’ consent to those
risks is necessary.
6.2 Legal Solutions
Many legal and regulatory methods are formulated to
deal with plenty of matters. Nevertheless, there are
still some challenges that should not be
underestimated. Among them, the most three acute
issues are explained below. Firstly, as Big Data, its
analysis and its commodification change so rapidly
and frequently, it is urgent to develop corresponding
legislations in time (Fuller, 2017). If legislations lag
behind current practices, not only these legal and
regulatory means would be old-fashioned, but also
bad people will make unbridled attacks and individual
rights and interests cannot be guaranteed promptly.
Secondly, although relevant legislation already exists
in some circumstances, in reality, they are not feasible
or/and maybe routinely overlooked. In such cases,
laws must be adapted to make sure they are
meaningful, believed and enforceable. Thirdly,
making applicable and global legislation is crucial.
Actually, each country enacts laws and regulations
based on its conditions and differentiation may exist
among different nations. However, there is no border
restricting data, so it is necessary to coordinate and
cooperate between countries to produce some agreed
international standards. These significant
undertakings may consume much cost, time and
effort, but they are undoubtedly crucial and deserve
these valuable resources.
6.3 Ethical Solutions
The financial crisis reflected the absence of
professional ethics and low moral standards. After
that, banking supervisors and politicians have been
aware that the corporate governance system and
financial institutions’ ethical culture play a vital role
in the occurrence and development of the crisis. That
is, poor performance in corporate governance and
unethical behavior of management and employees are
partly responsible for the financial crisis.
Consequently, the global financial stability and social
welfare will be influenced negatively. For instance,
Enron’s managers colluded with Arthur Andersen to
manipulate accounts, which indicated the deficit of
professional ethics. The incident caused both Enron
and Andersen to suffer from a terrible loss of
reputation (Markham, 2015). Thus, for financial
institutions, including the banking industry, ethical
status is rather significant. In order to improve the
overall level of financial practitioners’ ethics, first
and foremost, board characteristics count. Baselga-
Pascual et al. (2018) demonstrated that within an
organization, the board of directors (BOD) is
considered as the most vital internal governance
mechanism because formulating and monitoring the
ethical culture of the whole organization is BOD’s
responsibility. Baselga-Pascual et al. (2018)
investigated a positive relationship between ethical
reputation and board characteristics. More generally,
the larger board size, more diversified gender, and
CEO duality may contribute to more effective
monitoring and oversight. However, if board
members are too busy to have regular meetings, poor
monitoring and a low ethical reputation may be
formed. Therefore, it is crucial for financial
institutions to make a balance in the structure of
BOD. Additionally, BOD themselves are required to
conduct ethically and establish appropriate and
feasible ethics codes for employees. Last but not
least, regular evaluation of the company’s ethical
atmosphere and strict monitoring of employees'
behavior ought to be in place.
Besides, data scientists and employees have been
advised to undertake an oath of practicing specific
ethical codes, showing they fully understand those
standards and are willing to take responsibility and
accept the oversight from others. However, in order
to ensure the effectiveness, two requirements should
be satisfied. One thing is that all data scientists must
comply with the oath and everyone is equal before the
regulations. The other is that severe punishment
measures like penalties would be acted on any data
analysts who breach of it.
To sum up, in this review paper, we give a brief
introduction to Big Data and IoT. Then we focus on
the usage of Big Data and IoT in banking institutions
through analyzing the potential sources where banks
could acquire Big Data and the major areas that banks
adopt data. From these two parts, we find that the
banking industry frequently generates Big Data in
three ways. They are offline channels, online channel
and social banking activities. Generally, the banking
industry makes use of Big Data to reinforce risk
management frameworks to reduce financial risks. In
addition, Big Data can also be utilized to develop
more accurate marketing strategies, reduce
transaction and to operate costs and provide better
service to consumers. Next, we list some ethical
issues related to the application of Big Data such as
privacy and consent, security problems, commercial
FEMIB 2020 - 2nd International Conference on Finance, Economics, Management and IT Business
usage, unfairness and data cleaning, presentation
process. Finally, several responses are provided from
technical, legal and ethical aspects. On the whole, Big
Data is a strategic resource if it is used legally and
adequately by organizations. We expect Big Data
could benefit people and the whole society to the
greatest extent.
We are grateful to VC Research to support this
research, with grant number VCR 0000026.
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A Review Paper on the Application of Big Data by Banking Institutions and Related Ethical Issues and Responses