Dissertation on Information Technology: Exploring Opportunities and Challenges of Artificial Intelligence in the Finance Industry

In this dissertation we will discuss about dissertation on Information technology and below are the summaries point:-  Chapter 1: Introduction provides background information, problem statement, research aim, objectives, and significance. Chapter 2: Literature Review explores the opportunities and challenges of artificial intelligence in the finance industry. Chapter 3: Research Methodology discusses the research onion and research philosophy.

Running head: DISSERTATION ON INFORMATION TECHNOLOGY
Dissertation on Information Technology
Name of the Student
Name of the University
Author Note

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Table of Content
CHAPTER 1: INTRODUCTION..............................................................................................5
1.1 Introduction......................................................................................................................5
1.2 Background to the study...................................................................................................6
1.3 Problem Statement...........................................................................................................8
1.4 Research Aim and Objectives..........................................................................................9
1.5 Research Questions..........................................................................................................9
1.6 Research Significance....................................................................................................10
1.7 Research Outline............................................................................................................11
1.8 Summary........................................................................................................................12
CHAPTER 2: LITERATURE REVIEW.................................................................................13
2.1 Introduction....................................................................................................................13
2.1 Artificial Intelligence: Opportunities and Challenges in finance industry.....................13
2.2 Banking institutions in UAE making a big leap in technology advances......................15
2.3 Reproducibility Crisis in Artificial Intelligence.............................................................16
2.4 A depth impact on banking sector employment awaits.................................................17
2.5 Embedding technology thinking into financial services................................................18
2.6 Fintech Partnership.........................................................................................................20
2.7 Need for an agile approach............................................................................................21
2.8 Trends that Artificial Intelligence is helping the financial institutions..........................22
2.9 Ways how Artificial Intelligence transformed finance industry....................................24
2.10 Risks associated with AI..............................................................................................26

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2.11 Gaps in the review........................................................................................................28
..................................................................................................................................................29
CHAPTER 3: RESEARCH METHODOLOGY.....................................................................30
3.1 Introduction....................................................................................................................30
3.2 Research Onion..............................................................................................................30
3.3 Research Philosophy......................................................................................................30
3.4 Research Approach........................................................................................................31
3.5 Research Design.............................................................................................................32
3.6 Data collection methods.................................................................................................33
3.7 Sampling method...........................................................................................................34
3.8 Ethical Consideration.....................................................................................................36
CHAPTER 4: FINDINGS AND ANALYSIS.........................................................................37
Chapter 5: Conclusion and Recommendation..........................................................................58
5.1 Conclusion......................................................................................................................58
5.2 Recommendation............................................................................................................60
References................................................................................................................................64

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List of Table
Table 1: Artificial Intelligence has positive impact on banking organizations in UAE..........37
Table 2: AI technologies for performing financial activities...................................................38
Table 3: Effectiveness of AI in financial institutions...............................................................40
Table 4: Fintech is a proper solution for contemporary financial methods.............................42
Table 5: AI is affecting financial institutions in UAE.............................................................43
Table 6: AI in banking operation is affecting the human skills in the banking sector.............45
Table 7: Agile approach is required for facilitating financial operation..................................46
Table 8: Activities AI for financial operation in Emirates NBD bank....................................48
Table 9: Use of AI technologies is a risk consumers with respect to privacy.........................50
Table 10: Risks that are presently alarming for Emirates NBD Bank.....................................51
Table 11: Significant opportunity that AI brings about...........................................................53
Table 12: Customer service can be enhanced by the use of AI in the banking sector.............55
Table 13: Common advantages of using AI in banking sector................................................57
Graph 1: Artificial Intelligence has positive impact on banking organizations in UAE.........37
Graph 2: AI technologies for performing financial activities..................................................39
Graph 3: Effectiveness of AI in financial institutions..............................................................40
Graph 4: Fintech is a proper solution for contemporary financial methods............................42
Graph 5: AI is affecting financial institutions in UAE............................................................43
Graph 6: AI in banking operation is affecting the human skills in the banking sector............45
Graph 7: Agile approach is required for facilitating financial operation.................................47
Graph 8: Activities AI for financial operation in Emirates NBD bank....................................48
Graph 10: Use of AI technologies is a risk consumers with respect to privacy.......................50
Graph 10: Risks that are presently alarming for Emirates NBD Bank....................................51
Graph 11: Significant opportunity that AI brings about..........................................................53

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Graph 12: Customer service can be enhanced by the use of AI in the banking sector............55
Graph 13: Common advantages of using AI in banking sector...............................................57

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Project Title: “Risk Assessment, Future Opportunities and Benefits of Implementing
Artificial Intelligence within Retail Credit Center and Disbursal Units – An Impact Study at a
leading UAE Based Bank, Dubai”
CHAPTER 1: INTRODUCTION
1.1 Introduction
This research project is a detailed risk assessment, future opportunities and benefits of
implementing Artificial Intelligence within Retail Credit centre and disbursal units. In order
to perform the study with the real-world data, leading based banks of Dubai have been
considered in the analysis. In a general view, artificial intelligence is more of a computer
science which aims to create intelligent machines and it has been a necessary and crucial part
of technology sector. Artificial Intelligence is rather a stimulation of human intelligence
process by machines particularly the computer system and these processes could include
learning, reasoning and self-correction. Particular application applications of AI could
include expert system, speech recognition and machine vision. It is fundamentally composed
of man-made efforts supported by coding, structuring and implementing it within an IT
system. Hence, the fundamental part of AI is more of designing and processing of
knowledge.
Even though there is a constant growth in the field of AI, bunch of questions appear
doubting the growth of AI in the field of businesses. One of the questions is whether the
banks and credit unions still be consumers’ fundamental financial institutions. It is worth
telling that shifting consumer attitude is one the significant changes that banking industry
must have to acknowledge and deal with to remain relevant. It has been identified that for
financial institutions, the upward growth of AI represents $1 trillion in projected cost saving

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and it is in general predicted that by 2030 conventional financial institutions could shave 22%
in cost. When it comes to the use of AI in the banking sector, some significant applications of
AI should not go unnoticed. It is worth mentioning that AI-enabled devices are already using
both sound and vision to collect information more appropriately compared to humans and
software continues to get more human-like.
So, on the basis of the above facts, it is worth telling that growth of AI is inevitable.
However, it is necessary to know how AI is actually impacting Retail units. In addition to
this, it should also be noted that as the market has been dynamic in nature, threats and
opportunities of using AI are two significant possibilities. Thus, the purpose of the research
project is to analyse and assess opportunities and benefits of implementing AI in retail credit
units and alongside analyse the threats and risks associated with the growth of AI in the
financial sector. The study is based on the leading UAE based banks of Dubai. This chapter
of the dissertation provides a detailed background about the emergence and growth of AI in
the banking sector of Dubai and the possible challenges that banking organizations might
face simultaneously with the journey of AI in businesses have been presented. This
introductory chapter of the dissertation also provides clear aims and objectives on the basis of
which review of literature has been established in the following chapter.
1.2 Background to the study
It has been identified that the concept of non-living object coming to life as intelligent being
has been around for very long time. Ancient Greeks had several myths regarding Robots,
Chinese and Egyptian engineers developed automations. Dirican (2015) mentioned that the
beginning of modern AI is often traced in classical philosopher’s endeavour to explain human
thinking as the comprehensible symbolic system. In a chance event, the idea of AI came out
from the scientist Marvin Minsky in a conference. According to Moro, Cortez and Rita
(2015), in the conference, it was concluded that the issue of developing ‘Artificial

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Intelligence’ can be resolved. However, achieving an artificially intelligent being did not
seem to be simple; even though a series of reports criticizing the growth of AI, financial
support from the government and interest in the field of AI dropped off. Nevertheless, the
field was further fuelled in 1980 when British Government started to fund it again to compete
with the efforts by Japanese. In this context, Nazari and Alidadi (2013) commented that even
though the field further observed a series of ups and downs, in 1997 IBM developed its Deep
Blue which became of the first computer to win over a chess champion Garry Kasparov. It
has been identified that cash management as well as forecasting in the financial industry has
been more challenging because of the growing business complexities. Moreover, the
complexity is more likely to get worse in coming two years as banking organizations are
facing a tough technology revolution. This means technology is adopted but its use is more of
a great challenge. It has been identified that financial technology has arrived which meets the
fast pace of business, as those in finance tend to consume instant analytical technologies in
their personal lives and there are some tools available which could automate and deliver on a
timely manner (Sharma, Sharma and Barua 2013).
When it comes to real time applications of AI It has been identified that Artificial
Intelligence provides favourable changes in the business process and system by making it
faster and enhancing quality. AI is so different in providing a set of repetitive tasks like
social media sharing of blog posts on some particular time, scheduled post for publishing
match wanted solution by the means of AI (Zeinalizadeh, Shojaie and Shariatmadari 2015).
In production, cleaning, infrastructure development, design, transportation and several
engineering works and communication. Likewise, several other emerging benefits that AI
provides to its consumers.
Nonetheless, the accomplishment has been controversial but despite the series of
controversies, rapid pace of work in the field of AI have encouraged scientists and businesses

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to increase the level of expectation out of the field. As the large businesses and marketers are
showing their interest in AI, the interest of banking sector should not go unnoticed because as
the banking being one of the largest sectors to make investment in AI, consumers have
already starting encountering its work in their regular transaction. According to, Sharma,
Sharma and Barua (2013) global spending across industries on artificial intelligence is
estimated to reach about $20 billion in the last year and it could triple over next three years.
It has been particularly found that UAE is also interested in the new technology with AI
strategy targeting a contribution of AI of Dh350 billion. It has also been identified that for
consumers, technology is developing rapidly with the bank. It is found that banks use AI to
automate techniques, interact with consumers, and develop intelligent as well as real-time
lending models and improve fraud-related monitoring among other activities. In this context,
Zeinalizadeh, Shojaie and Shariatmadari (2015) mentioned that rule-based ‘bots’ help
banking organizations to perform repetitive tasks at the back end like shifting through a wide
set of documents to fetch data instantly or automate smoother processes. Chatbots are usually
text or voice based assistants which are helping the consumers to engage with the banking
organizations.
1.3 Problem Statement
It has been identified that even though the effect of AI is taking its toll on the
financial institution in UAE nations, adequate knowledge about the use of AI and cost has
always been a significant concern to the financial institutions of UAE. Another significant
concern is that implementation of AI needs a periodic basis check-up and the cost of such
practice might not always result in desired way because sometimes, cost could be huge.
Moreover, critical failure and bugs in the system are two most possible hazards which could
certainly the delay the operation in AI.

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As put forward by Acemoglu and Restrepo (2018), AI is an evolving technology as its end
goals are shifting because some of its capabilities become mainstream, prompting the
industry to pursuit yet another shiny and more advanced and intelligent system. In addition, it
has also been identified that not all AI dots can be linked and connected today and every user
in the field is learning –such as the Banks are learning, technology vendors are learning,
implementation partners are learning. Thus, it is difficult for all such parties to put their head
together in AI.
1.4 Research Aim and Objectives
The major aim of the study is to analyse the degree of risks in using AI within the retail credit
centre. Study secondarily aims to measure the influence of automation process which relies
on different services delivered by banks in Dubai. Studies also focus on the strategic
decisions which are implemented to deal with the emerging rival organizations appeared as
Fintech Counterparts. Following are the key objectives to meet the stated aim
 To understand the risks that might arise due to the implementation of automation
 To understand the impact that the automation process would be having upon the
different services and the ways of competing with the new Fintech who have entered
the market
1.5 Research Questions
 What are the risks associated with the implementation of the artificial intelligence?
 How can the AI implementation help the bank in remaining strategically enhanced?
 How can the AI implementation impact the process and the customer services of
Emirates NBD bank?

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1.6 Research Significance
There is no doubt that AI plays a great role in banking sector because the financial
transactions are facilitated due to use of AI. It has been identified that the use of AI helps in
tracking, monitoring as well as performing tasks at convenience of time. It has been identified
that due to the use and progress of AI, occurrence of fraud activities has been decreased over
time as well as smart actions about the banking practices are exercised within a fraction of
time. Moreover, the use and application of AI in banking sector is quite interlinked and
aligned within the idea of innovation in the banking sector. It is noted that other use of AI in
banking industry involves the fraud detection, enhanced consumer personalization, enhanced
governance and physical robots.
According to Vives (2017), continued advances in technology as well as changing
consumer profiles started driving the growth of digitalization as well as AI in banking.
Alongside, consumer acceptance of AI based solution is climbing steadily which is again
supported by data-related regulations coming into force stating the concern specifically on
privacy and security. Nonetheless, it is highly significant to learn how AI is actually
addressing the concern regarding privacy and security. It is important to learn and verify the
fact that future of banking sector is not simply a matter of selecting a robot over a human
being. This study helps to learn how in the field of AI, a large set of tools of digital
technology provides excellent banking experience to consumers. Implementation of the study
is particularly benefit the banking organizations with respect to the use of AI. Effectiveness
of usage of such platform with respect to the usage of such platform in UAE regions has been
evaluated to understand. Such evaluation is going to benefit organizations in the banking
sector as besides a series of benefits, organizations are going to know how they would deal
with the possible risks associated with the use of AI in banking sector. Moreover, each of the

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alternatives found in the AI are evaluated in the study which could benefit banking
organizations in UAE.
1.7 Research Outline
Introduction: This is an introductory chapter of the dissertation and it fundamentally
discusses the topic such as the emergence of AI in the banking sector of UAE and challenges
associated with the use of Artificial Intelligence. Chapter also provides clear aim and
objectives based on which a detailed review has been performed in the following chapter.
Figure 1: Organization of the study
(Source: Self-made)
Literature Review: This chapter provides an insight about AI and its contemporary
use in the banking sector. The review has been performed by considering more than thirty
five journal articles that are performed on AI and its application. On the basis of the review,
appropriate gaps in the paper have been found and the gaps are further addressed in the study.
Chapter 1: Introduction
Chapter 2: Literature Review
Chaper 3: Research Methodology
Chapter 4: Findings and Analysis
Chapter 5: Conclusion and Recommendation

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Research Methodology: This chapter is about the research methods have been used
in the study. Chapter effectively discusses how the methods are applied to achieve the stated
objectives in the introductory chapter of the dissertation.
Findings and Analysis: This is one of most vital section of the dissertation it holds
the fundamental part of thesis- primary findings about the existing use of AI in the banking
organizations of UAE. Moreover, this chapter also provides in-depth discussion about the
potential threats and opportunities of using AI in financial institutions.
Conclusion and Recommendation: This chapter basically concludes the work
performed on the chosen topic and on the basis of the findings, suitable recommendations
have been developed on the chosen context
1.8 Summary
This chapter builds the backbone of entire dissertation as it presents the topic effectively.
Chapter provides comprehensible background about the emergence and use of AI in the
banking sector. In addition to this, chapter also provides suitable research objectives and
questions based on which the following chapter on Artificial Intelligence has been reviewed.
In addition to this, clear research aim has been developed which has been further followed
throughout the dissertation.

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CHAPTER 2: LITERATURE REVIEW
2.1 Introduction
It has been identified that Middle East’s banking sector is gaining pace in technology
adoption as well as catching up with the rest of the world in digitalization in accordance to
the experts speaking at sixth annual Middle East Banking Forum in Dubai. There is no doubt
that due to the emergence of Artificial Intelligence, banking industry in UAE nations are
excelling in a rapid manner. Nonetheless, as discussed above in the introductory section of
the paper, even though, there is a significant progress in AI, progress is again delayed due to
potential threats and opportunities. Likewise, as the growth in AI in on, there are several new
challenges and opportunities are emerging alongside. This review helps to understand those
areas quite effectively and gain new insight about Artificial Intelligence in banking
organizations.
Scope of literature review
Review has been carried out by considering thirty five journal articles which are performed
on the emergence of Artificial Intelligence. In addition, the journal article selected for the
review have published after 2013. Even though the review covers a broad area, scholars have
hardly mentioned about the contemporary challenges in AI when using the system in the
financial sector. Moreover, as AI trends are emerging in nature, its application in the banking
organizations are not mentioned properly by scholars.
2.1 Artificial Intelligence: Opportunities and Challenges in finance industry
Makridakis (2017) performed an experimental analysis considering the growth of AI
in the banking industry and mentioned the fact that Fintech is having an enormous impact on
the banking sector due to the use of Artificial Intelligence machine learning, data analytics,
blockchain and all these are actually changing the ways financial industry works. Moro,

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Cortez, and Rita (2014) mentioned that the rapid pace of development as well as expected
implementation of Fintech technologies have led several sector experts to believe that finance
profession have peaked and there could be requirement for fewer finance professionals that
goes forward. Although the authors of this study have made this bold statement but the study
also provides a series of reasons behind the statement. In this context, Wu, Chen and Olson
(2014) commented that the most significant factor is blockchain technology as well as its
potential seismic influence on banking transactions across many nations. Findings of this
study also indicate the fact particularly, in the field of AI, blockchain technology has begun
to sweep the different fields of transactional finance like clearing, settlement, execution and
payment.
In response to this statement, Dapp, Slomka and Hoffmann (2015) mentioned that
slowly several market leaders as well as exchanges are investing in technology as it is secure.
It is highly secure and efficient and save cost by omitting the back office and operational staff
who could manually settle transactions and perform the investigation. Nonetheless, Elzamly
et al. (2017) mentioned that it is not only block-chain is being adopted by organizations,
Artificial Intelligence has started being employed in existing processes to know about all
reasons for inappropriate transactions and the deliver solutions to eliminate such significant
errors.
On the other side, Wamba et al. (2017) arguably mentioned that even though there is
still infancy, it is known that there are several algorithms trading functions that are widely
used across the world in multiple streams of asset management. Scholars have argued that
although some of them have proved to greatly successful, rapidly changing markets
environment indicates that one single strategy or approach cannot always be successful.
According to Helbing (2019), the forum organized by UAE banks federation functioned
global as well as regional banking sector experts, technology innovators and regulators.

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As mentioned by Pollari (2016), stakeholders’ unwavering commitment to come together as
well as contribute to ongoing effort on future proofing regional baking industry is derived
from enthusiastic involvement of industry regulators, experts and executives. On the other
side, Prisecaru (2016) mentioned the fact that while the banking organizations across several
nations and in the regions focusing on technology transformation, experts mentioned that the
adoption of new technologies is no longer an option instead it has been imperative for banks
as well as growingly banks and financial institutions are transforming into technology based
organizations.
In this context, Wu, Chen and Olson (2014) commented that embracing change as well as
revisiting business models could become imperative for conventional banking organizations
to survive in the centre of rise of digital technologies with the growing competition from new
non-banks entrants and changing consumer demands. On the other side, Moro, Cortez and
Rita (2015) performed a study and mentioned that given the current increasing pace of
change, industry experts told banks should work toward a financial architecture that strongly
support sustainability in the economy.
2.2 Banking institutions in UAE making a big leap in technology advances
As put forward by Lee and Shin (2018), as Fintech is radically changing the baking
sector, innovation such as Artificial Intelligence, machine learning, block-chain technology,
biometric identification, cloud computing as well as the use of big data are revolutionising
the sector. In a similar way, Stoneking and Curet (2014) added the fact that banking sector in
UAE are observing that incumbent monetary institutions as well as banks in particular that
tend to continue to make investments in Fitech solutions to remain ahead of the curve.
Finding of this study has also added the fact that in UAE. Furthermore, it has also been learnt
that both incumbent technology-savvy banks and challenger Fintech organizations are

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growingly offering technology based solutions which enhance user experience and the
capability to target consumers with better tailored products.
Remedies for expanding access to financial services through mobile banking as well
as other form of digital banking have become an increasing trend to particularly cater to the
requirement of consumers as well as influence efficiencies. In response to this statement,
Makridakis (2017) commented that the strategy both the upgrading of their own systems as
well as enhancing own services to allow the cost of delivery as well as effective consumer
experience.
2.3 Reproducibility Crisis in Artificial Intelligence
As put forward by Zhong et al. (2016), major development in Artificial Intelligence
have observed a machine which is being installed in business to derive safety critical
discussion. This guides vehicles for diagnosing the malfunction. However, Leek and Peng
(2015) stated that crisis related to reproducibility is developing a cloud of uncertainty over
the whole field which must wear away the confidence based on which the economy of AI
depends. Korinek and Stiglitz (2017) stated the fact that reproducibility can be treated as the
degree which allows the experiment and can be repeated with limited response which is more
of quality assurance and moreover it enables the existing findings to be verified accordingly.
It can be added that without the capacity to reproduce the past findings, the whole base is
established on which machines are increasingly making legal, medical and corporate
decisions. It can be added that deep reinforcement learning in which machine try every
solutions until and unless they are able to find the right solution, this could for example,
enable driverless cars to endlessly crisscross in augmented reality until they learn to change
lanes in the real world.
Lack of traceability

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As commented by Helbing (2019), the fundamental issue is that data science is not
operated and governed by the same in general which has accepted the standards of quality
assurance as the other context of science. However, consequently, the data trial charting the
road from the origins of AI to its latest iteration is covered in mystery. In this context,
Drummond (2018) also mentioned that there is no universal standards for governing the data
acquisition which process the techniques that give vital meaning as well as context to
Artificial Intelligence experiments.
2.4 A depth impact on banking sector employment awaits
As put forward by England and Cheng (2019), while there is a growing expectation
over the positive change that AI brings, a significant trepidation surrounding the technology
which is the most significant concern found as the high degree of substitutability that
members of the financial institutes are most likely to have with the robots. On the other side,
Hoces de la Guardia (2017) mentioned that as machine becomes more advanced, the tasks or
the jobs that involve significant repetition just like the bank tellers could be at risk as well as
the task requiring more complexity is most likely to be threated. As put forward by Alwan
and Al-Zubi (2016), Lex Sokolin known as the director of the global Fintech research
organization known as Autonomous Next stated that adoption of AI across several service
industry could serve US organization to $1 trillion in gain productivity.
As stated by Vives (2017), according to the report of Autonomous Next, banking as
well as lending could first observe the largest transformation with $450 billion in savings
which is being potentially achievable. Scholars of the study provide the statistics that almost
1.2 million jobs often are certainly at risks which is followed by insurance with $400 billion
in savings as well as 865,000 jobs remain under the threat. On the other side, Kiliç, Kuzey
and Uyar (2015) provided the data almost 48000 tellers, 219000 consumer –service
representatives and clerks could be replaced by the chatboat, voice assistance as well as

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automated authentication. In this context, Smith and Anderson (2014) commented that AI
based credit-underwriting as well as smart-contact technology could also be responsible for
shedding of 250,000 loan officers. Authors have also stated the fact that such fear about
employment assured by some world’s most reputable banking leaders. Thus, it is expected
that Artificial Intelligence could render as many as 30% of banking jobs.
Nonetheless, Sharma, Sharma and Barua (2013) argued that proliferation of AI could
be accomplished by an increase in the banking jobs and the findings of this paper stated the
fact that a net gain of 14% is most likely to materialize among those organizations which
effectively utilize AI with 34% increase in the revenue. It can be mentioned that as complex
algorithms are most likely to be used and installed by Artificial Intelligence, banking
shareholders, regulators and other parties with the vested interest are not supposed to be
assured if they do not find anyone suitable to explain the work as well as methodologies of
the machine.
2.5 Embedding technology thinking into financial services
According to Weber (2014) AI technology like Rubique is the significant
differentiator and it is more of a technology based services as with the use of technology and
automation brand or bank can resolve the challenges in the financial services. It is worth
telling that Rubique remains the earliest adopter of Artificial Intelligence in Fintech. Finding
of this study indicates the fact that AI devices tend to leverage its rich consumer data as well
as bank credit policies to deliver consumers a customized list of financial products to each
consumer on the basis of individual’s need and profile through the AI based match-making as
well as the ranking algorithm. On the other side, Lee and Shin (2018) performed a study and
added that AI-based technology Rubique tends to offer a win-win ecosystem for the
consumer experience on the demand side and the issue of quality for financial institutions on
supply side. It can be mentioned that AI based technologies to aggregates the information

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about all products from the banking organizations and there every consumers tend to search
for the most suitable and best deal according to the financial requirement.
It has also been identified that AI based technology developed the solutions as well as
mobile applications for its stakeholders like (Single Point of Truth) for the associates who
tend to empower the financial agencies or the entrepreneurs. In this context Moro, Cortez and
Rita (2015) commented that if it is considered from the technology based perspective, it is
worth mentioning that the individuals tend to plan developing innovative as well as
customized solutions to align with the needs of the stakeholders. On the other side, Lee and
Shin (2018) mentioned the fact that the burgeoning online lending segment in the nation is
giving rise to new sort of challenge on sourcing the credit score. Authors have added the fact
that in order to resolve this issue Fintech organizations tend to use Artificial Intelligence and
Machine Learning to generate alliance as well as Machine Learning to create alternate
lending data sources score for more than 80% of the population of the developing nations.
According to Zhong et al. (2016), the inappropriate credit details have led to the
emergence of many analytics start-ups are working on ways to enhance alternate data based
lending programs to provide personal solutions. On the basis of this findings, it can be added
that most of the financial institutions are adapting to technology based thinking to have a
more accurate insight about the dynamic businesses. It has been noted that leading banks
have embraced the digital-lending the revolution which brings ‘time to yes’ down to less time
and according to Sharma, Sharma and Barua (2013), such profound result of a top priority
form organizations around the globe such as digital transformation of end to credit journey
with the inclusion of consumer experience as well as supporting credit techniques. It is worth
stating that credit is at heart of most consumer relationship and digitalization and it tends to
offer significant advantages to banks and consumers alike. In order to support this statement,

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effective transformation ten to maximize the revenue growth and accomplish a good amount
of cost of advantages.
As put forward by Liedtka (2014), as the digitization proceeds quickly, dimensions of
banks’ ambition of digitalization could vary among the segments and products. Moreover, it
can be added that digitization tends to become the significant norm for the credit retail
technique and thereby, the personal loan-application can be submitted with few swipes on
mobile phone. Liedtka (2014) has arguably stated the fact that mortgage lending could be
more complex because of the regulatory challenges, still the financial institution in several
developed nations have certainly managed to digitalize a great part of the mortgage journey.
It is noted that trends of digital advancement in the financial sector is rapidly spreading in
corporate field even though the corporate banks tend to move highest caution as well as less
urgency. Thus, Folkinshteyn and Lennon (2016) added the fact that instead of reworking the
whole consumer experience, financial organizations are improving the common processes
such as some organizations’ digital initiatives tend to allow corporate transactions approvers
by focusing on their time on those clients which makes big difference. Peters et al. (2016)
gave an example that low-risk-line renewals, for example, can be automated but the valuable
consumer review which is centred on more complex and simpler deals.
2.6 Fintech Partnership
As put forward by Bryson, Daniels and Warf (2013), the ability to assess and manage
technology partners could be significant to digital-lending transformations. According to the
scholars of the study, some organizations have certainly observed that the workflow engines
underlying credit techniques which cannot be established to support the real-time as well as
online lending journey. In order to this address the challenge, several large organizations in
the financial sector have started partnering with Fintech and this partnership enabled banks.

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Author of this study, Wamba et al. (2017) mentioned that partnership have enabled banking
organizations to enhance and develop capabilities and present new consumer offering apace.
As put forward by Dirica (2015), there are certain advantaged that Fintech could
bring such as the complete capability and data feeds for end to end journey in the emerging
market, experience in lending new approaches such as credit decisions of small and medium
size enterprise decisions through the use of alternative sources. It can also be mentioned that
components of individual analytics can be integrated into existing bank processes. On the
other side, Sharma, Sharma and Barua (2013), the benefit of partnership have certainly
helped the global bank which has developed a strong digital-lending offering and then
worked with the developed SME lending fintech to generate software platform for consumer
journey.
2.7 Need for an agile approach
According to Zeinalizadeh, Shojaie and Shariatmadari (2015), divergent interest of
business as well as risk management might create the inherent tension for financial
institutions for banks in restructuring the credit processes. Authors gave the example that One
Eastern European organizations found that its month-long project to make simplification of
corporate-lending techniques which are little heavy because of legitimate internal interest.
Consequently, the bank organizations become blogged down with the individual silos which
optimize for their own interest instead of collaborating on optimizing consumer experience
and all this happens because it lacks an agile approach. As put forward by Acemoglu and
Restrepo (2018), agile project delivery is strongly necessary for the effective credit
digitalization and onset is more of cross-functional and dedicated teams empowered with the
decision-making authority.

22DISSERTATION ON INFORMATION TECHNOLOGY
Nonetheless, Moro, Cortez and Rita (2014) mentioned that while most of the
executives are actively seeking the agile approach, not all of them are actively doing it. It has
been identified that several organisations adopting the cosmetic agile approach in the
business process, while the approach of project management are equipped with agile lingo
but essentially core changes in some ways of working are not properly adopted. In this
context, Wu, Chen and Olson (2014) commented that the most common challenge here is
possibly the inability to deal with organizational silos and the authors have also added the
fact that a cross functional team with the business, risk, operations is highly essential for
many reasons such as the collaboration across several functions that help to strike the balance
of consumer journey as well business objectives with suitable credit decisions making as well
risk control. On the other side, Dapp et al. (2015) mentioned that agile redesign technique can
be referred as ‘Zero-based’ approach.
2.8 Trends that Artificial Intelligence is helping the financial institutions
As put forward by Moro, Cortez and Rita (2015), Artificial Intelligence is one such
thing that could disrupt diverse sectors but economic institutions are anticipated to generate
benefits for the incorporation of AI systems in net few years. It has also been identified that
Analyst should estimate that AI could save the banking sector more than 1 trillion by 2030. It
is worth telling that large financial institutions have enormous burden of consumer success,
so generally they tend to look towards the automation of consumer service with the chatboat.
On the other side, Zeinalizadeh, Shojaie, and Shariatmadari (2015) gave example that
banking organizations such as hedge funds are adopting AI above the new layers of data
sources and insurance organizations enhancing risks models with the Artificial Intelligence.
According to the authors of the above study, an increasing number of financial institutions in
developing nations are mostly stuck to the development of data infrastructure in some ways
that could enable all to leverage AI. On the other side, Acemoglu and Restrepo (2018) stated

23DISSERTATION ON INFORMATION TECHNOLOGY
that few issues and AI solutions that several financial institutioons at present generate values.
So, it can be added that this is not a compressive list of AI initiatives, that banking sector
experiments.
Chatbots and Personalized Customer Service
As put forward by Moro, Cortez and Rita (2014), as there is an increasing automation,
there is always fear of reduced loyalty because of limited personal contact. Nonetheless, Wu,
Chen and Olson (2014) commented the fact that increased AI usage does not essentially mean
less personalized experience, in fact, banks could use AI to enhance client satisfaction,
enhance efficiency as well as maintain consumer loyalty in several ways. For example, the
scholars Moro, Cortez and Rita (2014) mentioned for example that Bank of America has
already manufactured a chatbot which is called Erica and AI enabled tool that provides
financial guidance for banks’ consumers for bank’ customers through voice and text message.
Such service accessible 24X7 and it could perform regular transactions. This could allow
consumers to have access to service at any time without costing more hiring consumer
service personnel. Such function could allow the clients to gain access service anytime
without costing more money hiring consumer service personnel. According to the author of
this study, chatbot certainly helps to make sure that less-typical questions hold a ready-made
responses versus the presents status quo where advisors where the advisors often have to
consult the experts for sudden advices.
With the use of AI transactional as well as other data sources can be tracked to
understand a customers’ behaviour as well as preferences to enhance their experience. In this
context, Dapp, Slomk and Hoffmann (2015) commented that by using AI’s potential to
disrupt finance and fintech, competition among the leading institutions could increase in the
coming years. Large organizations have grasped the significance of innovation as well as the

24DISSERTATION ON INFORMATION TECHNOLOGY
application of AI in their businesses; and they have started to reap benefits while the small
and medium sized institutions started to catch up. On the other side, it has also been identified
that one of the biggest challenges faced by smaller organizations in adopting AI is the
deficiency of talents. According to the author of this study, it can be added that large
organizations have effective reputations for innovation as well as increasing profit-per-
employee ratios which is more likely to recruit and select top talent because of the significant
paycheck for Artificial intelligence as well as machine learning. Good news is that people are
seeing AI start-ups that believe in equal access to AI technologies by investing more in
education and preparing more AI engineers with the objectives of helping smaller as well as
medium players.
Compliance, Fraud Detection and Anti-Money-Laundering
Acemoglu and Restrepo (2018) mentioned that avoiding fraud as well as money
laundering could be challenging for several financial organizations and Artificial Intelligence
holds the potential to guide banks to become more efficient in the technique of tracing fraud
as well as money laundering. On the other side, Moro, Cortez and Rita (2014) mentioned the
fact that in order to identify the potential fraud quickly, AI engineers have enhanced some
specific tools and systems which can automatically compress as well as conduct data which
conventionally require several hours of labour in a matter of minutes.
Makridakis (2017) in this context added the fact that bigger institutions are intended
to upgrade their legacy systems because of the growing of fintech organizations that are
implementing AI. The largest bank giants like Emirates NBD in UAE has already started
using machine learning as well as big data to minimize and prevent criminal activities as well
as supervise potential threats in trade. Wu, Chen and Olson (2014) mentioned the fact that the
organization has adopted a new anti-money-laundering structure and made the investment of

25DISSERTATION ON INFORMATION TECHNOLOGY
$11 million to introduce new personnel finance application which encourages consumers to
take part in the third party services.
2.9 Ways how Artificial Intelligence transformed finance industry
Risk Assessment: As put forward by Wamba et al. (2017) as the actual basis of AI is learnt
from the existing data, it can be stated that AI could succeed in financial service domain
where booking keeping as well as records are usually the secondary nature to thee business.
For instance, today customers mostly use the credit card and banks often use the credit score
as the way of deciding who can be eligible for a credit card and the persons who cannot.
However, it is worth telling that grouping people in such way might not be effective for the
business and in such filed, AI plays a great role because the data is driven and the data is
highly dependent and it can be scanned through such records that provide AI the capability to
make suggestions about the loans and credit related offering. It has been identified that AI is
developed upon the machine learning and the system leans over time along with limited
possibility of errors. It creates a large volume of data and AI developed automation to the
context which requires intelligent analytics and concise ideas and thinking. Helbing (2019)
added that AI and Machine Language tend to occur in the most rapid way which is fast and
the chance of inaccuracy is less in the system compared to how a human processes the data.
Moreover, with the development of technology, AI developed automation to some areas
which require intelligent analytical and clear understanding among several tools and
chatboats have particularly turned out to be the most significant tool to consumer satisfaction
and unmatched resources for the firm to avoid the financial loss.
Trading: It has been identified that investment organizations have been depending on
compute devices as well as data scientists to determine future patterns in the market (Helbing
2019). Authors have also stated the fact trading as well as investment tends to depend on the
ability to make assumptions of the future accurately. According to this author, while

26DISSERTATION ON INFORMATION TECHNOLOGY
anomalies like 2008 financial crisis in data, organizations can teach a machine to study the
data to find triggers for such anomalies and a future plan in forecasting for the future.
Managing Finance
As put forward by Lee and Shin (2018), managing finance in this well-connected as
well as materialistic world could a challenging task for many of the people and the scholars
agree to the fact that AI is actually helping people to deal with the financial issues and all sort
of financial transactions. According to the finding of this study it can be mentioned that
personal financial management is one of the significant development of AI. It is worth telling
that AI has to develop algorithm to help consumers to make decisions regarding money when
they are spending it. On the other side, Makridakis (2017) claims that AI is basically the
future for the finance industry because as the speed at which it is making progressive stoops
towards making the financial process easier. Authors of this study has also mentioned that
enormous enhancement are being made but organizations who are seeing as a very long-term
cost cutting investment and it could help organizations in saving money of hiring humans and
avoiding human errors in the process
2.10 Risks associated with AI
Risk associated with AI is usually divided into two different categories or as per the
review, two perspectives can be considered in two ways, macro-financial risks and micro
financial risks. Jonsson et al. (2013) performed a study in the same and mentioned about the
following.
Micro Financial challenges
Financial market risk:

27DISSERTATION ON INFORMATION TECHNOLOGY
As put forward by Alwan and Al-Zubi (2016) balance and stability in the financial
market could be at risk. When a growing number of market participants adopt AI technology
at the same time, machine learning are most likely to outperform any other businesses in the
sector and most importantly traders could adopt some similar learning strategies. It creates
some financial barriers. Findings of the study also imply the fact that estimated patterns in
machine learning trading approaches could be vulnerable to manipulation of price in the
market by fake agents.
Risks for financial institutions
In this context, Korinek and Stiglitz (2017) commented that for most of the people, AI
decision-making technique is more of a black box and lack of transparency makes it more
difficult for regulators and investors to analyse the potential issues in the technique; this
means if the decisions made by AI result in market losses, it could be challenging to explain
the responsibility. Particularly, when there is any sort of uncertainty regarding the governance
structure of AI applications in the financial institutions, the challenge could be undermined.
For example, if the AI is highly reliant on a small number of third party technology providers,
it might certainly pose challenge for financial institutions.
Risk to customer privacy
As put forward by Leek and Peng (2015), Machine learning from data is more of an engine
behind Artificial Intelligence and it could allow a model to be continually enhanced by
gathering, counting and analysing data; thereby, the amount of personal data which is
recorded is rapidly increasing. It is worth stating that such data is the risk from disclosure as
well as abuse of personal information considering hacker’s attack. For instance, the data

28DISSERTATION ON INFORMATION TECHNOLOGY
collected or calculated by AI models might lead to prejudice or bias in result, thus, it can be
added that policies to protect customer privacy and data security thus necessary.
Macro Financial challenges
Macro financial risks are further divided and categorized in several ways, such as the risk of
market concentration.
Risk of market concentration
As put forward by Moro, Cortez and Rita (2015) if the use of AI technologies are left in the
hand of a small number of leading third party providers, particular functions in the financial
system could become more concentrated. Authors of this study has added the fact if some
particular organizations tend to control enormous amount of big data, one significant
organizations could afford the most cutting edge technologies because of the cost of R&D. It
is certain that market positions of those agencies might increase which eventually lead to
market concentration.
Risk of market loophole:
According to Acemoglu and Restrepo (2018) Machine learning trading algorithms could
involve a certain degree of unpredictability and if they create influence in the financial
market, it can be more challenging to describe the causes. In addition to this, if AI is used
greatly in a high frequency business, there could be a large scale of transactions at the same
time, which could lead to enhanced market volatility. Here, the application of AI could also
need to minimize the liquidity cushioning and result in potential risk.
Risks related to technology: If the model of AI is not particularly trained or there is not
enough feedback, or there is a lack of testing related to stress, users could be able to detect

29DISSERTATION ON INFORMATION TECHNOLOGY
potential technology based risks in time, particularly if they do not fully understand the
nature and constraints of Artificial Intelligence (Dapp et al. 2015).
2.11 Gaps in the review
It can be added that even though the existing papers have a broad view regarding AI is
making enormous impact on the organizations in the financial sector but hardly a study has
derived specific or precise insight about the emerging use of AI. In addition to this, existing
studies have paid significant attention to the challenges and the discussion significantly lack
the insight about how the opportunities are helping organizations in the banking sector. This
means if AI is an emerging trend and helping the businesses to play a crucial role in the
market, why scholars only considers the challenges is a gap in the review.
.

30DISSERTATION ON INFORMATION TECHNOLOGY
CHAPTER 3: RESEARCH METHODOLOGY
3.1 Introduction
This chapter of the dissertation gives a broad view about research in the study have
been selected and applied to know the future opportunities and benefits of implementing
Artificial Intelligence within Retail Credit Centre in UAE. In order to learn more about the
challenges and opportunity related to the use of AI, it is highly effective to use primary data.
Collection of primary data helps to learn how the organizations in the banking sector
presently use AI to gain benefits and deal with the risks. Present study uses a quantitative
data collection method in which only the first hand information have been used to perform
the chosen analysis.
3.2 Research Onion
Research Onion diagram consists of several research method or layers of methods that
remain in the form of onion layers. Each of the layer of onion has its own functions and
features. As put forward by, first layer of the onion is research philosophy or the
philosophical stance. Research philosophy further consist of three more philosophies such as
positivism, interpretivism and realism (Tuohy et al. 2013). Likewise, the second layer of
the onion framework is research approach which is two different categories namely deductive
and inductive. Likewise, the third layer of the onion is about the research strategies and
fourth layer is research choice such as mixed method or mono method. Similarly, the sixth
layer of the onion framework is technique and procedures. The following section discusses
how the methods and which of the methods have been selected and applied to the present
study.

31DISSERTATION ON INFORMATION TECHNOLOGY
3.3 Research Philosophy
On the other side, interpretivism research philosophy tends to interpret the elements
of the paper by integrating human interest into a study. In this context, Bresler and Stake
(2017) commented that intepretivisim research philosophy considers reality can only be
accessed through social constructions like language, shared meaning and instruments. The
major method of interpretivism research philosophy is interview and observation and
secondary data is one of the popular study with the interpretivist philosophy. Nonetheless, in
the present study, positivism research philosophy has been selected. So, if the use of AI in the
banking sector needs to be investigated, the study is only dependent in the secondary data and
the findings of past research. Nonetheless, in the present study, positivism research
philosophy has been selected for investing how Artificial Intelligence is running the
operation of banks in UAE.
Justifying then positivism research philosophy
Positivism research philosophy has been selected because positivism research philosophy will
help to quantify the outcome with respect to the use and impact of AI in banking transactions.
As primary data is required for performing the analysis in the chosen context, the selection of
positivism research philosophy helps to collect data by performing survey. Moreover,
positivism philosophy helps to generate digits and numbers which means the findings are
completely based on the mathematical calculations instead of relying on the theoretical
assumptions. In order to learn risk assessment, future opportunities and benefits of
implementing AI within the banking the sector, positivism is a best choice because it helps to
categorize the statistical data figure of each element of AI. For example, if the study is
required to know the degree of risk associated with AI, the degree can be quantified.

32DISSERTATION ON INFORMATION TECHNOLOGY
3.4 Research Approach
Research Approach can be divided into two different categories namely deductive and
inductive research approach. As put forward by Tuohy et al. (2013) deductive research
approach is majorly concerned with the development of the hypothesis which is based on the
existing theories and the design of the research theories to test and verify the hypothesis. It
has been identified that deductive research approach is fundamentally concerned with the
causal relationship which means it tends to identify the cause and effect relationship. For
example, with the help of the hypothesis testing, it can be learnt the relationship between
performance of the organizations and the impact of AI. On the other side, inductive research
approach is more of reasoning which basically starts with the observation and then theories
are developed towards the end of the research techniques. It has been identified that inductive
research may include the search pattern from observation and enhancement of explanation. If
the use of AI is required to be investigated in the financial sector and first observation
regarding the same can be made such as the review of existing papers and then primary data
can be used to make the observation. Eventually the implications about the study is made at
the end. Thus, in the present study, inductive research approach has been selected and applied
to the study because in the present study, first the observation regarding AI’s risks and
benefits are observed throughout the existing studied and verified by then primary findings,
implications halve been drawn at the end.
3.5 Research Design
It has been identified that different scholars have different views about regarding research
design. Nonetheless, research design is about how research questions are supposed to be
added, while Kumar (2019) mentioned that research design is referred to the choice of
particular method of collecting data and performing analysis, in the present study, research
design is more of a plan regarding how one is going to respond to the research questions.

33DISSERTATION ON INFORMATION TECHNOLOGY
Research design is divided into three different categories namely descriptive research
design, explanatory and explanatory research design. According to Macke and Gass
(2015), exploratory research design as the name indicates, it helps to explore the research
questions but it does not provide final and conclusive solutions to existing research issues. On
the other side, explanatory research design is used to identify the degree and nature of the
cause and effect relationship between the variables. Conversely, descriptive research design
helps to cast light on the existing issues with the help of data collection and then it helps to
describe the situation more completely that it was possible before.
Justifying descriptive research design
In the present study, descriptive research design has been selected because descriptive
research would help to pay attention to the recent issues and affairs related to AI and its use
in the banking sector. In addition to this, descriptive research design helps to describe
different elements of Artificial Intelligence and its use in the financial sector. So, if the aim of
the study is to identify the impact of AI in financial sector of UAE, descriptive research
design helps to pay attention to other elements of AI.
3.6 Data collection methods
Data collection process of gathering information from all relevant sources to find
responses to the research context, and evaluate the outcomes. It can be stated that data
collection procedure is divided into two different categories namely secondary data collection
and primary data collection.
Secondary Data Collection Method:
Secondary data has been performed on the basis of the type of data which has already
been released and published in newspaper, magazine, journal articles and online news portal.
It should be noted that access to secondary data is abundant regardless of the type of study

34DISSERTATION ON INFORMATION TECHNOLOGY
area. Thus, appropriate category of secondary needs to be collected. In the present study,
secondary data has been collected from authorized journals, books, articles and other
authorized wide reading sources.
Primary Data Collection method:
It has been identified that primary data collection method can be categorized into
different groups such as quantitative and qualitative.
Quantitative Data Collection: It has been identified that quantitative data collection method
are completely based on the mathematical calculations in different formats but the major
method of collecting information is the use of questionnaire with the close ended questions.
In the present study, quantitative data has been collected by performing a survey among the
organizational members of banking organizations located in UAE nations. Survey has been
performed by engaging the respondents into the data collection process. Survey has been
performed on the basis of the sampling method stated in the following.
Qualitative Data Collection: Qualitative data collection method on the other side do
not involve numbers and digits as the data is more of data which is completely based on
words, sounds, emotions and feeling and the data is non-quantifiable. As put forward by
Silverman (2016), qualitative data assure a large level of depth of understanding and
qualitative data which includes the process of interview and questionnaire with the open-
ended questions, observation and focus group and many more. However, in the present study,
it has been identified that qualitative data has been collected by performing an interview
among the senior organizational members of banks in UAE. Qualitative data has been
collected on the basis of the sampling method selected stated in the following.

35DISSERTATION ON INFORMATION TECHNOLOGY
3.7 Sampling method
Sampling method is known as specific issues consumed to select members of
population to be involved in the paper. It has been identified that due to large population of
interest, it is difficult to work with the population interest; thus, sampling method helps to
reduce the complexity. Sampling method can be divided into two different categories such as
probability sampling and non-probability sampling. In probability sampling method, each
population member has a non-zero chance of taking part in the study, which means all
members of the population can take part in the study, while in non-probability sampling
method only some particular members who are aware of the context of the study can take part
in the data collection process. In the present study, non-probability sampling method has been
selected because the study involves the organizational members.
So, when involving the organizational members, it is highly important to ensure the
convenience or availability of the members. On the other side, probability sampling method
has not been selected because probability sampling method is applied in a random manner,
which means the members are chosen randomly. This means members lacking adequate
knowledge about the context can also take part in the study. Following section defines how
the respondents have been selected for performing the survey analysis.
Target population: As put forward by target population tends to represent some
particular segment within a greater population which has been effectively positioned to serve
as the primary data source for the study. This target population in the present study is the
organizational members of banking organizations presently located in UAE. For example for
the title ‘Risk Assessment, Future Opportunities and Benefits of Implementing Artificial
Intelligence within Retail Credit Center and Disbursal Units – An Impact Study at a leading
UAE Based Bank, Dubai’ target population could be the organizational members of UAE
Based Banks.

36DISSERTATION ON INFORMATION TECHNOLOGY
Choosing sampling frame: It can be mentioned that sampling frame is known as the
list of people within a target population who can actually take part to the study. For example,
for the present dissertation, sampling frame could be an extensive list of people or the
organizational members of the bank who can take part in the study. So even though, the
organizational members are the target population, sampling frame is a specific list of people
who presently work in the organizations.
Determination of sampling size: Sampling size is the number of individuals from
the sampling frame who can actually take part in the study or in the data collection process.
In the present study, sampling size is 30 organizational members.
3.8 Ethical Consideration
Ethical Consideration is one of the significant part of the study as ethical issues could
often lead to failure if they are not properly addressed. Thus, in order to resolve or avoid
ethical issues, respondents’ consent has been taken; this means before involving the
respondents in the data collection process, an ethical check list form signed and authorized by
the authority has been submitted to the banks authority in UAE. It has been ensured that no
respondent has been forced to take part in the study and it has also been ensured that all
respondents hold the right of withdrawing their participation at any moment they wish. Data
Protection and Confidentiality is another significant concern; so to ensure confidentiality of
the data, Data Protection Act 2018 has been followed. Researcher ensures that the collected
data has only been used for the academic purpose and the data has not been used anywhere
apart from the same.

37DISSERTATION ON INFORMATION TECHNOLOGY

38DISSERTATION ON INFORMATION TECHNOLOGY
CHAPTER 4: FINDINGS AND ANALYSIS
1. How far do you agree that Artificial Intelligence has positive impact on banking
organizations in UAE?
Row Labels Count of
1.
Agree 29
Do not agree 2
Grand
Total
31
Table 1: Artificial Intelligence has positive impact on banking organizations in UAE
Graph 1: Artificial Intelligence has positive impact on banking organizations in UAE
It has been identified that almost 94% of the respondents have mentioned that Artificial
Intelligence has impact on banking organizations in UAE nations, while only 2% of the
respondents out of 31 have mentioned that AI is not able to create much impact on the

39DISSERTATION ON INFORMATION TECHNOLOGY
banking organizations. AI based-technology is an emerging technology based system which
has a wide series of products and entire banking organizations across the globe has started
adopting AI technology in the process. In the literature review, it was found that Middle
East’s banking sector is gaining pace in technology adoption as well as catching up with the
rest of the world in digitalization in accordance to the experts speaking at sixth annual Middle
East Banking Forum in Dubai (Moro, Cortez, and Rita 2014). Especially, the technology like
Fintech is having an enormous impact on the banking sector due to the use of Artificial
Intelligence machine learning, data analytics, blockchain and all these are actually changing
the ways financial industry works (Makridakis 2017). Thus, it can be mentioned that AI-
based technology has a significant impact on the banking operation in the UAE regions.
2. Does your bank use any of the following AI technologies for performing financial
activities?
Row Labels Count
of 2.
Blockchain 10
Chatbot 4
Fintech enabled
technology
17
Grand Total 31
Table 2: AI technologies for performing financial activities

40DISSERTATION ON INFORMATION TECHNOLOGY
Graph 2: AI technologies for performing financial activities
The above presented graph helps to state the fact that almost 10 respondents out of 31 have
mentioned that their organization presently use block-chain technology, and 4 respondents
out of 31 have stated that organizations have their organization use chatbot and almost 17
respondents have stated that Fintech technology is controlling the banking operation of the
organizations. It was studied in the literature review that unlike Fintech technology,,
blockchain technology has begun to sweep the different fields of transactional finance like
clearing, settlement, execution and payment. Blockchain technology as well as its potential
seismic influence on banking transactions across many nations (Wu, Chen and Olson 2014).
So, it is worth stating that both the technology have gained tremendous exposure in the recent
time due to the emergence of AI technology. In the field of AI, blockchain technology has
begun to sweep the different fields of transactional finance like clearing, settlement,
execution and payment. It is not only block-chain is being adopted by organizations,
Artificial Intelligence has started being employed in existing processes to know about all
reasons for inappropriate transactions and the deliver solutions to eliminate such significant
errors (Dapp, Slomka and Hoffmann 2015).

41DISSERTATION ON INFORMATION TECHNOLOGY
3. How far do you agree that use of AI in financial institutions is effective for
speeding up operation?
Row
Labels
Count
of 3.
Agree 28
Strongly
agree
2
Strongly
disagree
1
Grand
Total
31
Table 3: Effectiveness of AI in financial institutions

42DISSERTATION ON INFORMATION TECHNOLOGY
Graph 3: Effectiveness of AI in financial institutions
It has been identified that almost 28 customers out of 31 have mentioned that use of AI in
financial institutions is effective for speeding up the operation and on the other side, almost 2
participants out of 31 have stated that AI technology is not able to speed up the operation in
financial institutions. This means almost 90% of the respondents have mentioned that AI is
rapidly helping the financial institutions. In the literature review, it was studied that there is a
state of illiteracy in the use of AI in the banking sector. It was studied that despite the same
even though there is still infancy, it is known that there are several algorithms trading
functions that are widely used across the world in multiple streams of asset management.
Even though some of them have proved to greatly successful, rapidly changing markets
environment indicates that one single strategy or approach cannot always be successful.
Review of existing papers have stated the fact that embracing change as well as revisiting
business models could become imperative for conventional banking organizations to survive
in the centre of rise of digital technologies with the growing competition from new non-banks
entrants and changing consumer demands (Wu, Chen and Olson (2014). According to the
current increasing pace of change, industry experts told banks should work toward a financial
architecture that strongly support sustainability in the economy.
4. Do you believe that Fintech is a proper solution for contemporary financial
methods?
Row
Labels
Count
of 4.
No 2
Yes 29

43DISSERTATION ON INFORMATION TECHNOLOGY
Grand
Total
31
Table 4: Fintech is a proper solution for contemporary financial methods
Graph 4: Fintech is a proper solution for contemporary financial methods
The collection of primary data helps to observe the fact that almost of 29 respondents out of
31 have stated the fact that almost Fintech is a proper solution for the contemporary financial
techniques in the banking organizations, while 2 respondents out of 31 have a contradictory
view about the same. Fintech being an emerging AI based technology has gained tremendous
exposure in the recent time. In the literature review, it was found that there are certain
advantaged that Fintech could bring such as the complete capability and data feeds for end to
end journey in the emerging market, experience in lending new approaches such as credit
decisions of small and medium size enterprise decisions through the use of alternative
sources (Dirica 2015). Thus, it can be stated that this fact is quite relevant to the primary
findings provided above. Fintech is actually gaining the exposure in the banking sector.
Fintech partnership is an emerging concept as (Sharma, Sharma and Barua (2013), mentioned
that the benefit of partnership have certainly helped the global bank which has developed a

44DISSERTATION ON INFORMATION TECHNOLOGY
strong digital-lending offering and then worked with the developed SME lending fintech to
generate software platform for consumer journey. Nonetheless, beside, the fintech
technology, other AI based technology like chatbot gains a similar exposure in the banking
sector. For example, Bank of America has already manufactured a chatbot which is called
Erica and AI enabled tool that provides financial guidance for banks’ consumers for bank’
customers through voice and text message (Moro, Cortez and Rita 2014).
5. Do you believe that reproducibility crisis in AI is affecting financial institutions in
UAE?
Row
Labels
Count
of 5.
No 1
Yes 30
Grand
Total
31
Table 5: AI is affecting financial institutions in UAE

45DISSERTATION ON INFORMATION TECHNOLOGY
Graph 5: AI is affecting financial institutions in UAE
Collection of primary data helps to observe that almost 30 respondents out of 31 have stated
that reproducibility crisis in AI is actually affecting financial institutions in the United Aran
Emirates. In addition, a marginal 1% of respondents have stated the fact that reproducibility
crisis is not affecting the banking organizations in UAE regions. Even though AI provides
myriad of opportunities for speeding up the growth of financial institutions but
reproducibility crisis in the field is no less to consider. It was studied that major development
in Artificial Intelligence have observed a machine which is being installed in business to
derive safety critical discussion (Zhong et al. 2016). It was also studied in the literature that
crisis related to reproducibility is developing a cloud of uncertainty over the whole field
which must wear away the confidence based on which the economy of AI depends (Leek and
Peng 2015). On the basis of this primary and secondary findings, it can be stated that
reproducibility can be treated as the degree which allows the experiment and can be repeated
with limited response which is more of quality assurance and moreover it enables the existing
findings to be verified accordingly. Thus, it can be stated that reproducibility crisis is most
likely to affect AI-based technology in the banking sector.
6. How far do you agree that use of AI in banking operation is affecting the human
skills in the banking sector?
Row
Labels
Count
of 6.
Agree 28
Disagree 2
Strongly
disagree
1

46DISSERTATION ON INFORMATION TECHNOLOGY
Grand
Total
31
Table 6: AI in banking operation is affecting the human skills in the banking sector
Graph 6: AI in banking operation is affecting the human skills in the banking sector
The collection of primary data helps to observe the fact that almost 28 respondents out of 31
have stated the fact AI-based technology in the banking sector is actually affecting human
skills in the industry. On the contrary, only 1% of the respondents tend to hold an opposite
view about the same. It is worth stating that due to technology advances, human skills are not
being brushed up for performing the usual banking activities. Banking transactions, creating
security protocols, updating and generating account that once were done humans are no
longer the activities of humans. Technology plays a great role in performing all these
activities in the most effective and faster way. In the literature review, it was studied that
while there is a growing expectation over the positive change that AI brings, a significant
trepidation surrounding the technology is the most significant concern found as the high
degree of substitutability that members of the financial institutes are most likely to have with

47DISSERTATION ON INFORMATION TECHNOLOGY
the robots (England and Cheng 2019). It was also studied that as machine becomes more
advanced, the tasks or the jobs that involve significant repetition just like the bank tellers
could be at risk as well as the task requiring more complexity is most likely to be threated
(Hoces de la Guardia 2017). Nonetheless, in the literature, a contradictory view has also been
found such as Moro, Cortez, and Rita (2014) mentioned that rapid pace of development as
well as expected implementation of Fintech technologies have led several sector experts to
believe that finance profession have peaked and there could be requirement for fewer finance
professionals that goes forward.
7. How far do you believe that agile approach is required for facilitating financial
operation?
Row
Labels
Count
of 8.
Agree 25
Disagree 3
Strongly
agree
3
Grand
Total
31

48DISSERTATION ON INFORMATION TECHNOLOGY
Table 7: Agile approach is required for facilitating financial operation
Graph 7: Agile approach is required for facilitating financial operation
The graph presented above helps to observe the fact almost 25 respondents have stated the
fact that agile approach is required for facilitating financial operation, while only 1
respondent out of 31 have stated the fact agile approach is not required for financial operation
in the banking organizations. It has been identified that agile approach is often used by
organizations in the recent time to speed up the organizational operation. In the literature
review, it was studied that divergent interest of business as well as risk management might
create the inherent tension for financial institutions for banks in restructuring the credit
processes (Zeinalizadeh, Shojaie and Shariatmadari 2015). It was studied that One Eastern
European organizations found that its month-long project to make simplification of
corporate-lending techniques which are little heavy because of legitimate internal interest. As
the result, bank organizations become blogged down with the individual silos which optimize
for their own interest instead of collaborating on optimizing consumer experience and all this
happens because it lacks an agile approach. It was also studied that agile project delivery is
strongly necessary for the effective credit digitalization and onset is more of cross-functional

49DISSERTATION ON INFORMATION TECHNOLOGY
and dedicated teams empowered with the decision-making authority (Acemoglu and Restrepo
2018). So, it can be stated that executive approach is no longer a new approach as
organizational bodies are mostly dependent on the agile approach. It was studied in the
existing paper that most of the executives are actively seeking the agile approach, not all of
them are actively doing it. It was also studied that several organisations adopting the cosmetic
agile approach in the business process, while the approach of project management are
equipped with agile lingo but essentially core changes in some ways of working are not
properly adopted.
8. Which of the following is done by AI for financial operation in Emirates NBD
bank?
Row Labels Count
of 9.
Managing financial
activities
5
None of the given options 1
Risk assessment 2
Speeding up the overall
operation
23
Grand Total 31

50DISSERTATION ON INFORMATION TECHNOLOGY
Table 8: Activities AI for financial operation in Emirates NBD bank
Graph 8: Activities AI for financial operation in Emirates NBD bank
The collection of primary data presented with graph and table helps to observe the fact in
which almost 5 members out of 31 have stated that financial activities are done by AI
technology in NBD, while almost 2 respondents have stated that 2 risk assessment is done by
AI, and almost 23 respondents have stated that AI based technology is used in the
organization to speed up the overall organizational process. Eventually, 1 respondent out of
31 have stated that the fact none of the above of the activities are done by AI. This primary
findings of the paper indicate the fact that AI activities are often done by AI based technology
in the banking organizations. Existing papers on risk assessment indicate the fact that AI is
learnt from the existing data, it can be stated that AI could succeed in financial service
domain where booking keeping as well as records are usually the secondary nature to thee
business. For instance, today customers mostly use the credit card and banks often use the
credit score as the way of deciding who can be eligible for a credit card and the persons who
cannot. It was also found in the literature review that there is a state of challenges in the use
of AI technology, especially when it comes to assessing the risks. Most common challenge

51DISSERTATION ON INFORMATION TECHNOLOGY
here is possibly the inability to deal with organizational silos and a cross functional team with
the business, risk, operations is highly essential for many reasons such as the collaboration
across several functions that help to strike the balance of consumer journey as well business
objectives with suitable credit decisions making as well risk control.
9. How far do you agree that use of AI technologies is a risk consumers with respect
to privacy?
Row
Labels
Count
of 10.
Agree 26
Disagree 3
Strongly
agree
1
Strongly
disagree
1
Grand
Total
31

52DISSERTATION ON INFORMATION TECHNOLOGY
Table 9: Use of AI technologies is a risk consumers with respect to privacy
Graph 10: Use of AI technologies is a risk consumers with respect to privacy
The collection of primary data helps to observe that almost 26 respondents out of 31 have
stated that AI technology is a risk to consumers in terms of privacy. On the other side, almost
3 respondents out of 31 have stated that disagree to this fact and almost 1 respondent out of
31 have stated that almost 1 respondent out of 31 have stated that they strongly disagree to
this fact. However, this fact that is contrary to the findings found in the literature review, risk
associated with AI is usually divided into two different categories such as macro financial
risks and micro-financial risks. So under macro financial crisis, it is often seen that Machine
learning from data is more of an engine behind Artificial Intelligence and it could allow a
model to be continually enhanced by gathering, counting and analysing data; thereby, the
amount of personal data which is recorded is rapidly increasing (Leek and Peng 2015). Data
is the risk from disclosure as well as abuse of personal information considering hacker’s
attack. Data collected or calculated, for example, by AI models might lead to prejudice or
bias in result, thus, it can be added that policies to protect customer privacy and data security

53DISSERTATION ON INFORMATION TECHNOLOGY
thus necessary. Even though there is a strong growth in the AI technology but when it comes
to technology, there is always a loophole for the anonymous threats.
10. Which of the following is presently alarming for Emirates NBD bank?
Row
Labels
Count
of 11.
Macro
Risk
14
Micro
risk
17
Grand
Total
31
Table 10: Risks that are presently alarming for Emirates NBD Bank
Graph 10: Risks that are presently alarming for Emirates NBD Bank
The above presented graph and table helps to observe that almost 55% of the respondents
have mentioned that risk found in the micro environment is more threatening is to Emirates

54DISSERTATION ON INFORMATION TECHNOLOGY
NBD bank, while almost 45% of the respondents out of 31 stated the fact that risk found in
the macro environment is more challenging and threatening to organization. Nonetheless, this
fact is contrary to the findings found in the literature review because risk in both areas
requires attention and it should be considered at the highest priority. When it comes to macro
environment, financial risk is one such thing that should not go unnoticed. In the literature
review, it was found that balance and stability in the financial market could be at risk (Alwan
and Al-Zubi 2016). Particularly, when a growing number of market participants adopt AI
technology at the same time, machine learning are most likely to outperform any other
businesses in the sector and most importantly traders could adopt some similar learning
strategies. It creates some financial barriers. So, it is worth stating that estimated patterns in
machine learning trading approaches could be vulnerable to manipulation of price in the
market by fake agents. It has also been identified that percentage of perception towards both
macro and micro environment has no much difference. Moreover, risk particularly for the
financial institution is worth considering. In the literature, it was found that for most of the
people, AI decision-making technique is more of a black box and lack of transparency makes
it more difficult for regulators and investors to analyse the potential issues in the technique;
this means if the decisions made by AI result in market losses, it could be challenging to
explain the responsibility (Korinek and Stiglitz 2017). On the contrary, risk in macro
environment, risk of market loophole is worth considering. In the previous study, it was
found that AI is used greatly in a high frequency business, there could be a large scale of
transactions at the same time, which could lead to enhanced market volatility.
11. What according to you is a significant opportunity that AI brings about? Choose
from the following
Row Labels Coun

55DISSERTATION ON INFORMATION TECHNOLOGY
t of
15.
ATMs 1
Customer Support and Helpdesk 3
Fraud detection 1
Risk Management 12
Security 14
Table 11: Significant opportunity that AI brings about
Graph 11: Significant opportunity that AI brings about
Collection of primary data presented in the graph and table indicate the fact that
almost 45% of the respondents have stated the fact that when it comes to opportunity from AI
security is one such thing often provided by AI based technology. On the other side, almost
39% of the respondents out of 31 have stated that risk management is one of the significant
opportunities that banking organizations is most likely to consider. Apart from this, almost

56DISSERTATION ON INFORMATION TECHNOLOGY
10% of respondents have stated that customer support is one of the most significant
opportunities that organizations must find when adapting to AI trends. Almost, 3% of the
respondents stated that almost 3% of respondents mentioned that AI technology helps to
identify the fraudulent activities that often take place in banking operations. On the other
side, in the literature review, it was found that stakeholders’ unwavering commitment to
come together as well as contribute to ongoing effort on future proofing regional baking
industry is derived from enthusiastic involvement of industry regulators, experts and
executives.
While the banking organizations across several nations and in the regions focusing on
technology transformation, experts mentioned that the adoption of new technologies is no
longer an option instead it has been imperative for banks as well as growingly banks and
financial institutions are transforming into technology based organizations (Prisecaru 2016).
Risk management is one such activity which can be treated as the opportunity for the banking
organizations. Grouping of organizational people play the role of resolving risk related issues
in the banking organization. It was found that grouping people in such way might not be
effective for the business and in such filed, AI plays a great role because the data is driven
and the data is highly dependent and it can be scanned through such records that provide AI
the capability to make suggestions about the loans and credit related offering
12. How far do you agree that customer service can be enhanced by the use of AI in
the banking sector?
Row
Labels
Cou
nt of
15.

57DISSERTATION ON INFORMATION TECHNOLOGY
Strong
ly
Disagr
ee
1
Disagr
ee
3
Neutr
al
1
Strongly Agree 12
Agree 14
Grand
Total
31
Table 12: Customer service can be enhanced by the use of AI in the banking sector

58DISSERTATION ON INFORMATION TECHNOLOGY
Graph 12: Customer service can be enhanced by the use of AI in the banking sector
Collection of primary data helps to observe the fact that almost 25 respondents out of 31
agree to the fact that customer service in the banking organizations can be enhanced by AI
technology in the banking organizations. Only 2 respondents out of 31 have stated that
disagree to the fact. This findings is quite similar to the findings found in the literature review
as Moro, Cortez and Rita (2014) found that since there is an increasing automation, there is
always fear of reduced loyalty because of limited personal contact. Increased AI usage does
not essentially mean less personalized experience, in fact, banks could use AI to enhance
client satisfaction, enhance efficiency as well as maintain consumer loyalty in several ways
(Wu, Chen and Olson 2014). Bank of America has already manufactured a chatbot which is
called Erica and AI enabled tool that provides financial guidance for banks’ consumers for
bank’ customers through voice and text message. Such service accessible 24X7 and it could
perform regular transactions. So, it is worth stating that customer service can be enhanced by
AI based technology in the banking organizations. The above stated device allows consumers
to have access to service at any time without costing more hiring consumer service personnel.
Such function could allow the clients to gain access service anytime without costing more
money hiring consumer service personnel. 13. What are the common advantages of using
AI in banking sector? Please choose from the following
Row Labels Count
of 15.
ATMs 1
Customer Support
and Helpdesk
3
Fraud detection 1
Risk Management 12

59DISSERTATION ON INFORMATION TECHNOLOGY
Security 14
Grand Total 31
Table 13: Common advantages of using AI in banking sector
Graph 13: Common advantages of using AI in banking sector
It has been identified that almost 14 respondents stated that almost 14 respondents out of 31
have stated that security towards the financial transaction is one of most important advantage
that banks might have to gain when dealing the financial transactions. On the other side,
almost 12 respondents out of 31 have stated that the fact that risk management is a significant
advantage that banks should gain. Conversely, 3 respondents out of 31 have stated that
customer support and help desk is a significant advantages. On the other side, when it comes
to fraud detection, it was found that avoiding fraud as well as money laundering could be
challenging for several financial organizations and Artificial Intelligence holds the potential
to guide banks to become more efficient in the technique of tracing fraud as well as money
laundering (Acemoglu and Restrepo 2018). This fact clarifies that increased AI usage does

60DISSERTATION ON INFORMATION TECHNOLOGY
not essentially mean less personalized experience, in fact, banks could use AI to enhance
client satisfaction, enhance efficiency as well as maintain consumer loyalty in several ways.

61DISSERTATION ON INFORMATION TECHNOLOGY
Chapter 5: Conclusion and Recommendation
5.1 Conclusion
In conclusion, it can be stated that AI technology plays a great role in financial
transactions. This means that banking organizations across UAE regions have started to adopt
AI based technology. This research project was performed on the basis of the objectives
developed in the introductory chapter of the dissertation. One of the objectives is about
understanding about the risks that appear due to the execution of implementation of
automation. Findings indicate that a large percentage of the employee mentioned that that risk
found in the micro environment is more threatening. In the macro environment, macro
environment, financial risk is one such thing that should be considered at the highest priority.
Balance and stability in the financial market could be at risk. When a growing number of
market participants adopt AI technology at the same time, machine learning are most likely to
outperform any other businesses in the sector and most importantly traders could adopt some
similar learning strategies. So such situation could create financial barrier that banks must
have to deal with. Estimated patterns in machine learning trading approaches could be
vulnerable to manipulation of price in the market by fake agents. AI decision-making
technique is more of a black box and lack of transparency makes it more difficult for
regulators and investors to analyse the potential issues in the technique; this means if the
decisions made by AI result in market losses, it could be challenging to explain the
responsibility.
Thus, it can be stated that when using AI based technology in the banking operations,
risk is one such thing that organizations have to deal with. Moreover, when the risks are
considered in particular, it is learnt that financial market risks could generate significant
business threats to organizations in the sector. AI is highly reliant on a small number of third

62DISSERTATION ON INFORMATION TECHNOLOGY
party technology providers, it might certainly pose challenge for financial institutions.
Likewise, risk was also found in the field of customer privacy. Machine learning from data is
more of an engine behind Artificial Intelligence and it could allow a model to be continually
enhanced by gathering, counting and analysing data; thereby, the amount of personal data
which is recorded is rapidly increasing. So, it should be noted that even the use of AI based
technology is not free from the risk of customer privacy. In the context of risk, it can be
added that AI technology in the banking organizations is not immune to the potential risks.
Second objective of the dissertation about the automation process would be about
having upon different services and the ways of competing with the Fintech who entered the
market. It has been identified that implementation of Fintech technologies have led several
sector experts to believe that finance profession have peaked and there could be requirement
for fewer finance professionals that goes forward. Findings indicate that Fintech Partnership
is another significant area in the field of AI which gain tremendous attention in banking
organizations. Fintech partnership have enabled banking organizations to enhance and
develop capabilities and present new consumer offering rapidly. It has also been identified
that there are certain advantaged that Fintech could bring such as the complete capability and
data feeds for end to end journey in the emerging market, experience in lending new
approaches such as credit decisions of small and medium size enterprise decisions through
the use of alternative sources. Advantages of partnership have certainly helped the global
bank which has developed a strong digital-lending offering and then worked with the
developed SME lending fintech to generate software platform for consumer journey. So, it is
worth telling that automation process derived from the AI technology provides enormous
amount of advantages.
Artificial Intelligence is one such thing that could disrupt diverse sectors but
economic institutions are anticipated to generate benefits for the incorporation of AI systems

63DISSERTATION ON INFORMATION TECHNOLOGY
in net few years. It is worth telling that large financial institutions have enormous burden of
consumer success, so generally they tend to look towards the automation of consumer service
with the chatboat. Banking organizations such as hedge funds are adopting AI above the new
layers of data sources and insurance organizations enhancing risks models with the Artificial
Intelligence. AI usage does not essentially mean less personalized experience, in fact, banks
could use AI to enhance client satisfaction, enhance efficiency as well as maintain consumer
loyalty in several ways.
It has also been identified that Bank of America has already manufactured a chatbot
which is called Erica and AI enabled tool that provides financial guidance for banks’
consumers for bank’ customers through voice and text message. Findings indicate that
chatbot certainly helps to make sure that less-typical questions hold a ready-made responses
versus the presents status quo where advisors where the advisors often have to consult the
experts for sudden advices. Moreover, with the use of AI transactional as well as other data
sources can be tracked to understand a customers’ behaviour as well as preferences to
enhance their experience. When it comes to popularity and acceptance of AI in banking
organizations, finding clarify the fact that large organizations have grasped the significance
of innovation as well as the application of AI in their businesses; and they have started to
reap benefits while the small and medium sized institutions started to adopt. However, it has
also been identified that even though AI technology provides tremendous opportunities with
respect to service quality and security, it is worth mentioning that there are certain risks
associated with the AI, which, the large organization can deal with but potential challenges
faced by smaller organizations in adopting AI is the deficiency of talents.
5.2 Recommendation
It has been identified that banking organizations today to minimize costs, meet
margins, as well as exceed consumer expectations through personal experience. So, in order

64DISSERTATION ON INFORMATION TECHNOLOGY
to enable this, executing AI is particularly significant. Banks should start embracing AI and
related technologies across the globe. It has been identified that dawn of mobile technology,
data availability and the explosion of open source software provide AI enormous payment
field in the banking organizations and here this changing dynamics of an app-driven world is
certainly enabling the banking sector to leverage AI as well as integrate it quite effectively
with the business imperatives.
AI in consumer service
Particularly, AI should be implemented in customer service and it is noted that
automated AI –powered customer service gains strong traction. So, the organization should
gather data from users’ devices and hence AI depends upon the information using machine
learning by minimizing users to the sources. AI related features enable the services, offer
insight in line users’ behaviour and requirement. It is noted that cognitive machine should be
adopted by organizations and it should be trained to advise as well as communicate
particularly by analysing users’ data and information.
Fraud and risk management
It has been identified that online fraud is more of a massive concern for the businesses
because they digitize at certain ranges and the risk management at the internet scale would
not be managed with the use of information system. As most of the organizations should look
to deploy machine or deep learning as well as predictive analytics to investigate all
transactions in the real time, machine learning should play a great role in banks’ operation.

65DISSERTATION ON INFORMATION TECHNOLOGY

66DISSERTATION ON INFORMATION TECHNOLOGY

67DISSERTATION ON INFORMATION TECHNOLOGY
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