The Thesis

On.
Assessing the impact of Artificial Intelligence in Financial Institutions

Department of Finance
Faculty of Business Studies
Chittagong University Center For Business Administration

Submitted To:

Dr. Mohammad Hasmat Ali

Professor
Department of Finance
Faculty of Business Studies
University of Chittagong

Prepared By:

Ismita Afrin Farsi

Batch: 22nd
ID: BG233071509
Major: Finance
Section: BG
Program: MBA

Date of Submission: 29/12/2024

 Letter of Transmittal

December 29, 2024

To,
The Honorable Supervisor
Dr. Mohammad Hasmat Ali
Professor
Department of Finance
Faculty of Business Administration
University of Chittagong.
Subject : Submission of Thesis.
Dear Sir,
With due respect and humble submission of thesis on “ Assessing the impact of Artificial
intelligence in financial institutions ”. I put my best efforts to make this thesis a successful one. It
has been an enjoyable and enlightening experience for me to prepare this thesis. However, this
has obviously been a great source of learning for me to conduct similar types of studies in future.
I would like to express my sincere gratitude to you for your guidance and suggestions in
preparing the thesis.
Therefore, I would like to request you to take steps to evaluate my thesis and oblige thereby.

Sincerely,
Ismita Afrin Farsi
ID- BG233071509
Batch- 22
Major: Finance
Program- MBA

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 Acknowledgement

This thesis would not have been possible without the blessings of almighty AllAH, who led me
to this journey and gave me this opportunity not only to learn but also build the determination to
complete this thesis.

I feel honored to lay my sincerest gratitude to my honorable supervisor, Dr. Mohammad Hasmat
Ali for his continuous encouragement, inspiration and guidance to shape this study.

Secondly, I want to express many thanks to my family members and friends for believing in me
and giving me unconditional support.

Lastly, I am very much grateful to all faculty members of Chittagong University Center For
Business Administration for their essential contribution and cooperation in advancing the thesis.

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 Executive Summary
The financial services industry is transforming with the integration of artificial intelligence (AI),
particularly in financial technology (FinTech). AI enhances efficiency, accuracy, and security,
significantly modernizing traditional financial operations. Key AI technologies like machine
learning, natural language processing, and predictive analytics empower financial institutions to
improve decision-making, streamline processes, and deliver personalized customer experiences.

AI automates labor-intensive and error-prone tasks, efficiently processing vast amounts of data
for tasks such as investment strategy formulation, risk assessment, and fraud detection. This not
only enhances operational efficiency but also reduces costs for financial organizations. Real-time
data analysis enables AI systems to provide valuable insights to investors, helping them make
informed decisions to maximize returns.

Customer interaction in financial services has also been revolutionized by AI through the
development of chatbots and virtual assistants. These tools offer personalized, efficient
assistance by addressing customer queries, providing financial advice, and aiding in personal
financial management, thereby improving customer satisfaction and engagement.

However, the growing reliance on AI in FinTech raises concerns regarding ethics, data security,
and privacy. Financial institutions must ensure the protection of sensitive data and maintain
transparency in AI algorithms whAile handling large volumes of consumer information. The
study aims to analyze and compare the financial performance of AI-driven financial services
with traditional methods, highlighting AI's impact on the sector.

Finally, I summed up the whole topic and specified them to different chapters and parts.

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 Table of Contents
Serial No. Particulars Page No.

Cover Page

Letter of Transmittal I

Acknowledgement II

Executive Summary III

Table of Contents IV - V
Chapter- 1
1
Introduction
1.1 Background of the study 2

1.2 Research objectives 3

1.3 Scope of the study 3

1.4 Methodology of the study 4

1.5 Problem statement 4

1.6 Chapter summary 5

Chapter -2
6
Review of Literature
2.1. Introduction of AI in finance 7

2.2 Functions of AI 7

2.3 Benefits of AI in financial sector 9 - 10

2.4 AI impact on financial performance and financial services 10 - 11

2.5 AI voluntary disclosure and financial performance 11 - 12

2.6 AI implications in the banking industry 12 - 13

2.7 Case studies and industrial examples 13 - 14

2.8 Challenges of AI in financial industry 14 - 15

2.9 Current Application of AI in Bangladesh’s financial sector 15 - 16

2.10 Brac bank uses AI for the selection of young leaders 16

2.11 Brac bank implementing AI transformation within financial sector 17

2.12 Chapter summary 17

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 Chapter -3
18
Research Methodology
3.1 Introduction 19

3.2 Research Design 19 - 20

3.3 Research Approach 20

3.4 Data Analysis 20 - 21

3.5 Data collection 21

3.6 Validity 21

3.7 Ethical consideration 21

3.8 Chapter summary 21 - 22

Chapter -4
23
Result and Discussion
4.1 Introduction 24

4.2 Data collection techniques 24

4.3 Descriptive Analysis 24 - 27

4.4 The findings from likert scale 27

4.5 Theoretical implementations 28

4.6 Managerial implications 29

4.7 Chapter summary 30

Chapter -5
31
Conclusion, Recommendations and furture research
5.1 Introduction 32

5.2 Research outcome and findings 32 - 33

5.3 Recommendations 33

5.4 Limitation of the study 33 - 34

5.5 Scope for future research 34 - 35

5.6 Conclusion 36

5.7 Chapter Summary 36

References 37 - 38

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Chapter: One
Introduction

1
1.1 Background of the study

At the advent of growing economies across the world, the financial sectors today have a great
burden and face increasing challenges to be managed and to satisfy growing demand of
consumers (Jeucken & Bouma, 2017). Rapid technological thrust forward with new demands to
the current financial industry (like banking sectors making it imperative to cope up with the
current corporate and consumer spheres. The competence and ability of the financial sectors
needs continuous upgrading. At the present time, the term “AI” illustrates a wide range of
technologies that power many of the services and products we operate every day. AI is an
umbrella term that encircles a wide variety of technologies. Due to the impact of globalization,
the banking sector has the responsibility of revolutionizing their operations and service delivery
system through initiating innovations (Roy,2015)

Artificial intelligence has a longstanding presence in the financial services industry, marked by
continuous evolution and innovation. This section reviews the historical development of AI in
finance, covering foundational concepts, key technologies, and the factors driving its widespread
adoption. AI's roots in finance go back to the 1950s, with early efforts to use computational
techniques for financial modeling and analysis.

The significant advancement and practical application of AI started to gain momentum in the
21st century with the advent of more powerful computing systems and availability of large
amounts of data. (Ratia et al 2018, HelenLien and Kaplan 2019).AI is the process of human
intelligence by machines.Al promotes sustainable and effective use of resources ( Nikitas 2020).
The findings of different literature reviews reveal the most powerful engine for the growth of an
organization is nothing but technological advancement and continuous exercise on it. It does not
matter whether it is in individual or at organizational levels, Technology is an essential driver for
success.

In Asian countries, the banking industry keeps facing radical changes due to different speed of
growth in the economy and financial sectors. Competition has become the word of the day at the
advent of globalization. Currently Al plays a vital role in autonomous decision-making process,
monitor assets, process in real time and enables value creation (Alcacer and Cruz-Machado
2019) and the benefits will increase going forward (Cockburn 2018). While AI can improve
financial reporting, it can also lead to biases, lack of transparency, data privacy concerns, and
compliance challenges. Organizations may face job displacement, training gaps, high
implementation costs, interoperability challenges, and ethical concerns (Nguyen 2022). To
mitigate these negative effects, organizations should prioritize responsible AI practices, invest in
data quality and governance, and address potential biases in AI models. Staying informed about
regulations and ethical considerations is also crucial (Nguyen and Dang 2023).

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Thus, the motivation behind the study is the contradictory results from some or many earlier
Literatures. This phenomenon has given rise to further research in order to understand that AI
applications are favorable for the banking sector, as well as for increased efficiency in the
financial sector, which leads to economic benefits. Clear and ethical communication about AI
initiatives can also enhance a company’s image as a responsible innovator, fostering trust among
consumers and partners (Hasan et al. 2023). On the other hand, a lack of transparency or
negative perceptions could lead to skepticism and a tarnished reputation, potentially affecting
financial performance. In addition, this study is motivated to provide valuable insights that
contribute to the discourse on AI’s role in shaping contemporary business success, offering a
comprehensive perspective that encompasses ethical, regulatory, financial, and reputational
considerations. Depending on the timing of the study, there could be a lack of empirical evidence
regarding the direct relationship between AI disclosure and financial outcomes.

Therefore, the study based on the different findings from past research aims to close the gap with
respect to the antecedent which may increase technical behavior of machines that tranquil
employees working procedures.

1.2 Research Objectives

The objective of this research paper is to explore the role, impact, and future trends of AI in
financial services.
Specifically, the thesis aims to -
1.To Provide in-depth analysis of AI applications within the financial service industry.
2. To analyse the impact of AI technologies, improving efficiency, accuracy, and customer
experience within the financial sector.
3. Examine the challenges, benefits, implications of AI adoption for financial institutions and
stakeholders.
4. To investigate the current applicationre in Bangladesh's financial industry.
5.To identify the exploration in the field of AI in finance and areas for future research.

1.3 Scope of the study

The main focus of the study falls on assessing the impact of Artificial intelligence in the
financial sectors. The study is motivated due to the fact that organizations are operating
increasingly at competitive speed and complex environments with the help of artificial
intelligence (AI). Moreover, the study gives a contribution to financial sector practitioners who
are transforming their operations using AI mechanisms and supports the need for more AI
disclosure and informed decision making in a manner that aligns with the objectives of financial
institutions.
The thesis aims to provide insights into how AI could transform the future of financial services.

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1.4 Methodology of the study

Methodology is the process that ensures readers about ways of collection of data for the report.
The methodology of this thesis composed such components.

When preparing a thesis, it is necessary to have a suitable research method in order to collect the
research data. There are also two methods of research like qualitative and quantitative.
Qualitative research aims to provide a complete, detailed description of the research topic while
quantitative research concentrates more on counting, classifying features and constructing
statistical models and figures to expand what is observed. In this report the research method is
quantitative. The study is performed based on the information extracted from different sources
collected by using a specific methodology. This report is analytical in nature.

This thesis is basically formed by secondary data which is collected from the Internet, different
websites, annual reports, different kinds of books, journals , articles, newspapers and so on.

1.5 Problem statement

The increasing prominence of AI-driven FinTech companies highlights their growing importance
in the financial sector. These companies leverage AI algorithms and data analytics to offer
innovative financial products and services, disrupting traditional financial institutions. Despite
their rise, there is a lack of comprehensive understanding regarding their performance and
impact, particularly in areas such as financial performance, technological advancements, market
competitiveness, and customer satisfaction.

Assessing the financial health and sustainability of these companies involves analyzing
indicators like revenue growth, profitability, return on investment, and funding raised.
Technological advancements are also crucial, focusing on the effectiveness of AI algorithms,
data analytics capabilities, and the infrastructure used to deliver innovative solutions. Market
competitiveness looks at the positioning, market share, and competitive advantages these
companies hold in relation to traditional financial institutions and other FinTech players.
Additionally, understanding customer satisfaction is vital for evaluating how well these solutions
meet consumer needs and enhance user experiences.

The study also aims to identify the factors influencing the success or failure of AI-driven
FinTech companies, such as technological innovation, regulatory compliance, strategic
partnerships, talent acquisition, and market demand. The findings will be valuable for
stakeholders, including investors, regulators, policymakers, financial institutions, technology
providers, and consumers. These insights can help investors make informed funding decisions,

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guide regulators in developing suitable frameworks, and assist industry stakeholders in crafting
strategies for sustainable growth and innovation.

Overall, the research provides insights into the drivers of success in the AI-driven FinTech space,
which can inform strategies for fostering innovation, achieving sustainable growth, and
addressing challenges in this rapidly evolving landscape.

1.6 Chapter Summary

The main purpose of the study is highlighting Artificial intelligence has significantly impacted
the global financial sector. From automating processes to enhancing customer service, several
institutions of the country adopt AI for operational efficiency. However, AI optimization is
required to unlock more value in Bangladesh, specially in areas like financial institutions.

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 Chapter : Two
Review of Literature

6
2.1 Introduction of AI in Finance

Artificial intelligence (AI) has been a topic of discussion since the 1950s, but recent
developments in big data, data analytics, and supercomputing have brought it into the spotlight.
In finance, AI is widely used to gain insights, measure performance, make predictions, perform
real-time calculations, enhance customer service, and retrieve information intelligently. It
consists of technologies that help financial organizations better understand markets and customer
behaviors, learn from digital interactions, and engage with clients in ways that resemble
human-like intelligence on a large scale. AI's key contributions in finance nclude personalizing
products and services, identifying new opportunities, managing risks and detecting fraud,
promoting transparency and regulatory compliance, and streamlining operations to cut costs.

AI will be a major driver of growth in financial services, supporting digital advancements that
enhance sales, streamline processes, and maximize data use. Moving forward, organizations will
need to scale personalized, relationship-centered customer interactions. AI will enable financial
institutions to offer tailored responses, recommend products and services more safely and
responsibly, and build trust by expanding around-the-clock concierge services.
Additionally, financial institutions must create comprehensive, permission-based digital
customer profiles, but their data is often siloed. By breaking down these barriers, applying AI,
and integrating human interaction, they can craft unique, scalable experiences that meet the
specific needs of each customer.

2.2 Functions of AI

AI systems operate through a combination of some components like - data collection, data
processing, model training, model evaluation, deployment, feedback and improvement.AI is
driving significant efficiency improvements across various functions, reducing customer support
response times by 33% and halving some code-generation times. AI serves multiple functions
which are :

1.Automation : Industries have increasingly used technology to boost productivity and

lower production costs by automating repetitive tasks. By reducing human involvement,
machines and computers can now handle routine activities and adjust to changing
conditions. This trend of automation has become common in both manual labor and
office-based roles.

2. Machine learning : Machine learning is a transformative concept where machines

analyze large datasets to learn from the information and enhance their algorithms over
time. A key aspect of machine learning is neural networks, which are inspired by the
structure of the human brain. These networks consist of interconnected nodes, known as

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neurons or perceptrons, which work together to process and learn from data. ( Shaan Ray,
published in codebrust, Aug 17, 2018). Neural networks can recognize patterns and
improve their ability to categorize new data through experience. For instance, a neural
network designed to identify dog breeds will analyze labeled images of different dogs,
gradually learning to associate specific features with each breed. Through this process,
the machine becomes increasingly accurate in its identification. ( Shaan Ray, published in
codebrust, Aug 17,2018)

3.Problem solving and decision making

A core goal of AI is to create systems capable of analyzing vast datasets, recognizing
patterns, and making decisions based on data insights. This skill in efficient
problem-solving and decision-making is highly valuable across sectors like healthcare,
finance, transportation, and manufacturing.( By Sneha Kothari, simpli learn, sep 17,2024)

4.Customer Support
Generative AI offers more than just automation and efficiency in customer support; it can
also proactively reduce the need for support by predicting and resolving potential issues.
Its applications in customer service include analytics to anticipate problems, chatbots for
self-service, algorithms for matching customers with suitable agents, and tools to help
agents work more efficiently. Generative AI can speed up agent response times by up to
35%, assist consultants by organizing knowledge sources, and improve service quality by
up to 40%. For instance, a tech and manufacturing firm created two AI-based prototypes
for field services: a maintenance assistant to enhance technician productivity and systems
to process vast, varied sensor data, aiding emergency responders in decision-making.

5.Fraud Detection:
AI systems identify fraudulent transactions by examining patterns in financial
data.(Samir bazatu, software engineer at spacex,May 2024).Banks have long used
machine learning techniques within their credit card portfolios, as transaction data offers
a valuable foundation for training unsupervised learning models. These models are
particularly effective in detecting credit card fraud, given the extensive data available for
development, training, and validation. Workflow engines within credit card payment
systems continuously monitor transactions to evaluate fraud risk. The detailed transaction
histories allow banks to identify distinct characteristics that differentiate fraudulent from
legitimate transactions.

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2.3 Benefits of AI in financial sector

AI in finance has improved process efficiency by minimizing manual tasks and supporting
various areas, such as market research, data analysis, and more.Here are few benefits of AI for
finance :
● Cost reduction:
In finance, labor costs—particularly in compensation and benefits—represent a major
expense for many institutions. AI in accounting can significantly boost worker efficiency
and productivity. For instance, Decision Management Systems (DMS) enable quicker,
more informed decision-making. DMS can also streamline customer onboarding by using
preset responses to common questions, reducing the need for front-line employees.
Additionally, by standardizing data entry, DMS minimizes human errors, which can
protect a company's reputation and avoid costly government fine.

● Automating the investment process

AI is transforming investment decision-making in retail banking by streamlining
processes and uncovering new opportunities. Financial organizations now use AI for
tasks like identifying investment prospects, enhancing algorithmic trading strategies, and
delivering personalized advice through robo-advisors. With machine learning and
predictive analytics, AI tools can reveal patterns and insights that human analysts might
miss, leading to better-informed investment choices. However, integrating AI into
investment management comes with challenges, particularly around model transparency.
Financial institutions must ensure their AI systems are understandable to meet regulatory
standards and provide clear justifications for recommendations.

● Evolutionary improvement :
For over a decade, investment firms have used machine learning and neural networks to
drive high-speed, automated trading, especially in highly liquid markets. The recent
development of large language models (LLMs) is advancing this capability further,
allowing investors to process vast amounts of unstructured, text-heavy data to boost their
analytical power. This advancement not only enhances broad forecasting but also aids
quantitative investors in quickly analyzing complex documents like bond agreements and
earnings reports, leading to better price discovery across various assets. (Tobias Adrian,
September 6, 2024)

● Improving financial forecasting

AI-based forecasting models are extensively used in the finance sector to improve
prediction accuracy by incorporating diverse traditional and non-traditional data
sources.AI-based models focus on predicting company bankruptcies, stock price changes,

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 and credit risk. AI models also play a role in asset return predictions, with Li and Mei
(2020) using deep learning with multiple hidden layers for asset forecasting, and Ruan et
al. (2020) applying machine learning to gauge stock market returns based on investor
sentiment. Petrelli and Cesarini (2021) combined AI methods to predict high-frequency
asset prices. In the insurance industry, AI has been adopted for claim prediction. For
example, models can predict reimbursement outcomes for claims related to accidents,
property damage, or medical emergencies. Both feed-forward and recurrent neural
networks are used, enabling insurers to analyze relationships between test data and
standardized data for annual claim predictions (Rawat et al., 2021).

● Automating key business process in customer service and insurance

Financial institutions have increasingly adopted Robotic Process Automation (RPA) to
streamline core processes and enhance customer service, especially in retail banking and
wealth management. These RPA tools often function as robo-advisors, providing
automated financial services such as tax guidance, account opening, insurance
recommendations, and investment advice (Wittmann & Lutfiju, 2021; Kruse et al., 2019).
By leveraging an AI-first approach, banks can expand into new client segments, reduce
acquisition costs, and increase usage of existing services, all while meeting customer
demand for convenient, round-the-clock service (McKinsey, 2020). AI-powered RPA
allows banks to offer personalized services, enhancing user experience and reducing
decision fatigue for customers. This strategic emphasis on RPA helps banks stay
competitive, improve profitability, and operate more efficiently by integrating real-time
analytics and messaging, which requires modernized IT systems for processing client
data (Zeinalizadeh et al., 2015; Villar & Khan, 2021).

2.4 AI impact on financial performance and financial services

AI's integration in the financial sector has begun to improve efficiency, drive new services, and
enhance revenue for institutions. However, quantifying AI's impact on bank performance is
challenging due to difficulties in measuring AI-specific contributions. This raises questions about
AI's broader effects on businesses, consumers, and the economy.

When it comes to disclosing AI practices, particularly in banking, disclosure remains mostly

voluntary. With no established standards, companies independently decide what AI-related
information to share. Financial services with high data processing needs often lead in disclosing
AI use, especially in natural language processing. However, there is no unified framework
guiding these disclosures, and current practices fail to fully address AI's unique impact.

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The OECD emphasizes AI's impact in financial markets, particularly in increasing
competitiveness through cost efficiency, productivity, and enhanced service quality. By
streamlining processes, automating tasks, and strengthening risk management, AI can lead to
higher profitability, better decision-making, and improved regulatory compliance. The
technology allows for new, customized financial services, improving customer experiences and
creating opportunities for personalized solutions like robo-advisors.

AI adoption also reduces the need for low-skilled labor, boosting the productivity of remaining
employees. Many banks report benefits like cost reduction, revenue growth, and operational
improvements, especially in lending, fraud detection, and compliance. While AI offers
substantial economic advantages for both stakeholders and shareholders, challenges remain in
measuring its direct impact on bank performance, leaving questions about its broader effect on
businesses, consumers, and the economy.

2.5 AI voluntary disclosure and financial performance

AI advancements have notably influenced sectors like banking, where companies are
increasingly adopting AI-driven solutions. In banking, this includes applications that process vast
volumes of text and speech data, particularly for customer service and operational efficiency
(Sætra 2021). According to the AI McKinsey Global Surveys, financial services tend to disclose
more about their AI-driven natural language capabilities due to these high data demands.

However, AI-related disclosures remain voluntary, and decisions about what, how much, and
which aspects to disclose largely depend on each company. No universally accepted guidelines
currently exist for AI disclosure, as the field is still emerging and lacks established international
reporting standards. Current practices do not yet fully address the unique impacts that AI
technologies can have.

In today’s AI-driven world, the opacity of AI systems poses a significant risk. S. Lu (2021)
argues that current corporate disclosure frameworks in securities law have limited effect in
reducing this opacity and calls for a more effective framework to ensure transparency in AI
products and services. Enhanced disclosure is essential to reveal potential algorithmic risks,
protect stakeholders, and foster stable, sustainable markets.

Regulators are increasingly considering AI disclosure requirements. The OECD, for instance, has
emphasized the need for transparency and responsible disclosure in AI systems to enable people
to understand and question AI-driven outcomes (OECD, 2019). Consumers, especially in
financial services, should be informed if AI is involved in delivering a product, so they can make
informed choices. In 2021, the OECD further emphasized that disclosures can help financial
service providers assess whether clients understand AI’s impact on services. Likewise, the

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International Organization of Securities Commissions (IOSCO) has advocated for clearer
information on AI systems’ abilities and limits within disclosures.

The EU AI Act (2023) introduces transparency requirements for AI systems used within the
European Union, marking an important step in AI regulation that could significantly influence AI
development and usage across the region. The Act outlines requirements for transparency,
documentation, auditing, and user information provisions (Article 13). Although still in the early
stages, this legislation has the potential to shape the future of AI in both Europe and the United
States. Early preparation, including establishing effective disclosure procedures, is key to
adapting to these new standards.

2.6 AI implications in the banking industry

AI, along with advancements like machine learning, big data analytics, cloud computing, and
social media, is transforming digital business and is widely integrated into modern life.
Technology not only alters physical and operational processes but also improves efficiency,
competence, and enables forward-thinking business solutions (Tekic and Koroteev, 2019). AI’s
impact is multifaceted: it can predict and interpret the environment by processing audio, text, and
computational data. Through natural language processing (NLP), it enhances human-machine
interaction and operates independently without human intervention (Purdy and Daugherty, 2016;
Rao and Verweij, 2017; Tákacs et al., 2018; Ottosson and Westling, 2020). Additionally, AI
stands out from traditional machines due to its self-learning ability, allowing it to improve
continually based on past experiences (Öztemel and Gursev, 2020). These innovations are
reshaping competitive business strategies and contributing to new methods of value creation.

The integration of AI in banking has significantly improved decision-making, enabling

compliance with regulatory standards while enhancing accuracy and efficiency. AI technologies
help minimize false contracts, predict resource needs, and adhere to regulations (Han et al., 2020;
Couchoro et al., 2021; Garcia-Bedoya et al., 2020; Kute et al., 2021). Various AI methods,
including data mining, fuzzy logic, machine learning, sequence alignment, and genetic
programming, aid banks in reducing fraud (Raj & Portia, 2011). Autonomous data management
has boosted the speed and reliability of banking processes (Soni, 2019). Predictive analytics
tools—such as SSL, encryption, multi-level authorization, device fingerprinting, and endpoint
protection—help prevent fraud before it occurs (Kikan et al., 2019). Deep learning and artificial
neural networks allow personalized banking services by helping banks assess customer
preferences and responses to marketing (Kim et al., 2015; Zakaryazad & Duman, 2016). AI has
streamlined processes, reduced costs, minimized risks, and enhanced customer experience with
chatbots and robo-advisors.

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2.7 Case studies and industrial example

AI implementations in financial institutions, offering insights into their applications, advantages,

and challenges faced.

Examination of successful AI implementation in financial institutions -

Case 1 : JPMorgan Case
JPMorgan Chase, a leading global financial institution, has integrated AI into various aspects of
its operations to improve efficiency and customer experience. The bank utilizes AI algorithms
for fraud detection, risk management, customer service, and trading. By harnessing machine
learning and natural language processing, JPMorgan Chase has enhanced the accuracy of fraud
detection, minimized false positives, and improved customer interactions through personalized
recommendations and chatbots.

Case 2 : Vanguard
Vanguard, a prominent investment management firm, has incorporated AI-driven robo-advisors
into its wealth management offerings, delivering automated portfolio management and
investment guidance. These robo-advisors use machine learning algorithms to assess investor
preferences, risk profiles, and market trends, enabling the creation of tailored investment
strategies and asset allocations at a lower cost than traditional advisory services. This innovation
has democratized access to wealth management, appealing to a wider array of investors and
contributing to the firm's growth.

Case 3 : Blackrock
BlackRock, the largest asset manager globally, has implemented AI-driven solutions to enhance
its investment strategies, risk management, and portfolio construction. The firm employs
machine learning algorithms to analyze market data, discover investment opportunities, and
optimize asset allocations within its portfolios. These AI-enhanced strategies have provided
clients with superior risk-adjusted returns compared to traditional investment methods,
contributing to the firm’s growth.

Case 4 : Ping and insurance

Ping An Insurance, a top insurer in China, has integrated AI throughout its operations to boost
efficiency, manage risks, and enhance customer service. Using AI for underwriting, claims
processing, CRM, and fraud detection, Ping An has optimized insurance processes, shortened
claims handling times, and improved customer satisfaction. Additionally, the company uses
AI-driven predictive analytics to evaluate and reduce risks, strengthening its competitive edge
and profitability.

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2.8 Challenges of AI in financial sector

The above section highlights key issues arising from the ongoing and widespread adoption of AI
in finance. However, additional challenges will persist. If not effectively managed, these
challenges could hinder the broader implementation of AI-driven systems within the financial
industry.

1. Availability and quality of Training data

Training AI models requires extensive data to enhance their accuracy and reliability.
Large datasets allow AI to learn from diverse examples, enabling it to recognize complex
patterns and relationships that smaller datasets might miss. This abundance of data also
helps reduce the risk of overfitting, where models perform well on training data but
struggle with new data. In the financial sector, however, much of the available data
remains underutilized for AI training. Many financial services have yet to fully digitize
their processes, limiting the amount of data readily available for AI applications (BoE
and FCA, 2022; Cao, 2022; Kruse et al., 2019; Milana and Ashta, 2021). Data quality is
equally important, as incomplete, inaccurate, or biased data can undermine model
performance and lead to unfair outcomes. High-quality, consistent data is essential,
particularly for deep learning models and when data is unstructured or gathered from
multiple sources (Greenspan et al., 2016; Lee, 2017).

2. Lack of appropriate skill

Using AI presents challenges for financial organizations, as many employees lack the
specialized technical skills needed to effectively operate AI systems (Kruse et al., 2019).
These systems often require knowledge in programming, data analysis, and machine
learning, which can make it difficult for employees to use them correctly and interpret
their results. Additionally, the fast-paced advancements in AI make it challenging for
employees to stay current on the latest techniques, suggesting a need for ongoing training
and development programs. Furthermore, the adoption of AI can lead to shifts in job roles
and responsibilities, as some tasks may be automated while others may require new skills
or approaches (Culbertson, 2018; LinkedIn, 2017). Organizations, therefore, should
prepare for these changes by providing necessary support and training to help employees
transition into their evolving roles.

3. AI model development challenges

Despite recent advancements in AI, financial organizations still face difficulties in
developing accurate and high-performing AI models. Beyond general skill and expertise
issues, some challenges are inherent to the AI techniques themselves. For instance,
natural language processing (NLP) is often used in sentiment analysis to predict stock
prices or generate trading signals from financial text data (Osterrieder, 2023). However,

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 NLP faces unique challenges in interpreting language accurately. Unlike humans, NLP
models struggle to understand context when words have multiple meanings and find it
difficult to recognize different words expressing the same idea (Khurana et al., 2023).
Additionally, homonyms can complicate tasks such as question-answering and
speech-to-text applications, as they lack written context.

4. Selecting the optimal ML model

No single AI algorithm is universally effective for all problems; using an inappropriate
algorithm can lead to poor performance, inaccurate predictions, or failure to solve the
problem. Traditional methods may even outperform AI in some cases, as discussed in
section 2. Selecting the right algorithm requires understanding the problem and data
characteristics, as well as the strengths and weaknesses of various models. Organizations
often choose different algorithms based on needs for accuracy, interpretability, and data
type (Lee and Shin, 2020). Similarly, using robotic process automation (RPA) can be
challenging, particularly for financial institutions that often struggle to identify the best
use cases for RPA. Targeting overly complex processes with RPA is a common issue that
results in high costs that could be better allocated elsewhere (Lamberton et al., 2017).
This issue partly arises because many organizations lack a clear understanding of bots'
capabilities and operations (Cooper et al., 2019). Another concern is the need to protect
business processes and manage information flow across jurisdictions (Cooper et al.,
2019).

5. Requirement of better agility and faster adaptability

Agility and adaptability are essential for managing AI-related risks in the financial
industry, such as data bias, security and privacy issues, and model opacity (Thowfeek et
al., 2020). These risks can impact both businesses and customers, making it vital for
companies to respond quickly and flexibly. Additionally, companies with advanced AI
capabilities can gain a competitive edge, underscoring the need for adaptability in
adopting AI to stay relevant. As AI reshapes business operations and decision-making,
companies must adjust existing processes and structures to maximize AI's benefits.

2.9 Current application of AI in Bangladesh's financial industry

Financial institutions in Bangladesh, such as BRAC Bank and City Bank, are leveraging
AI-powered chatbots to enhance customer service by providing quick responses to routine
inquiries like balance checks, transaction notifications, and loan applications. These chatbots not
only improve efficiency but also reduce the pressure on call centers. Globally, studies reveal that
around 60% of customers prefer chatbots for basic assistance over waiting for human agents,
underlining the growing importance of AI integration in banking.

15
AI also plays a crucial role in fraud detection and risk management within Bangladesh’s banking
sector. Advanced algorithms and machine learning models monitor transaction patterns to
identify anomalies in real-time, enabling prompt action against potential fraudulent activities.

In the area of credit scoring and loan distribution, traditional methods that rely heavily on credit
history often exclude underserved populations. AI-driven credit scoring systems, like those used
by fintech companies such as bKash and ShopUp, analyze alternative data such as phone usage
and transaction behaviors. This approach has significantly boosted financial inclusion by
providing micro-loans to individuals without formal credit histories. For instance, bKash
reported increased productivity and onboarding rates through AI products like Nimonton and
Biponon, developed by Intelligent Machines, a local AI firm.

While still in its early stages, AI-powered algorithmic trading and portfolio management are
gaining traction in Bangladesh. These systems analyze stock market trends, reduce human errors,
and optimize investment returns. Efforts to modernize the Dhaka Stock Exchange (DSE)
infrastructure, including the adoption of Nasdaq's X-stream INET platform, indicate progress
toward embracing automated trading systems.

2.10 Brac bank uses AI for the selection of young leaders

BRAC Bank has integrated AI technology into the recruitment process for its Young Leader
Programme (YLP), enhancing its sustainability efforts. In a groundbreaking move for
Bangladesh, the bank conducted an entirely paperless online examination for over 50,000
candidates in the 2023 batch. AI features monitored candidate behavior during the exam to
ensure fairness, detect misconduct, and uphold assessment standards. This approach allowed the
bank to evaluate a large pool of candidates effectively, yielding reliable and unbiased results.

The YLP is a prestigious recruitment initiative that attracts graduates from diverse universities
across the country, offering robust development opportunities and fast-tracked career growth. Its
reputation, coupled with BRAC Bank's focus on ethics, transparency, and good governance, has
positioned it as a top employer for young professionals.

According to Akhteruddin Mahmood, Head of Human Resources, the use of AI in the

recruitment process reflects the bank's commitment to leveraging technology for finding the best
talent. This inclusive approach has enabled BRAC Bank to connect with candidates in remote
areas, supporting its goal of fostering growth and inclusivity in the banking sector.

16
2.11 Brac bank implementing AI transformation within financial sector

BRAC Bank began its digital transformation in 2018 to enhance customer experiences across all
segments. The first phase focused on upgrading core systems, including banking, card
management, and call centers, alongside process automation and streamlining. This resulted in
faster service, greater convenience, increased revenue, and improved operational control.

The second phase emphasizes creating differentiated customer experiences through data-driven
decision-making and predictive models. The bank’s super-app, Astha, provides over 120
services, including Astha Lifestyle, instant fixed deposits, and integrations with partners like
bKash. Users of the app enjoy free internet access while using it. Additionally, CorpNet, the
bank’s internet banking platform for corporate clients, has gained significant traction.

BRAC Bank prioritizes SME support through scalable solutions like a loan origination system
for faster processing. Digital loan services, such as Baki, Shafollo, Jibika for SMEs, and Saddho
for retail customers, further enhance accessibility. With tools like Astha and biometric-enabled
Agent Banking, the bank is driving financial inclusion by bringing banking services to remote
areas, even beyond standard operating hours.

2.12 Chapter Summary

Artificial intelligence (AI) is a cutting-edge innovation that has significantly impacted the
banking sector. Its integration has improved operations by enhancing customer satisfaction,
providing virtual assistance, and minimizing risks.The banking industry acknowledges
challenges like confidentiality concerns and a shortage of skilled professionals. Despite these
hurdles, the integration of artificial intelligence in financial services is seen as a valuable
opportunity rather than a threat, offering significant potential for positive outcomes.

17
 Chapter: Three
Research Methodology

18
3.1 Introduction

The chapter presents an overview of the research design and aims to provide explanation of the
selected methodological approach employed to address the research objectives.
This chapter introduces the research design followed by this research and also discusses the
applied methodologies to answer the research question and to test the hypothesis proposed in this
chapter. Specifically, this chapter provides a description of the research design, the measurement
of research variable, survey instrument description, the procedures of data collection and finally
technique of analyzing the data.

3.2 Research Design

In general, the research design is divided into two categories,which are qualitative and
quantitative research. Quantitative research is closely related to the classical scientific
paradigm,thus it is identified as less contentious compared to qualitative research. On the other
hand, quantitative research applies objective measurement and statistical analysis of numerical
data to examine and interpret a phenomenon that is not biased. This research mainly quantifies
how people regard an issue or feel or act so that they can be counted and model the intention or
goal of the research. Furthermore, in most cases, researchers use the research to generate
hypotheses to be proven or disapproved. (Bryman,2017)

In this regard, qualitative research mainly produces the description of feelings, stories, pictures
and sometimes emotions. Incorporate qualitative methods to gather insights into factors
influencing the success or failure of AI- driven financial companies, such as interviews, case
studies, experts opinion and so on. This research is not selected only by the choice of the
researcher but also according to the nature of the research objectives and questions. There are
some research questions that cannot be answered by quantitative methods that need to be used
qualitatively and vice versa. ( Smith and McGannon, 2018)

The quantitative research design is applied as a framework for this study as it is typically used in
science for theory verification and hypothesis testing. This type of research is characterized by
carefully developed conceptual frameworks and measurement aiming to give the data numerical
structure. Utilize quantitative methods to analyze numerical data related to the financial
performance, market share and technological capabilities of AI - driven financial institutions.
This approach allows for statistical analysis and comparison of performance metrics. Since the
idea of empirical study based on pre- specified research question, structured design and
pre-structured data, quantitative research design is more suitable for this study and the
measurements through the use of appropriate scales to provide more scientifically results
(Nardi,2018) Therefore, the quantitative research is used since it allows researchers to measure
more than one variable to meet the objective of the research. ( Nardi, 2018)

19
3.3 Research Approach

This research investigates how artificial intelligence (AI) is transforming financial technology
(FinTech), focusing on its impact within financial services. It starts by defining both AI and
FinTech, then sets an objective to explore the effects of AI in this sector. A review of existing
literature highlights AI's role in improving efficiency, accuracy, and risk management in finance.

The study employs a qualitative, quantitative, or mixed research approach, using methods like
surveys, interviews, and data analysis, with careful attention to sample size and selection. It
examines AI's transformative role in financial services, including areas such as algorithmic
trading, fraud detection, customer service, and personal finance. The benefits AI offers
FinTech—like cost savings, better decision-making, and improved customer experience—are
emphasized.

The research also considers challenges in AI adoption, such as data privacy, algorithmic bias,
and regulatory issues that may complicate implementation. Real-world case studies illustrate
successful AI applications in FinTech, providing insights into strategies and outcomes. Combine
quantitative and qualitative methods to gain a comprehensive understanding of performance
drivers and outcomes.

The objective of the research is to collect the data from a number of people who can represent
the target population. The researchers use the collection information from the survey to make
generalizations from drawn samples that represent the population. Therefore, the sample size
determination is significant since excessive,inadequate and inappropriate sample sizes tend to
have a negative impact on the accuracy and quality of research. ( Saunders, 2012)

3.4 Data Analysis

Data collection is done based on the self- administered survey. Like,

● Financial Analysis : Use financial ratio, trend analysis and benchmarking to evaluate the
financial performance of AI- driven fin-Tech companies.
● Statistical Analysis : Apply statistical techniques such as regression analysis, correlation
analysis and hypothesis testing to analyze quantitative data and identify relationships
between variables.
● Qualitative Analysis : Employ thematic analysis, content analysis and coding techniques
to analyze qualitative data obtained from interviews, case studies and expert opinions.
● Quantitative Analysis : This type of analysis is typically expressed using numerical
values. Data is represented through measurement scales, which can then be analyzed
statistically.

20
3.5 Data Collection

To analyze AI-driven FinTech companies:

● Financial Data: Collect information from sources like financial reports, databases, and
industry publications.

● Market Data: Evaluate competitiveness through market share, growth rates, and
competitive landscape analysis using market research and industry reports.

● Technological Data: Assess technological capabilities, including AI sophistication, data

analytics, and product innovation, by analyzing patents, technological infrastructure, and
conducting interviews.

● Customer Data: Analyze customer feedback, satisfaction surveys, and reviews to

measure how well solutions meet customer needs and improve user experience. Data can
be sourced from surveys, online platforms, and customer reviews.

3.6 Validity

Validity refers to the accuracy of an instrument and can be determined in two ways. First, content
validity is assessed by evaluating the clarity and readability of the items. This process involves
consulting a group of experts from both academia and industry. These experts provide feedback
on aspects such as readability, alignment with professional workplace contexts, clarity, and
potential redundancy of the items. The feedback collected during the pilot testing phase is then
used to refine and reassess the instrument (Stevens, 2012).

3.7 Ethical consideration

Follow ethical principles in collecting, storing, and analyzing data, with particular attention to
protecting the privacy and confidentiality of sensitive information. Obtain participants' informed
consent for interviews or surveys and ensure their anonymity or confidentiality as
appropriate.Only researcher has direct access to all research data

3.8 Chapter Summary

The information contained in this chapter is crucial for future research. It can be used as
guidelines so that similar research can be conducted using the most similar way or improved
techniques. This chapter also outlined the research methodology that is applied in the study. At
the beginning of the chapter, research design for this study was discussed. Then, the chapter

21
explains the data collection procedures and the method of data analysis is also introduced in the
discussion.

22
 Chapter : Four
Result and Discussion

23
4.1 Introduction

This chapter presents the analysis and explanation of empirical results of the current study. The
analyses are conducted using statistical techniques. The introduction is followed by an overview
of the data analysis process and the preliminary analysis of the data which includes the
descriptive statistics. As stated earlier this study involves assessing the impact of AI in the
financial industry of Bangladesh. Finally, the chapter will include discussion of the results.

4.2 Data collection techniques

A structured questionnaire has been designed to collect data, allowing respondents to express
their views using a Likert scale with varying labels for different questions. The scale ranges from
"Not at all relevant" to "Very relevant," with corresponding values from 1 to 5. The questionnaire
is modeled after two survey reports: the New Zealand FinTech survey (PwC, 2017) and the
European FinTech survey report by the CFA Institute (2016). Additionally, ranking methods
have been included in some questions to gather preferences and feedback. The questionnaire has
been tailored to suit the Bangladesh market, drawing insights from the mentioned sources.

4.3 Descriptive Analysis

Bangladesh, one of the world's fastest-growing economies, is transitioning towards

upper-middle-income status and diversifying beyond traditional industries (Ahmed, 2019).
However, the country's financial sector remains underdeveloped, with over 35 million people
relying on informal channels for financial needs, often without access to bank accounts. Despite
its large population, significant gaps in financial technology persist.Chatbots can replace
customer care to reduce costs and enhance service speed. Bangladesh has significant potential for
FinTech adoption, supported by a large youth population and high mobile subscription rates.
These factors position FinTech to drive macroeconomic growth, but effective regulatory
frameworks will be essential for its success (Chakraborti, 2020).
Banks in Bangladesh are embracing new technologies by hiring younger, tech-savvy employees
and offering training programs to adapt to FinTech advancements (Ahmed & Rahman, 2020).
FinTech startups have significant potential, particularly in rural areas, where financial inclusion
is hindered by a lack of knowledge, income disparity, and inadequate banking infrastructure.
Traditional banks find it viable to establish branches in these regions. To address these
challenges, government policies and increased FinTech awareness are crucial for promoting
financial inclusion in rural areas (Kuddus, Saha & Rahman, 2020).

24
Figure1: Prime areas of financial sector are likely to be the most disrupted by Fintech in Future

The survey results indicate that consumer banking will face the highest disruption from FinTech
at 53%, followed by fund transfer and commercial banking, each at 20%. Wealth management is
expected to experience the least disruption at 7%.

Figure 2 : Ranking the activities as per consumers already conduct with Fintech companies

The survey reveals that personal financing is the top-ranked use of FinTech, as most customers
prioritize this purpose. In contrast, insurance and wealth management rank lowest due to their
limited development in online services, relying heavily on representatives and direct,
face-to-face marketing.

25
 Figure 3 : Rank the opportunities related to scale rise of fintech within financial industry

The survey highlights customer retention as the top opportunity linked to FinTech, prioritized by
70.1% of respondents. This is followed by differentiation (8.1%), expansion (5.4%), and
reducing IT infrastructure costs (4.1%). These findings emphasize the critical importance of
focusing on customer retention in future company strategies.

The integration of AI in banking often faces resistance, primarily from employees concerned
about job displacement or a lack of understanding of AI's benefits. Roles such as tellers,
customer service executives, financing officers, regulatory officials, and finance executives are
particularly susceptible to automation through AI. To overcome this resistance, banks must adopt
effective change management strategies and clearly communicate the advantages of AI,
emphasizing how it can enhance rather than replace current responsibilities.

Figure 4 : How AI is reshaping jobs in banking

26
The graph highlights the significant job losses across various sectors due to AI integration. In the
banking and lending sector alone, projections indicate that 1.2 million workers will be replaced
by AI by 2030, underscoring the transformative impact of automation on employment.
(Crosman, P., 2018).

4.4 The findings from the likert scale

According to the survey, over 70% of respondents consider factors such as globalization, service
availability, investment costs, user knowledge, advanced technology, and innovation speed to be
relevant. Over 30% view brand value, profitability, transaction speed, accessibility, and network
as highly relevant. Meanwhile, more than 10% believe business location is not relevant at all.

Figure 4 : Results on the basis of likert scale

27
4.5 Theoretical implications

Research on AI in FinTech has significant theoretical implications across various domains:

1. Technological Adoption Theories: Studies can advance models like the Technology
Acceptance Model (TAM) and Diffusion of Innovations by providing empirical evidence on
factors driving AI adoption in financial institutions and among consumers.

2. Theory of Disruptive Innovation: Research can explore how AI-driven innovations disrupt
traditional financial services, contributing to Clayton Christensen's theory of disruptive
innovation.

3. Resource-Based View (RBV): AI technologies can be examined as valuable and

hard-to-imitate resources, highlighting their role in achieving competitive advantages for
financial institutions.

4. Agency Theory: Studies may investigate how AI-based decision-making affects

principal-agent relationships and the role of governance mechanisms in aligning stakeholder
interests and ensuring accountability.

5. Information Asymmetry and Market Efficiency: Research can analyze how AI reduces
information asymmetry, improves transparency, and enhances market efficiency.

6. Complexity Theory: Financial systems, viewed as complex adaptive systems, can be studied
for how AI enables self-organization, adaptation, and emergent behaviors within financial
ecosystems.

7. Institutional Theory: Studies may explore how regulatory frameworks evolve in response to
AI-driven innovations, shaping industry norms and institutional practices.

8. Social Exchange Theory: Research can examine how AI fosters trust, facilitates value
exchanges, and shapes social relationships within financial services.

9. Ethical and Societal Implications: Studies may address ethical concerns, including fairness,
bias, transparency, and accountability, as well as how societal norms influence AI adoption and
regulation in FinTech.

These insights collectively enrich the understanding of AI's transformative impact on financial
services.

28
4.6 Managerial implications

To remain competitive, financial institutions should strategically plan and invest in AI

technologies. Key areas for focus include:
1. Strategic Planning and Investment: Integrate AI into long-term strategies, assessing benefits
like efficiency, enhanced customer experience, and new revenue streams, and allocate resources
accordingly.

2. Technology Integration: Invest in infrastructure upgrades, integrate AI algorithms, and

ensure data quality and security for seamless operations.

3. Talent and Skill Development: Recruit AI experts and provide training for employees to
build internal AI capabilities.

4. Regulatory Compliance: Ensure AI solutions meet regulatory standards, address ethical

concerns, and manage risks like algorithmic biases and data privacy.

5. Customer Engagement: Use AI-driven tools, such as chatbots, recommendation engines, and
predictive analytics, to enhance customer experiences.

6. Operational Efficiency: Streamline operations and reduce costs by automating tasks,

optimizing processes, and enabling predictive maintenance.

7. Risk Management: Identify and mitigate AI risks, including biases, cybersecurity

vulnerabilities, and model interpretability, through robust frameworks and continuous
monitoring.

8. Partnerships: Collaborate with FinTech firms and tech vendors to accelerate AI innovation
and share resources.

9. Change Management: Foster a culture of innovation, agility, and continuous improvement by

engaging employees and aligning them with the organization’s AI vision.

These strategies collectively support successful AI implementation and adaptation.

29
4.7 Chapter Summary

This chapter analyses the theoretical and managerial implications of AI adoption. The result of
moderation also shows the application of AI in the banking sector. Moreover, the discussion
about the objective is also included in the above chapter.

30
 Chapter : Five
Conclusion, Recommendations and Future Research

31
5.1 Introduction

With the respect of current body knowledge, the following section presents the discussion of
empirical research findings. Finally, this chapter discusses the limitations of the current study as
well as suggestions for future study.

The aim of the thesis is to find out the direct effect of AI in financial sectors like banks of
Bangladesh. In addition, the study sheds more light on the benefits and challenges which a bank
faces to adopt AI in their applications

5.2 Research Outcome and Findings

1.Transformation of financial services with AI in Fintech

The research shows that artificial intelligence (AI) is significantly changing the financial services
landscape within the FinTech industry. AI technologies, including machine learning, natural
language processing, and predictive analytics, are being widely adopted in financial processes,
resulting in improved efficiency, automation, and decision-making.

2.Application and benefits of AI in financial services

The study emphasizes the various applications and advantages of AI in financial services,
spanning banking, investment, insurance, and risk management. In banking, AI-driven chatbots
and virtual assistants enhance customer service and engagement, while in investment,
algorithmic trading systems improve portfolio management and investment strategies.

3.Future trend and development in AI - driven FinTech

The study examines future trends in AI-driven FinTech, highlighting advancements in deep
learning, reinforcement learning, and explainable AI. It also anticipates the rise of AI-powered
robo-advisors, automated underwriting systems, and fraud detection technologies, which are
expected to disrupt traditional financial services and business models.

4. Impact of AI of efficiency, Accuracy and Customer Experience

AI technologies are significantly improving efficiency, accuracy, and customer experience in the
financial sector. Machine learning algorithms help financial institutions automate tasks, analyze
large datasets for insights, and tailor services to individual customers, leading to better
operational efficiency and enhanced customer satisfaction.

5. Development of Human Capital

One of the key findings is that human capital is a significant barrier in the development of
FinTech. This is supported by a recent study on intellectual capital in FinTech services, which
found that human capital development was the most important factor in the growth of FinTech in

32
Bangladesh (Ashraf, 2019). This type of research has not been conducted in the context of
Bangladesh before, and this study can help fill a crucial gap in understanding FinTech
development in the country's financial sector by analyzing experts working in the industry.

5.3 Recommendations

Financial institutions should focus on investing in AI talent and capabilities, hiring experts like
data scientists and machine learning engineers, and providing ongoing training to foster
innovation and technological proficiency. They should also establish strong governance
frameworks to ensure the ethical use of AI, including guidelines for data privacy, security, and
fairness, and mechanisms for addressing potential biases.

Collaboration is key to developing ethical AI standards, with stakeholders such as regulators,

industry groups, and academia working together to define best practices for AI governance.
Regulatory oversight should be enhanced to mitigate risks and protect consumers while
balancing innovation with market stability.

To encourage responsible innovation, institutions and startups should conduct risk assessments,
validate AI algorithms, and implement safeguards to prevent misuse. This includes adopting
explainable AI principles and regularly auditing systems to ensure compliance with ethical and
regulatory standards.

Finally, fostering collaboration and knowledge sharing among financial institutions, regulators,
and technology providers through forums and research initiatives will help accelerate AI
adoption and innovation in the FinTech sector.

5.4 Limitations of the study

Data availability and quality

The study's conclusions depend on the quality and availability of the data used for analysis.
Incomplete or limited data, along with inconsistencies in data accuracy across sources, may
impact the study's validity and reliability.

Scope and generalizability

The study focuses on AI's impact within the FinTech industry, limiting its scope. As a result, the
findings may not be fully applicable to other sectors where AI is also driving change in
operations and decision-making.

33
Methodological consideration
The study may face methodological limitations, such as relying on descriptive statistics or
secondary data without advanced analytics or primary research, which could affect the
depth and rigor of its findings.

Bias and Assumption

The study's findings may be affected by biases or assumptions in its research design, data
collection, or analysis. For instance, focusing on specific FinTech companies could introduce
selection bias and limit the generalizability of the results.

Regulatory and legal constraints

Regulatory and legal constraints, such as data privacy laws, intellectual property rights, and
confidentiality agreements, may limit access to sensitive financial data, affecting the study's
scope and ability to derive meaningful conclusions.

Dynamic nature of AI and Fintech

The study's findings may quickly become outdated due to rapid advancements in AI, FinTech
innovations, market dynamics, and regulatory changes, necessitating ongoing updates to
maintain relevance.

5.5 Scope for future research

Longitudinal Studies
Conduct long-term analyses to track the adoption and evolution of AI technologies in financial
services. These studies can offer insights into the sustainability of AI-driven innovations,
adoption patterns, and their long-term impact on financial markets and institutions.

Cross-Cultural Studies
Examine cross-cultural differences in the acceptance and adoption of AI-based FinTech
solutions. Comparative research across regions and cultures can shed light on the factors driving
AI adoption, regulatory variations, and ethical concerns.

Robustness and Stability of AI Models

Evaluate the robustness and reliability of AI models in financial applications, particularly during
volatile market conditions. Research stress testing, model validation, and risk management to
enhance the stability and dependability of AI-driven systems.

34
Explainability and Transparency of AI Algorithms
Investigate methods to improve the explainability and transparency of AI algorithms in financial
services. Research on interpretable AI, transparency frameworks, and bias mitigation can address
concerns around compliance, fairness, and stakeholder trust.

Human-AI Collaboration and Decision Support

Study the design of human-AI collaboration frameworks and decision support systems in
financial institutions. Research on augmented intelligence, human-computer interaction, and
ergonomic approaches can foster effective collaboration between humans and AI in
decision-making.

Regulatory and Policy Implications

Explore the regulatory and policy challenges of AI adoption in financial services, including data
privacy, consumer protection, and accountability. Research on regulatory sandboxes, ethical
guidelines, and governance frameworks can support policymakers in addressing AI-related
challenges.

Impact on Employment and Workforce Dynamics

Analyze the effects of AI on employment trends, workforce dynamics, and skill requirements in
the financial sector. Research on reskilling, labor market transitions, and socio-economic impacts
of automation can guide workforce development and policy strategies.

AI Ecosystem Dynamics and Industry Structure

Investigate the evolving AI ecosystem in financial services, focusing on the roles of startups,
incumbents, tech firms, and regulators. Research on ecosystem changes, industry consolidation,
and competition can reveal innovation trends and strategic responses to AI-driven disruption.

Consumer Trust and Perception

Examine consumer attitudes, trust, and perceptions of AI-based FinTech solutions. Research on
factors influencing acceptance, privacy concerns, and trust-building mechanisms can support
user-centric design and effective marketing strategies.

5.6 Conclusion

In a rapidly growing economy like Bangladesh, driven by the government's Vision 2021 plan to
create a "Digital Bangladesh," the financial sector is undergoing swift digitalization. The

35
research highlights the growing role of Fintech in expanding both the user base and the range of
financial services offered. Fintech integration is increasingly being embraced by local
companies, with many already tapping into these opportunities, demonstrating that fintech is
disrupting the traditional financial sector. However, the transition will not be immediate or
without challenges. Many consumers are accustomed to traditional financial methods, and
privacy concerns will persist unless adequate security measures are communicated effectively.
Additionally, the country's low internet and banking penetration rates pose further obstacles to
widespread Fintech adoption.

5.7 Chapter summary

The thesis aimed to the test AI impact in financial services and identify whether individuals with
different type of empowerment would require specific AI technology in order to increase their
level of productivity.

36
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