HAMAD BIN KHALIFA UNIVERSITY

COLLEGE OF ISLAMIC STUDIES

DEGREE OF GOVERNMENT AI READINESS OF MENA

COUNTRIES AND ITS IMPACT ON BANKING PERFORMANCE

BY

ESRAA MOHAMED MAHMOUD MOHAMED

A Thesis Submitted to the Faculty of the

College of Islamic Studies

in Partial Fulfillment

of the Requirements

for the Degree of

Master of Science

March 2025

© Esraa Mohamed Mahmoud Mohamed. All Rights Reserved

 ABSTRACT

The Middle East and North Africa (MENA) open banking sector is undergoing a
profound transformation driven by technological advancements, particularly artificial
intelligence (AI). It is projected to grow at a compound annual growth rate (CAGR) of
25% through 2027, reaching US$1.17 billion in the MENA region. By 2030, AI is
expected to contribute US$38 billion to the financial, professional, and administrative
services sector and account for 13.6% of the GCC's GDP. AI adoption in MENA
banking is characterized by applications in customer service enhancement, fraud
detection, anti-money laundering, risk assessment, operational automation, and
personalized financial services. Despite an innovation-focused regulatory environment
and thriving FinTech ecosystems, banks face challenges such as data quality issues,
fragmented legacy systems, integration difficulties, and a shortage of skilled
professionals. This study examines the relationship between AI maturity and banking
sector performance in MENA countries. Employing a quantitative research design with
panel data analysis, the study uses a random effects model to control for country-
specific effects. The sample data includes 167 banks, comprising 51 Islamic banks and
116 conventional banks, originating from 14 dual-banking countries, over the period
from 2020 to 2023. The results reveal a negative relationship between AI readiness and
bank profitability (return on assets, ROA), likely attributed to increased competition
from non-banking financial institutions (NBFIs) and the high cost of implementing AI
technologies. Also, the findings suggest that AI readiness moderates the economic
growth-profitability relationship by reducing this negative impact. Notably, the study
finds no significant difference in the behavior of Islamic and conventional banks
regarding AI adoption. The study contributes to understanding how varying levels of
AI maturity across MENA countries correlate with banking performance metrics. It
provides valuable insights for financial institutions seeking to optimize AI strategies in
the region's unique technological and regulatory landscape.

Keywords: artificial intelligence, banking performance, MENA, digital

transformation, financial technology, FinTech

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 TABLE OF CONTENTS

ABSTRACT...................................................................................................................ii
LIST OF FIGURES ....................................................................................................... v
LIST OF TABLES ........................................................................................................ vi
ACKNOWLEDGMENTS ...........................................................................................vii
DECLARATION ....................................................................................................... viii
DEDICATION .............................................................................................................. ix
CHAPTER 1: INTRODUCTION .................................................................................. 1
1.1. Background of the Study .................................................................................... 1
1.2. Problem Statement .............................................................................................. 2
1.3. Research Questions and Objectives .................................................................... 3
1.4. Scope of the Study .............................................................................................. 3
1.5. Research Methods ............................................................................................... 3
1.6. Significance of the Study .................................................................................... 4
1.7. Organization of the Study ................................................................................... 5
CHAPTER 2: LITERATURE REVIEW ....................................................................... 6
2.1. Artificial Intelligence (AI) Readiness Index ....................................................... 6
2.2. The government AI Readiness in the MENA Region ........................................ 9
2.3. AI Applications on the Financial Sector and its Impact on Profitability .......... 14
2.4. The Role of AI in Islamic Finance.................................................................... 18
2.4.1. AI Adoption in Islamic Finance ................................................................. 19
2.4.2. AI Integration Challenges in Islamic Finance ........................................... 22
2.5. AI Implementation Challenges ......................................................................... 23
2.6. The Impact of AI on Banks............................................................................... 25
2.7. Gaps in the Literature and Research Hypotheses ............................................. 26
CHAPTER 3: RESEARCH METHODOLOGY ......................................................... 27
3.1. Data ................................................................................................................... 27
3.2. Model Specification .......................................................................................... 28
3.3. Empirical Methods ............................................................................................ 29
CHAPTER 4: DISCUSSION OF FINDINGS ............................................................. 31
4.1. Preliminary Analysis......................................................................................... 31
4.2. Empirical Analysis ............................................................................................ 37

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 4.3. Discussion of Results ........................................................................................ 46
CHAPTER 5: CONCLUDING REMARKS ............................................................... 51
REFERENCES ............................................................................................................ 54
APPENDIX: LIST OF DUAL BANKING COUNTRIES .......................................... 61

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 LIST OF FIGURES

Figure 1: Marginal Effects ........................................................................................... 46

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 LIST OF TABLES

Table 1: Description of variables ................................................................................. 27

Table 2: Descriptive statistics ...................................................................................... 31
Table 3: Correlation matrix .......................................................................................... 33
Table 4: Regression outputs – Aggregate variable (SC) .............................................. 37
Table 5: Regression outputs - With components (Part 1) ............................................ 40
Table 6: Regression outputs - With components (Part 2) ............................................ 41
Table 7: Regression outputs - Interactions with ECG ................................................. 44

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 ACKNOWLEDGMENTS

First and foremost, I would like to express my deepest gratitude to my supervisor, Dr.
Ruslan Nagayev (Adam), for his invaluable guidance, insightful feedback, and
unwavering support throughout this research journey. His expertise and encouragement
have been instrumental in shaping this thesis and helping me overcome numerous
challenges.

On a personal note, I owe an immeasurable debt of gratitude to my family, especially

my brother Ahmed Mohamed, —my husband, Osama Mostafa, and my siblings—for
their unconditional love, endless encouragement, and unwavering belief in me. Their
support has been my anchor during the most challenging moments of this process.
This thesis would not have been possible without the collective efforts and
contributions of all those mentioned above. I am eternally grateful for their support and
encouragement.

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 DECLARATION

This is to certify that the work described in this thesis is entirely my own unless
otherwise referenced or acknowledged. This work has not previously been submitted
for qualifications at any other academic institution.

Signed: Esraa Mohamed Mahmoud Mohamed

Date: March 24, 2025

viii
 DEDICATION

In the name of Allah, the Most Gracious, the Most Merciful.

All praise is due to Allah, the Lord of all worlds, who has bestowed upon me the
strength, patience, and guidance to complete this work. I dedicate this thesis to my
beloved parents, whose unwavering love and sacrifices have been a source of light and
inspiration throughout my journey. May Allah reward them abundantly for their care
and prayers.

To my teachers and mentors, who have imparted knowledge and wisdom with sincerity,
I express my deepest gratitude. May Allah bless them for their efforts in guiding me
toward understanding and excellence.

I also dedicate this work to all seekers of knowledge striving to benefit humanity and
uphold justice. May this humble effort serve as a contribution to the betterment of
society and a means of attaining Allah’s pleasure.

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CHAPTER 1: INTRODUCTION

1.1. Background of the Study

Artificial Intelligence (AI) is transforming the world's financial system, especially in
the banking sector. The transformation revolutionizes traditional banking procedures,
customer service frameworks, and risk management practices (Jain, 2024). These
applications augment banks' internal operational effectiveness and assist them in
delivering more personalized and advanced financial services and products (Cao,
2020). The accelerated uptake of AI in the finance sector is driven by the requirement
for greater efficiency, improved decision-making, and the capacity to manage vast
amounts of data.

With its data-rich world and complex operations, the banking sector is especially well-
suited to leveraging AI technologies to enhance performance and gain a competitive
edge. Industry analysis suggests that most banks appreciate the massive potential AI
can deliver, with value potential in banking and finance estimated at $1 trillion
(McKinsey & Company, 2020). Adopting AI-fueled technologies like machine
learning, natural language processing, and computer vision enables banks to process
data, make informed decisions, and automate processes at unprecedented velocities.
However, the rapid absorption of AI also brings about associated challenges, including
ethical considerations, rigorous compliance mandates, and the necessity for robust
governance frameworks (Lazo & Ebardo, 2023).

The MENA open banking sector is undergoing a profound transformation driven by

technological advancements, with AI expected to grow at a compound annual growth
rate (CAGR) of 25% through 2027, reaching US$1.17 billion, as estimated by the Arab
Monetary Fund (2023), a regional development institution. PwC reported that by 2030,
AI will contribute US$38 billion to the financial, professional, and administrative
services sector in the MENA region and account for 13.6% of regional GDP (PwC,
2018).

Recently, the Government AI Readiness Index was introduced - a quantitative

benchmark that assesses a government's readiness to utilize AI (Oxford Insights, 2019).

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Developed with the assistance of the International Development Research Centre, the
index encompasses all United Nations (UN) countries beyond the earlier focus on
Organization for Economic Co-operation and Development (OECD) members. To
begin designing the Government AI Readiness Index methodology in 2019, they
wanted to answer a question: How ready is a government to use AI in public services
for its citizens? It served as a guiding principle that structured both the methodology
and selection of input measures for the Index. In 2020, they, therefore, produced three
new hypotheses, each related to one fundamental pillar of a government AI Readiness
Index (Oxford Insights, 2020): 1) Government: A government's strategic vision for
developing and governing AI as a cornerstone of its readiness; 2) Technology Sector:
The technology sector as being pivotal in government AI readiness. The adequate
supply of AI tools from the country's technology sector is essential for any government
pursuing its AI ambitions, and 3) Data and Infrastructure: AI tools require immense
amounts of high-quality data (data availability) that, to avoid bias and error, should also
represent the citizens in each country (data representativeness).

While a significant body of literature does exist on the impact of AI on banking in

developed economies (Abdelraouf et al., 2025), even less literature is directly present
focusing on the unique financial landscape of the MENA region. Specifically, little
empirical research quantifies the relationship between a country's AI readiness, as
presented by the Government AI Readiness Index, and its banking sector performance
in the MENA region. The bulk of the literature is aimed at developed economies, and
evidence may not be readily applicable to the MENA region due to differences in
regulatory regimes, technology infrastructure, and economic configurations.

1.2. Problem Statement

There is a growing influence of AI on the MENA banking sector, potentially having
implications on banking performance. Despite the expected growth in AI adoption
across the region, there are no empirical studies on how much correlation exists
between the AI-readiness of the country and profitability of banks in the MENA region.
Existing literature primarily deals with developed economies, and there is no literature
to explore how AI readiness influences the performance of banks in the MENA region
as it has a unique regulatory, technological, and economic environment. The present
study intends to determine to what extent government AI readiness, measured by the

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Government AI Readiness Index, influences banks' profitability in MENA region. The
research aims to provide actionable suggestions to policymakers, bank managers, and
investors by analyzing the connection and potential causal impacts between AI
readiness and other bank performance metrics.

1.3. Research Questions and Objectives

The study aims to bridge the gap by examining whether the country-level AI readiness
directly or indirectly contributes to bank profitability in MENA countries. The research
questions guiding this investigation are:
1) What is the relationship between government AI readiness and banking
profitability in the MENA region? And, whether Islamic banks perform
differently from their conventional counterparts?
2) Does AI readiness impact the economic growth-bank profitability links in the
MENA region?

1.4. Scope of the Study

This research focuses on examining the relationship between government AI readiness
and banking profitability in the MENA region. Hence, the scope is defined as follows:
• The study is limited to MENA countries due to its unique economic, regulatory,
and technological landscape, which differs from developed economies.
• The analysis covers the period from 2020 to 2023, a period that captures the
rapid adoption of AI technologies in the banking sector and the evolving
economic conditions in the region.

1.5. Research Methods

Addressing the research questions, the study employs random effects panel data
estimator on a sample of 167 banks, comprising 51 Islamic banks and 116 conventional
banks from 14 countries, over 2020-2023 period. Return on Assets (ROA) is used as a
dependent variable, expressed as net income divided by total assets. It is a widely used
measure of bank performance showing how effectively a bank can generate profit out
of its assets, hence making it a suitable measure of profitability. Meanwhile,
Government AI Readiness Index is a focus independent variable. Provided by Oxford
Insights, the index is utilized to determine the preparedness of governments towards

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adapting and implementing AI technologies. Governance, infrastructure, and data
availability are components of this index which are also employed to give a holistic
perspective of AI readiness. Besides, the empirical model controls for macroeconomic
heterogeneity using Economic Growth. As a percentage change in yearly GDP, it
potentially influences the banking performance, such as loan demand, interest rates,
and sector stability. Also, the following bank-specific control variables are included: a)
Capitalization - measured as a ratio of equity to total assets, b) Asset Quality - the ratio
of non-performing loans to gross loans, c) Management Efficiency – income to cost
ratio, and d) Bank Size representing the banks’ ownership of assets.

Limitations: Since, the sample is limited to the MENA region, the findings may not be
generalizable to other regions. Besides, it focuses on limited period (2020–2023), hence
may not capture the longer-term effects of AI adoption since the AI readiness index is
introduced very recently.

1.6. Significance of the Study

This study is important in the context of the widespread adoption of AI in the banking
sector by multiple players in the MENA region, particularly:
• Policymakers: The study provides regulators with empirical evidence for the
effectiveness of AI readiness schemes in impacting the profitability of banking
sector. This may guide the establishment of targeted policies to drive the
adoption of AI, improve regulatory environments, and mobilize resources
aimed at spurring innovation within the financial sector. By quantifying the
relationship between AI preparedness and banking performance, the study helps
identify how AI can drive economic growth and enhance the competitiveness
of the financial sector in the MENA region.
• Industry players: Bank decision-makers and CEOs can leverage the research
conclusions for implementation of AI adoption strategy and positioning for
competition to face the increased rivalry from non-bank financial institutions
(NBFIs).
• Academia: The study fills a critical gap in the existing literature by investigating
the MENA region, which has unique regulatory, technological, and economic
characteristics. This provides a localized understanding not often available in

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 global AI research. The research also serves as a base for future research on the
long-term effect of AI on banking and its global economic implications, which
would motivate increased innovation and investment in AI technology.

By addressing these areas, the study not only contributes to scholarly discourse but also
provides real-world suggestions that can drive the MENA banking sector's shift through
AI implementation.

1.7. Organization of the Study

The remainder of this thesis is structured as follows:

• Chapter 2 comprehensively reviews the existing literature on AI readiness indices,

the factors driving AI adoption in the banking sector, and the challenges and
opportunities facing the MENA region.
• Chapter 3 outlines the research methodology employed in this study, including the
panel data analysis approach, the definition of key variables, the data sources used,
and the econometric techniques applied to examine the relationship between AI
readiness and banking profitability.
• Chapter 4 presents the empirical findings based on regression results and assesses
the statistical significance and economic impact of AI readiness on banking
performance in the MENA region.
• Chapter 5 discusses the implications of these findings, draws conclusions based on
the empirical evidence, provides recommendations for future research, and suggests
policy implications for governments and financial institutions in the MENA region.

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CHAPTER 2: LITERATURE REVIEW

2.1. Artificial Intelligence (AI) Readiness Index

The Government AI Readiness Index is a quantitative benchmark that assesses a
government's readiness to utilize AI (Oxford Insights, 2019). By gathering extensive
data through desk research on AI strategies and using statistics from Crunchbase on AI
startups as well as UN indices, it condenses this information into a singular figure that
facilitates global comparisons and allows for tracking a country's advancements in this
domain over time. The comprehensive nature of the data collection instills confidence
in the Index's findings. In early 2017, Oxford Insights created the inaugural
Government AI Readiness Index in response to a fundamental inquiry: How equipped
are national governments to harness the advantages of AI in both their internal functions
and the provision of public services? The findings aimed to illustrate governments'
existing ability to leverage AI's innovative capabilities.

The 2019 Government AI Readiness Index, developed with the assistance of the
International Development Research Centre, improved its methodology and broadened
its scope to encompass all UN countries beyond the earlier focus on OECD members
(Oxford Insights, 2019). It evaluated the readiness of 194 countries and territories in
terms of their preparedness to implement AI in public service delivery. The Index's role
in fostering global AI innovation is inspiring. The assessment was based on 11 input
metrics categorized into four overarching clusters: Governance, Infrastructure and
Data, Skills and Education, and Government and Public Services. Data sources ranged
from desk analyses of AI strategies to various databases, including the count of
registered AI startups on Crunchbase and indices such as the UN eGovernment
Development Index.

To begin designing the 2019 Government AI Readiness Index methodology, they wrote
a question: How ready is a government to use AI in public services for its citizens? It
served as a guiding principle that structured both the methodology and selection of
input measures for the Index (Oxford Insights, 2019).

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They created several working hypotheses regarding the factors contributing to a
government's 'readiness' to implement AI in public service delivery. Their goal with the
2019 Index was to be more representative worldwide than the previous one, which was
comprised solely of OECD members; they thus assess all UN countries, plus Taiwan.
This broadened perspective significantly influenced their data selection, as they sought
datasets applicable to as many of these nations as possible—previous Index datasets
were primarily focused on the OECD (Oxford Insights, 2019). They retained a structure
like the prior year, featuring high-level 'clusters' encompassing multiple indicators or
proxies to assess government AI readiness. These clusters, namely Governance,
Infrastructure and Data, Skills and Education, and Government and Public Services,
provided a comprehensive framework for evaluating the preparedness of governments
to implement AI in public service delivery.

The Government AI Readiness Index is constructed upon four theoretical propositions

regarding governmental preparedness for artificial intelligence implementation. This
index conceptualizes readiness as a government's capacity to effectively develop,
regulate, and deploy AI technologies. The 2020 methodology established a distinctive
analytical framework comprising three dimensions, each corresponding to a
fundamental component of governmental AI readiness (Oxford Insights, 2020):

• Government: A government's strategic vision for AI development and

governance constitutes a fundamental element of its readiness posture. This
strategic orientation requires complementary regulatory frameworks and ethical
risk management. The internal digital capabilities of governmental institutions,
including technical competencies and organizational practices that facilitate
adaptation to emerging technologies, are equally significant determinants of
readiness.
• Technology: The technology industry plays an instrumental role in
governmental AI readiness assessment. The provision of AI tools from domestic
technology sectors represents a critical input for governmental AI initiatives.
However, this supply-side contribution necessitates sufficient industry
maturation. The sector must demonstrate substantial innovation capacity,
supported by an entrepreneurial ecosystem and adequate research and

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 development expenditures. Human capital development for personnel operating
within this sector also represents a crucial variable.
• Data and Infrastructure: The qualitative and quantitative aspects of data
resources are indispensable for AI readiness evaluation. AI systems require
substantial volumes of high-quality data (availability) that accurately represent
the demographic composition of the jurisdiction (representativeness). The
economic potential of these data assets cannot be realized without
corresponding investments in technological infrastructure to operationalize AI
applications and deliver services to constituents.

The 2019 Index results revealed significant concentration of AI readiness among

nations with robust governance structures and innovative private sectors. Singapore
achieved the highest ranking, with Western European governments, Canada, Australia,
New Zealand, and four additional Asian economies constituting the remainder of the
top twenty positions. Latin American and African nations were notably absent from
this highest performance tier.

Meanwhile, North America is the best-performing region on average, followed by the

worst-performing regions like Africa and the Asia-Pacific. The Index highlights the
disparities in how prepared various governments are for AI, showing that higher-
income countries are generally better placed in the rankings than those in the middle-
and lower-income brackets. Given the anticipated extensive deployment of AI across
numerous sectors, including public services, this emphasizes the current inequality in
access to AI (Oxford Insights, 2019).

More than anything has ever been done before, the COVID-19 pandemic has amplified
the strategic importance of AI to governments around the world. Whether through
pharmaceutical firms that leverage AI in devising new treatments and drugs or through
using the technique to support contact tracing by using mobile phones and geolocation
data, modern technologies support the efforts made by governments to control this
pandemic. They may be involved in economic recovery. COVID-19 applications
outlined above are just a few illustrations of how AI can assist governments in their
operations. In transport, education, and healthcare, AI can improve the provision of

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public services, which bodes well for the future. However, what can governments do to
position themselves to gain from this AI revolution? (Oxford Insights, 2020).
In 2023, AI was in the headlines more than ever: generative AI breakthroughs,
significant developments in the regulation of the AI area, such as the European Union's
AI Act, and a steep rise in the number of summits on AI organized all over the world
through this technology into the spotlight (Oxford Insights, 2023). The capacity of AI
to bring significant change is a global phenomenon and not a local one, and it has been
embraced by governments worldwide. Governments are leading the way in regulating
and deploying AI technology because they want to promote innovation in the industry
and incorporate this technology into public services. For instance, the Republic of
Korea uses AI to enhance government operations through the Digital Platform
Government. At the same time, the UK's National Health Service supports the
innovation of new AI screening technologies for health and social care.

2.2. The government AI Readiness in the MENA Region

The MENA region stands out for their diverse range of government AI readiness scores,
reflecting a broad development spectrum. The scores range from the lowest of 17.93
for Yemen to the highest of 71.60 for the UAE (Oxford Insights, 2021).

The policy implications of the region's widely diverse level of readiness in AI are
significant, but they also represent a tremendous opportunity for growth. Two countries
in the region, Egypt and the UAE, have a national AI strategy, while in others, AI is
just beginning to register on the decision-makers’ screen (Oxford Insights, 2020).

There is still much work to be done to prepare for AI in the MENA region. With
increasingly interconnected societies and economies, the need to gather societal data is
crucial so that all the AI boats of the nations can rise. Here, 'AI boats' refers to AI's
collective progress and development in the region. There has been a raft of initiatives
from various quarters that seek to harness a coordinated regional response to the digital
development of the MENA region. In this respect, building common, shared, and open
data can form the foundational building block for AI development in that region.
Prerequisites include laying down the regulatory foundations and privacy safety nets
for compatible data governance frameworks (Oxford Insights, 2020).

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A primary constraint in evaluating AI activities and governmental preparedness in
North Africa is the deficiency of systematic investigation. Consequently, empirical data
remains scarce, with much of the available information on regional AI developments
being anecdotal in nature. Nevertheless, existing indicators point towards heightened
interest and increased activity concerning AI within the region. It is anticipated that this
growing engagement will foster the collection of more robust and superior data,
potentially leading to enhanced rankings for African governments in subsequent
iterations of the Government AI Readiness Index (Oxford Insights, 2019).

The diversifying Middle Eastern oil Gulf countries have conveyed powerful messages
concerning the role of AI in the future. The UAE has moved to have the world's first
AI minister as a clear indication of interest in pursuing AI technology in the country.
Saudi Arabia has meanwhile granted citizenship to a robot as a witness of the nation's
attention towards that end. The UAE, Saudi Arabia, and Qatar have all made clear and
firm commitments to enhance their AI capabilities. They have invested heavily in new
technology, with governments being the initial buyers. The UAE, for instance, has
several AI-related projects in cities, and autonomous transport, among others, could
speed up the pace of AI adoption. Qatar, KSA, and the UAE have all made it apparent
that they intend to improve their AI capabilities. They have made significant
investments in contemporary technology, with governments serving as early adopters.
For instance, the UAE has several AI-related municipal laws and autonomous mobility
could hasten the adoption of AI, among other things. In the near to medium term, the
economies of the Middle East will have to focus hard on attracting and retaining foreign
talent, which is already in short supply, and companies. While volatility in oil prices
might hurt investment, it has also catalyzed the Gulf economies to diversify away from
the traditional sectors based on oil. As discussed so far, investment in the adoption of
AI, these governments will have to herald their societies toward full utilization of AI
growth and its disruptions, inspiring a transformation in the region's economies (Oxford
Insights, 2019).

In 2020, the MENA region represents a global snapshot of government readiness for
AI. The scores among the 18 countries covered in the MENA region group are also
more spread out than in most other regions. This region has the most significant
disparity of any region globally, with 53 points separating the best- and worst-

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performing nations. The UAE, for instance, is among the top 20 countries in the world
(ranked 16 with a score of 72.40), while Yemen, the region's lowest-scoring nation, is
among the lowest-scoring nations globally (ranked 172 with a score of 19.07) (Oxford
Insights, 2020).

In 2021, the UAE and Qatar distinguished themselves among the top 30 nations in the
Government AI Readiness Index. The MENA region demonstrated above-average
performance with a mean score of 49.68 out of 100, exceeding the global average of
47.42. The UAE exhibited consistent strength across all three pillars of the index,
securing the 18th position in the Government dimension, 22nd in the Technology
Sector, and 20th in the Data and Infrastructure category.

The Gulf region witnessed particularly significant AI policy developments. Qatar and
Saudi Arabia introduced comprehensive National AI Strategies, each structured around
six foundational pillars: education, data accessibility, employment, commercial
applications, research, and ethical considerations. Qatar's strategy aligns with its
National Technology 2030 agenda, which positions artificial intelligence as a
fundamental catalyst for transitioning from hydrocarbon dependency to a knowledge-
based economic model. Concurrently, Saudi Arabia promulgated its National Data and
Artificial Intelligence Strategy, articulating four strategic objectives: (1) achieving
global leadership status among the top 15 nations in AI development and utilization by
2030; (2) enhancing domestic ICT competencies; (3) establishing a regulatory
environment conducive to commercial innovation in data and AI; and (4) facilitating
the growth of data and AI entrepreneurial ecosystems within the Kingdom by 2030.

The MENA region exhibits the most pronounced variance in AI readiness scores
globally. The performance differential between the highest and lowest scoring MENA
countries is substantial, with mean scores of 51.14 and 38.59 respectively. Within North
Africa, Egypt and Tunisia demonstrate markedly superior performance relative to
neighboring states, ranking among the region's top ten. Egypt's comparative advantage
lies in governmental institutional capacity, while Tunisia excels in data infrastructure
development.

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By 2020, only five countries had published formal national AI strategies, with Qatar,
UAE, and Saudi Arabia emerging as early adopters. All three articulated ambitious
objectives to establish global leadership positions in artificial intelligence. The year
2022 witnessed an acceleration in strategic planning, with four additional countries
announcing national AI frameworks—a rate exceeding that observed in most other
global regions. Oman and Jordan recently introduced their respective strategies,
representing part of an emerging cohort of nations conceptualizing AI not merely as an
isolated technological sector, but as a transformative catalyst for accelerating digital
development and stimulating innovation across priority economic domains.

Research by Golestan Radwan identified a pattern wherein governments seek to embed

AI applications within key economic sectors, including agriculture, manufacturing,
healthcare, and infrastructure development. An illustrative example is Golestan, an
Egyptian agricultural initiative comprising three integrated components.

Concerning government readiness for AI in 2023, the MENA region have the third-
biggest range of scores, meaning there is a significant variation across this region.
Significant differences exist, as evidenced by the average ratings of 38.89 and 51.11
for MENA nations, respectively. Due to its excellent performance in the Government
pillar, Egypt is a new outlier in North Africa and is ranked among the top 10 in the
MENA region. The UAE leads the region with high rankings in all three pillars and an
18th-place ranking globally. Some significant advancements in the MENA region
governance and AI ethics occurred in 2023. Egypt has also achieved significant strides
by embracing the Egyptian Charter for Responsible AI. This comprehensive project
turns knowledge into action to ensure the development, use, management, and
deployment of AI systems in an ethical manner. Based on guidelines from leading
international organizations (OECD, UNESCO, WHO, IEEE, EU), the Charter aims to
raise awareness about ethical considerations in AI among all stakeholders in the AI
ecosystem. Likewise, the Kingdom of Saudi Arabia issued the AI Ethics Principles
(Oxford Insights, 2023).

Recently, countries in the MENA region have taken the lead in further developing their
Data and infrastructure frameworks: Bahrain published its Sixth National
Telecommunications Plan, which stipulated the strategic vision and the overall policy

12
the government pursued regarding the telecommunications industry. Of note is that
lately, there has been a 'data center boom' across the region. Oman recently inked an
agreement with SAP to set up a private cloud data center, and Huawei recently
announced the inauguration of its cloud region in Riyadh. Additionally, Egypt will host
a massive data center with a $250 million investment. These are likely to affect the
current standing of the region in terms of Data and infrastructure readiness, which is 4
points behind the global average of 60.09 (Oxford Insights, 2023).

The MENA region 2024 is in the Middle of the regional rankings, in fifth place, with
an average score of 48.50 in this year's Government AI Readiness Index. It is led by
the UAE, which, with a score of 75.66, just broke into the global top 15. The two top-
scoring nations in the MENA region are Israel, with 74.52, and Saudi Arabia, with
72.36. However, the area also posts the widest spread of scores in the index: 61.03
points, separating the UAE from Yemen, which ranks 14.62 and is in last place globally
(Oxford Insights, 2024).

The MENA region demonstrates performance proximate to global averages at the pillar
level, albeit with notable internal variance. The region exceeds worldwide benchmarks
in two critical dimensions: the Technology Sector pillar (59.34) and the Data &
Infrastructure pillar (59.94). These metrics reflect substantial progress in innovation
capacity and data availability throughout the region. However, the Government pillar
presents a contrasting assessment, with a score of 47.77. This indicates significant
opportunities for enhancement in strategic AI policy formulation, governance
frameworks, and responsible AI utilization protocols. The leadership position of
countries such as the United Arab Emirates and the Kingdom of Saudi Arabia has
contributed to the region's overall positive trajectory. These frontrunners have
established foundations that support an increasingly innovative regional capacity,
characterized by progressive infrastructure development despite persistent disparities
across the MENA region. This diverged performance profile suggests that while
technological and data capabilities have advanced considerably, institutional and
regulatory frameworks require additional development to maximize the region's AI
potential.

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Project Transcendence, announced at the October 8th Future Investment Initiative
Summit in 2023, represents a significant commitment by the KSA to artificial
intelligence development through a $100 billion investment strategy. This
comprehensive initiative aims to strengthen all components of the AI value chain with
the objective of establishing a globally competitive AI ecosystem within the Kingdom.

The strategic framework encompasses multiple dimensions of AI infrastructure

development, including data center construction, talent acquisition initiatives, and the
cultivation of domestic AI modeling capabilities. This ambitious program aligns with
broader regional trends toward formalized AI governance structures. Concurrent
developments across the MENA region demonstrate increasing institutional
commitment to AI development. Mauritania implemented its inaugural national AI
strategy in 2023, marking a significant advancement in regional AI governance
frameworks. Oman revised its Executive Program for AI in September 2023,
prioritizing infrastructure development in coordination with Saudi Arabia's sovereign
wealth fund, the Public Investment Fund. This collaboration culminated in a strategic
partnership with Google Cloud to establish an AI hub in eastern Dammam, facilitating
joint research initiatives in Arabic language models and Saudi-specific applications.

The regional investment landscape for digital transformation continues to expand, with
the Abu Dhabi Fund for Development committing approximately $100 million to
support Jordan's healthcare digitalization initiatives. These developments collectively
indicate accelerating institutional engagement with AI technologies throughout the
MENA region, with substantial capital commitments supporting infrastructure
development and specialized applications in priority sectors.

2.3. AI Applications on the Financial Sector and its Impact on Profitability

New technologies drive banks to innovate in response to changing customer
requirements and remain competitive. These new technologies are AI-based for most
banking functions, ranging from back-end functions to customer services. The AI
history of the past, beginning when it was invented in 1956 and now being a
fundamental part of cognitive computing, has transformed data analysis and decision-
making in banks (Fares, Butt, & Lee, 2022). Infusing AI into banking is an excellent
paradigm shift, transforming old methods and establishing a new constructive

14
symbiotic relationship between technology and customer care (Almutairi & Nobanee,
2020). AI represents an evolutionary step toward computerizing and updating modern-
day business. In brief, computers can learn and apply knowledge independently without
any programmer intervention. Investors are lining up for the future change (Kaya,
2019). AI attracted US$ 24 billion in investment globally in 2018, a twelve-fold
increase since 2013. Startups based in the US were most in the spotlight, followed by
Chinese ones, which already outnumbered European AI startups (Panakaje & Madhura,
2023).

Early research suggests the potential uses and utilization of AI from a business
perspective. Data mining (one of the key building blocks of AI) has been used to predict
bankruptcy and to automate risk models. However, it must be realized that boosting AI
technology to increase organizational performance also carries potential dangers and
limitations. These include the potential for AI to exacerbate preexisting biases in data,
the requirement for ongoing updates and modification of AI systems to keep them
morally and effectively sound, and the potential for job displacement due to automation.
Despite these issues, AI presents more excellent business efficiency prospects for
financial institutions than conventional means of strategizing and risk model creation
(Fares, Butt, & Lee, 2022).

Following the advent of Web 2.0, research on the use of AI in banking started to surface.
This may have been precipitated by the proposed application of AI to forecast stock
market movements and stock picking (Kim & Lee, 2004; Tseng, 2003). At this point,
the banking literature on AI was about its application in credit and loan analysis
(Baesens et al., 2005; Ince & Aktan, 2009; Kao et al., 2012; Khandani et al., 2010;
Fares, Butt, & Lee, 2022). In the initial stages of AI implementation, one has to develop
a rapid and stable AI infrastructure (Larson, 2021). Ince and Aktan (2009) used a data
mining approach to analyze credit scores and found that the AI-based data mining
approach was better than traditional approaches. Similarly, Khandani et al. (2010)
applied machine-learning-based models effectively in modeling consumer credit risk.
Every bank activity, from lending and traditional deposit-taking to investment banking
and asset management, depends on data (Kaya, 2019). For this reason, banks can
enhance speed, accuracy, and efficiency through autonomous data handling without the
need for human intervention. Potential uses of AI in banking can be categorized into

15
four categories: 1) front-office customer-oriented applications, 2) back-office
operations-oriented applications, 3) trading and portfolio management, and 4)
regulatory compliance. Banks are currently testing AI technologies rather than
implementing them fully in their procedures. Customer- and operations-oriented AI
solutions are being researched more heavily than others.

AI is being tested for real-time fraud detection and prevention in online banking. Credit
card fraud is among the most prevalent forms of cybercrime in recent years, driven by
the robust expansion of internet and mobile payments. To identify fraud, AI programs
check customers’ real-time credit card transactions for authenticity and cross-check
new transactions with previous amounts and locations. AI blocks transactions if it
detects risks. Some of the banks have already succeeded in implementing AI-driven
fraud detection systems, which have significantly reduced fraudulent transactions and
enhanced customer trust.

AI is also being applied to the Know-Your-Customer (KYC) process for client identity
verification. AI algorithms are reading client documents, and the authenticity of the
provided information is checked against web source data. Discrepancies are alarms
raised by AI algorithms, and bank employees conduct strict KYC validation.

Another area where banks are experimenting with AI technologies is customer service.
Chatbots, as virtual support personnel, interact with customers via text or voice and
answer their questions without the intervention of a bank employee. These chatbots are
designed to provide accurate and timely information, thereby enhancing customer
satisfaction. This emphasis on AI-facilitated customer service enhancement must instill
in our readers confidence in the industry's dedication to customer satisfaction.

Banks are also exploring AI to chart data from legal agreements or annual reports, for
instance, and mark up key clauses. Models are built in-house by AI software by looking
at the data and back-testing to gain experience from past mistakes and increase
accuracy.

Several financial technology instruments have progressively developed into

sophisticated AI applications. Notable instances include robo-advisors, which provide

16
fully automated asset management solutions, and online financial planning platforms
that assist individuals in optimizing consumption and savings behavior. As these
fintech solutions advance, they increasingly employ autonomous methods to analyze
data and identify patterns without human intervention.

There are numerous AI applications in banking and financial services (Umamaheswari,

Valarmathi & Lakshmi, 2023):
1. Marketing and Customer Service Chatbots: are self-learning computers that can
converse sensibly with humans via audio or chat. They are always available and
straightforward but have a very slow learning process.
2. Robo-Advisors for Financial Products: online platforms that use algorithms to
generate the portfolio automatically, reinvest dividends, and provide financial
advice. There is little to no human involvement.
3. Personalized Financial Services: Robo-advisors monitor customer goals and
suggest purchasing/selling stocks or bonds; they offer personalized attention to
customers regardless of risk tolerance.
4. Smart Wallets: Intelligence into mobile wallets for new-generation services like
chat, bus ticket reservation, cab, events, movie, and utility bill payments.
5. Hedge Fund Trading and Management: Trading and handling hedge funds can be
done on the circulate with the help of AI-based mobile app solutions for the banking
segment; AI-related hardware devices can fetch real-time information from
different financial markets across the world, and AI models can process different
monetary markets, so AI models can assist the customers in making decisions
promptly.
6. Offering high security: AI might completely alter banking security. Such security
should provide the customers with a sense of comfort regarding what the industry
has in store for the future because AI allows client-centric operations to be managed
effectively without the cost of hiring additional employees.
7. Efficiency and Accuracy Improve: Artificial intelligence improves the performance,
accuracy, and speed of mathematical calculations; it is capable of processing
gargantuan volumes of data, and banks can calculate the most optimal set of initial
margin-reducing transactions at a given point in time primarily depending on the
degree of initial margin reduction in the past when specific sets of those transactions
are employed.

17
The adoption speed of AI technologies is central to the fight against persistently low
profitability and competitiveness (Panakaje & Madhura, 2023). AI may assist bank
profitability in two ways: Firstly, by taking over some of the mundane functions of
bank employees, autonomous AI software would reduce the demand for lower-level
staff and render the remaining bank employees more productive. This is significant
since salaries for workers comprise a significant percentage of the bank's cost structure.
Secondly, adopting AI would facilitate income generation. For example, it may enable
banks to formulate new products and create more tailor-made products based on
customer decisions.

Tong and Yang (2024) investigate the impact of FinTech on the profitability of listed
banks. According to the authors, FinTech has two distinct functions. On the one hand,
it encourages product innovation through technological spillovers, increases production
efficiency, and assists banks in learning from and imitating regional FinTech
businesses—all of which improve bank profitability. FinTech also lowers operating
costs by increasing online services, changing traditional banking models, and
eliminating the need for much labor. Bank profitability is increased as a result of
increased cost and revenue efficiency. However, the researchers agree that FinTech
makes the market more competitive, forcing traditional banks to boost R&D spending
and adjust to changing consumer demands and technology breakthroughs to keep their
market share. This underscores the importance of customer-centric strategies, as banks
need to align their offerings with the evolving needs and preferences of their customers
to remain competitive.

2.4. The Role of AI in Islamic Finance

The Islamic finance sector, which is currently worth about $4 trillion and operates in
more than 80 countries, is heavily concentrated in a small number of markets. Only ten
countries hold nearly 95% of the world's Shari’ah-compliant assets, according to
analysis of multiple sources. Saudi Arabia and Iran each hold a 25% to 30% market
share, with Malaysia (12%), the United Arab Emirates (10%), Kuwait and Qatar
(5.5%), Bahrain and Turkey (3.5%), and Indonesia and Pakistan (2%), following in
order of market leadership as reported by Al Huda Centre of Islamic Banking and
Economics (CIBE) in December 2024, accelerating AI adoption under government

18
digital transformation initiatives like Saudi Arabia's Vision 2030. Integrating artificial
intelligence (AI) across Islamic finance institutions in the MENA has facilitated
significant advances in ethical compliance and business efficiency.

Research on AI's use in Islamic finance is scarce despite its potential—especially

considering the subtleties of globalization. The capabilities of AI have been thoroughly
investigated in conventional finance, but there are particular opportunities and
challenges for integrating AI due to the particular needs and tenets of Islamic finance.
Lack of thorough research makes it difficult to create AI solutions that are suited to the
unique requirements of Islamic finance, like guaranteeing Shari’ah compliance and
taking ethical issues into account (Aliyu & Yusof, 2016). Because Islamic finance must
negotiate various regulatory frameworks and market conditions across nations,
globalization further complicates the situation (Wilson, 2009). It is essential to
comprehend how AI can be used efficiently in this intricate, international setting to
advance Islamic finance. In addition, research on the socioeconomic effects of AI on
Islamic finance is necessary, as well as an analysis of how it might improve financial
inclusion and promote sustainable development (Beck, Demirgüç-Kunt, & Merrouche,
2013).

2.4.1. AI Adoption in Islamic Finance

AI is greatly enhancing the auditing and financial reporting frameworks of Islamic
finance. Auditing would involve a lot of human intervention, e.g., manual checks to
ensure compliance with Shari’ah had been followed. AI software, however,
automatically performs such auditing tasks as identifying variances and potential
violations of Islamic law. To flag transactions involving illegal activities like Gharar or
Riba, which would otherwise need manual intervention, machine learning algorithms
are essential (Shalhoob, 2025). AI-powered auditing tools offer real-time data analysis,
improve financial reporting transparency, and continuously track financial transactions
(Finastra, 2023).

Artificial intelligence is increasingly deployed to automate accounting functions,

including bookkeeping, financial reconciliations, and compliance verification. This is
particularly critical for financial instruments such as Murabaha (cost-plus financing)
and Mudarabah (profit-sharing), which require rigorous monitoring to ensure

19
adherence to Shari’ah principles (Shalhoob, 2025). Additionally, AI facilitates portfolio
rebalancing and asset management optimization by analyzing extensive datasets to
identify investment opportunities that conform to Shari’ah requirements.
In the realm of contract management, AI is fundamentally reshaping processes by
automating the review of contracts for Shari’ah compliance. Leveraging natural
language processing and machine learning, AI systems can detect clauses involving
prohibited elements—such as interest or excessive uncertainty—and generate
recommendations for amendments, thereby improving operational efficiency
(Shalhoob, 2025).

AI plays a pivotal role in regulatory compliance and risk management. AI-powered

compliance systems automatically detect and adapt to evolving local and international
regulatory requirements, enabling institutions to maintain alignment with both financial
regulations and Shari’ah compliance standards. These systems operate in real time,
monitoring regulatory updates and adjusting institutional processes accordingly
(Shalhoob, 2025). In risk management, AI supports financial and operational risk
analysis, forecasts market behaviors, evaluates credit and liquidity risks, and assists
institutions in making informed decisions to mitigate risks associated with non-
compliance to Shari’ah principles.

AI also drives operational efficiency by automating routine tasks such as data entry,
customer service, and transaction processing. This automation reduces the operational
workload, allowing institutions to focus on strategic priorities like customer
relationship management and Shari’ah compliance oversight. AI tools—including
chatbots and robo-advisors—deliver real-time, personalized customer service while
upholding ethical standards and enhancing user satisfaction (Shalhoob, 2025; SAMA,
2023). Moreover, AI streamlines procedures, lowers error rates, and strengthens
compliance with both Shari’ah and legal requirements (SAMA, 2023). Robo-advisors,
for example, offer customized, Shari’ah-compliant financial guidance, leveraging large
datasets to recommend appropriate products that adhere to Islamic financial principles
(Shalhoob, 2025).

The Takaful sector is also integrating AI to improve risk assessment, pricing, claims
management, and customer service. AI-driven tools help predict future risks, enabling

20
Takaful providers to design more effective and compliant insurance products
(Shalhoob, 2025; Eurisko, 2023).

While the adoption of AI in financial and securities markets is still emerging, rapid
growth is anticipated. AI is expected to enhance trading strategies, monitor market
trends, and identify investments that meet Shari’ah compliance requirements. For
instance, AI can be used to screen out investments in sectors prohibited under Islamic
law, such as alcohol or gambling. Increasingly, AI is being utilized to analyze financial
market data, offering insights into price trends and market risk, and supporting
Shari’ah-compliant investment decision-making (Shalhoob, 2025; Eurisko, 2023).

AI and Natural Language Processing (NLP) facilitate the creation of web-based

platforms that can reach a global audience. This technology allows easier access to
Islamic financial services, making it possible for more individuals and Small and
Medium Enterprises (SMEs) to benefit from Zakat and Qardh-Al-Hasan and enhance
accessibility (Syed et al., 2020). AI also plays a core role in verifying beneficiaries'
needs. Employing advanced algorithms, the system can examine the legitimacy of
requests for assistance to certain that resources reach people who genuinely need them
(Syed et al., 2020).

The recommender system, which is equipped with an AI- and NLP-based system,
recommends a list of potential recipients to lenders. This aspect facilitates matchmaking
between donors and recipients and maximizes resource allocation (Syed et al., 2020).
Last but not least, AI application in Islamic finance is also aimed at achieving broader
objectives such as social justice and improving the quality of life of Muslims globally.
By applying technology, the model also attempts to treat all members of the Ummah
with compassion and care, especially in times of disaster, such as the COVID-19
pandemic (Syed et al., 2020).

AI technologies are expected to improve operational effectiveness within Islamic

banking operations. This includes automating processes, reducing costs, and increasing
the speed of service delivery, which can lead to a more competitive market (Qudah et
al., 2023). The incorporation of AI allows for the development of innovative financial
products and services. For instance, AI can facilitate peer-to-peer lending,

21
crowdfunding, and robo-advisory services, which are becoming increasingly relevant
in the Islamic finance sector. AI can analyze vast amounts of data to better understand
customer demands and preferences. This insight enables Islamic financial institutions
to create more specialized products and services that align with the values and needs of
their clients.

This technological evolution positions MENA Islamic finance as a global testbed for
ethical AI applications. Future research should prioritize interdisciplinary collaboration
between AI ethicists and Usul al-Fiqh scholars to develop robust governance
frameworks that maintain jurisprudential integrity amid rapid digital transformation.

2.4.2. AI Integration Challenges in Islamic Finance

The integration of AI in Islamic finance has several challenges that must be addressed
to implement it effectively (Guellil & Bouri, 2024). Below are the significant
challenges:
1. Shari’ah Principles Compliance: Ensuring AI applications adhere to Shari’ah or
Islamic law is one of the most significant challenges. The financial services and
products based on AI must adhere to the moral tenets of Islamic finance, e.g.,
prohibiting Riba (interest) and excessive uncertainty (Gharar). This needs an
effective framework for the evaluation of AI algorithms to check if they are
Shari’ah-compliant (Guellil & Bouri, 2024; Hamadou et al., 2024). There is an
inverse correlation between AI application levels and Shari’ah-related complexity,
in the sense that the subtle guidelines can present difficulties to parties interested in
incorporating these principles into AI systems. The absence of structured Shari’ah-
compliant datasets also complicates such incorporation (Shalhoob, 2025).
2. Integration with Legacy Systems: There is a challenge in balancing between
innovation and traditional Islamic finance practices. AI has the potential to innovate
and create new financial products, but it has to be made sure that the innovations
do not compromise the fundamentals of Islamic finance. This requires collaboration
between the technology sector and Shari’ah scholars to balance the complexities of
modern financial needs while still adhering to traditional values (Guellil & Bouri,
2024).
3. Data Security and Privacy: Integrating AI requires dealing with enormous amounts
of private financial data. Particularly in Islamic finance, where trust is one of the

22
 fundamental tenets, the privacy and security of this data are crucial. To stop data
breaches and information exploitation, institutions need to have strong data
protection policies in place (Guellil & Bouri, 2024). Given the vulnerability of these
platforms to attacks, cybersecurity is yet another major concern for AI banks. One
participant noted that “AI systems in financial institutions are exposed to
cybersecurity risks,” highlighting the need for robust security protocols to safeguard
sensitive client and financial information. Additionally, the execution of effective
cybersecurity can be costly, complicating the integration process further (Hamadou
et al., 2024).
4. Limited Availability of Structured Shari’ah Data - needed to develop AI models
that can accurately interpret and apply Islamic legal principles. The respondents
ranked this hindrance as the first among all the identified hindrances, reflecting the
requirement for greater data structuring and availability (Shalhoob, 2025).
5. High Implementation Costs: One of the most referenced hindrances to AI adoption
is the prohibitive expense of AI technology implementation. There is a strong
positive correlation between implementation cost and AI application level in the
sense that institutions willing to invest more are likely to implement AI systems.
However, the price can deter smaller institutions from approaching such
innovations because they might lack the resources necessary (Shalhoob, 2025). The
high price tag can deter Islamic banks from employing AI applications because they
will need to weigh such costs against the anticipated benefits (Hamadou et al.,
2024).

2.5. AI Implementation Challenges

The integration of AI in banking, while promising significant advantages, also presents
several challenges that demand immediate attention. This study underscores the need
for proactive measures to address these challenges and ensure AI's responsible and
practical implementation in banking. These challenges can be addressed as follows:
• Data Privacy and Security: Most banking AI applications involve sensitive
customer data. Any security breach would result in devastating consequences.
This is one of the biggest challenges for banks: finding a balance between the
needs of AI systems, getting large volumes of data to learn from, and the
protection of the privacy of their customers where applicable laws like the
GDPR take effect. To solve this problem, advanced encryption techniques are

23
 implemented along with secure multi-party computation of data in both the
training and deployment cycles of AI. Adopting federated learning approaches
would allow AI models to be learned from decentralized data without breaching
any privacy of individual contributors. They are designing privacy-preserving
AI techniques, such as differential privacy, where noise is inserted into data to
protect individual records without losing most of the statistical utility of the data
(Lazo & Ebardo, 2023). Like what JP Morgan did in September 2022 to drive
innovative research in cryptography and secure distributed computation for
artificial intelligence, JP Morgan launched the firm-wide AIgoCRYPT Center
of Excellence. The AIgoCrypt Coe is a cross-functional team of researchers
from AI Research and partners across the business whose objective is to design,
share, and implement state-of-the-art techniques that enable secure computation
of encrypted data. Membership within the Coe keeps intense contact with
academia and the scientific arena via regular publications in the best venues of
cryptography/security, active participation in scientific events for
dissemination, and interactions with other international groups of researchers.
The center further advances the frontiers in many ways, including fully
homomorphic encryption, which would allow computation on encrypted data
without first decrypting it. This helps the bank to analyze sensitive customer
information while remaining private and secure.
• Integration with Legacy Systems: Most banks still depend on legacy IT setups
incompatible with state-of-the-art AI technologies. Therefore, the challenge is
integrating AI solutions into existing systems without disrupting critical
banking operations (Reddy, 2024). To address this challenge, adopting
microservices architecture and API-first approaches facilitates seamless
integration of AI solutions with existing banking systems. Cloud-based AI
platforms that can interface with on-premises legacy systems, connecting the
old with the new. Gradual migration strategies enable a paced implementation
of AI solutions with coexistence between AI and legacy.
• Algorithmic Bias and Fairness: If adequately designed and monitored, AI
systems would exacerbate the existing biases in financial decision-making. This
concerns areas like credit scoring or loan approval, where AI-driven decisions
may show explicit discrimination against certain demographic groups. To
resolve this problem and the bank be ready for AI implementation, Periodic

24
 auditing of AI models with a wide range of data sets is taking place to discover
and correct arising biases and establish regulations and industry standards
regarding the use of non-discriminatory AI in financial services. A good
example is the recently proposed EU AI Act (Aggrey et al., 2024). For example,
Upstart, a lending platform, was founded over a decade ago to improve access
to credit by applying innovative technology and data science. It uses AI in credit
decisions. On the regular fairness audit, and after various bias mitigation
techniques had been implemented, approvals were noted as 27% higher, with
16% lower interest rates for minority applicants than traditional models. They
made significant progress towards this goal—serving more than 3 million
customers and facilitating more than $40.5 billion in loans as of September
2024—and along the way, we have developed industry-leading techniques for
detecting and avoiding unlawful bias on our platform.

2.6. The Impact of AI on Banks

While most of the research suggests a positive relationship between AI adoption and
banking profitability, there is an opinion that AI negatively affects banking
profitability. This is because:
• Implementation costs: The initial investment in AI systems is usually high, with
the costs often outweighing short-term profits. The initial years of AI
implementation can reduce banks' profitability since implementation costs are
incredibly high, including infrastructure modifications, staff training, and
regulatory compliance (Alzeghoul & Alsharari, 2025). AI adoption involves
significant upfront costs for implementation, maintenance, and repair, as well
as the need for technical expertise. These costs can strain smaller or less
financially robust banks, potentially reducing their profitability (Koerselman,
2025).
• The negative relationship between AI adoption and banking profitability could
be influenced by competition from NBFIs. Academic literature highlights
several relevant points:
a) Increased competition from tech-based NBFIs: Fintech companies are
substituting traditional banking services through innovative, ease-of-use online
solutions. With this increased competition, banks' market share and profitability
have been squeezed (Tarawneh et al., 2024; Bogaard et al., 2024).

25
 b) Operational efficiency of NBFIs: Tech-based NBFIs have lower overheads,
which allow them to be price-competitive. This makes it difficult for traditional
banks' margins since they invest heavily in AI to remain in the competition
(Bogaard et al., 2024).
c) Shifting customer expectations: Customers' expectations have shifted
significantly, and most now demand quick, personalized, and easy digital
services. NBFIs are well suited to provide such expectations, leading to possible
customer loss for conventional banks (Tarawneh et al., 2024; Bogaard et al.,
2024).
d) Regulatory asymmetry: NBFIs typically have less regulation than conventional
banks and, consequently, have more excellent service delivery and pricing
freedom. regulatory easing can amplify the competitive pressure on banks
(Bogaard et al., 2024; OECD, 2020).

2.7. Gaps in the Literature and Research Hypotheses

The literature review has identified several gaps in understanding the relationship
between AI readiness and banking performance in the MENA region. First, there is a
lack of empirical research that quantifies the impact of government AI readiness on
banking profitability in the MENA region. Second, limited research examines the
moderating role of AI readiness on the relationship between economic growth and
banking profitability.

Based on the discussions, the following hypothesis is proposed:

• H1: There is a positive relationship between government AI readiness and
banking profitability in the MENA countries. This effect is comparable for both
Islamic banks and conventional banks within the region.
• H2: AI readiness has a positive impact on economic growth-bank profitability
links in the MENA region.

This study aims to address these gaps by examining the direct and indirect links
between government AI readiness and banking profitability in MENA countries.

26
CHAPTER 3: RESEARCH METHODOLOGY

This chapter presents the methodology employed to analyze how the Government AI
Readiness Index affects bank performance in the MENA region. The research design
was quantitative, with panel data analysis applied to quantify the impact of AI readiness
on core banking performance metrics. The research aims to close the gap in the
literature to address the unique financial environment of the MENA region specifically
by testing the nexus between government AI readiness and profitability. The central
research question guiding this investigation is: To what extent does AI readiness
influence banking profitability in the MENA region?

3.1. Data
The study period spans from 2020 to 2023, encompassing a total of four years
(depending on data availability). It includes a sample of 167 banks, comprising 51
Islamic banks and 116 conventional banks, originating from 14 dual-banking countries
(see Table A of the Appendix).

Table 1: Description of variables

Variable Description Source
ROA Bank Profitability = Return on Assets (dependent variable) FitchConnect
Focus independent variables
SC AI Readiness Index Oxford Insights
GOV Governance Oxford Insights
TEC Technology Oxford Insights
DAT Data & Infrastructure Oxford Insights
Control independent variables
CAP Capitalization = Equity / Total Assets FitchConnect
ASQ Asset Quality = Non-Performing Loans / Gross Loans FitchConnect
EFF Efficiency = Income / Costs FitchConnect
SIZ Bank Size = Log of Total Assets FitchConnect
ECG Economic Growth = Real GDP per Capita Growth Rate World Bank

The study employs the following variables (see Table 1 above):

• Dependent Variable - Return on Assets (ROA): Expressed as net income divided
by total assets, ROA is a widely used measure of bank performance. It shows how
effectively a bank can generate profit out of its assets, hence making it a suitable
measure of profitability.

27
• Independent Variable - Government AI Readiness Index: Provided by Oxford
Insights, the index is utilized to determine the preparedness of governments towards
adapting and implementing AI technologies. Governance, infrastructure, and data
availability are also analyzed in this index to give a holistic perspective of AI
readiness.
• Country-Specific Control Variable - Economic Growth (ECG): As a percentage
change in yearly GDP, it factors in macroeconomic factors that potentially influence
banking performance, such as loan demand, interest rates, and sector stability.
• Bank-Specific Control Variables: a) Capitalization - measured as a ratio of equity
to total assets, b) Asset Quality - the ratio of non-performing loans to gross loans,
c) Management Efficiency – income to cost ratio, and d) Bank Size representing the
banks’ ownership of assets.

This study has several important limitations concerning data. The Government AI
Readiness Index data is restricted to 2019-2023 period. This temporal constraint is
significant because AI adoption and readiness represent relatively recent phenomena in
the global context, particularly for the MENA region. The index's methodology also
presents limitations, as it aggregates multiple indicators across three main pillars:
Government, Technology Sector, and Data and Infrastructure (Oxford Insights, 2024).
Some researchers have questioned the weighting criteria used in the index, suggesting
that alternative analytical approaches, such as arithmetic and geometric non-linear
functions, might provide more nuanced assessments of country rankings (Nasution et
al., 2024). Additionally, data availability issues affect certain countries, with some
lacking comprehensive or up-to-date information on AI policies, potentially reducing
the reliability of their scores.

3.2. Model Specification

A quantitative research design is employed, utilizing random effects panel data
estimator to examine the relationship between AI readiness and banking performance
over time while controlling for country-specific effects. The baseline model takes the
following form:
𝐵𝑃!"# = 𝛼 + 𝛽$ 𝐴𝐼"# + 𝛽% 𝐵𝑆!"# + 𝛽& 𝐶𝑆"# + 𝜖!"# (1)

28
Where: BP is bank profitability, AI – is AI Readiness Index (focus variable: SC, GOV,
DAT, and TEC), BS – is bank-specific factors (control variables), CS – is country-
specific factors (control variables), and e is an error term.

First, we plot box plots to identify outliers. Consequently, winsorization is applied to

these variables: ROA (below 2 and above 96 percentiles), ECG (at 3 and 99), and CAP,
ASQ, EFF, and SIZ (at 1 and 99). Next, we run diagnostics to decide between
homogeneous and heterogeneous models based on Breusch-Pagan LM test which
suggests using heterogeneous models. Though Hausman test suggests using Fixed
Effect model, we opt for Random Effects model because of inclusion of a dummy
variable differentiating between conventional and Islamic banking models.

To address the second research question, we modify the baseline model by interacting
economic growth with AI Readiness factors to identify its indirect impact on bank
profitability. The banking model dummy (Islamic bank = 1) will also be included to
assess whether Islamic and conventional banks are equally affected by this technology.

3.3. Empirical Methods

This study employs a quantitative, correlational approach to examine the relationship
between banking performance and AI maturity in the MENA region. Specifically, the
research addresses the core question: To what extent does AI readiness influence
banking profitability in the MENA region? AI readiness is operationalized using the
Oxford Insights Government AI Readiness Index, and banking performance is
measured through standard financial performance indicators.

Given the longitudinal nature of the research questions and considerations regarding
data availability, this study utilizes panel data analysis. This type of analysis is
particularly suitable because it allows for examining both cross-sectional variations
across MENA countries and time-series variations within individual countries.
Moreover, it facilitates controlling country-specific, time-invariant factors that may
influence banking performance, thus providing a more robust test of the relationship
between AI readiness and banking profitability. Additionally, panel data analysis
reduces potential biases arising from omitted variables and endogeneity issues,
enhancing the validity of the findings.

29
Alternative research designs, such as cross-sectional analysis, were considered less
effective in capturing the dynamic relationship between AI readiness and banking
performance over time. While cross-sectional analysis would provide only a snapshot
at a specific time, it would fail to reflect how evolving AI adoption levels influence
banking profitability throughout the studied period. By employing panel data analysis,
this study aims to provide policymakers, bank executives, and investors with deeper
insights into how AI maturity impacts banking performance within the MENA region.

30
CHAPTER 4: DISCUSSION OF FINDINGS

Based on the panel data analysis described in the methodology chapter, this chapter
presents the findings on the impact of Government AI Readiness on banking
performance in the MENA region. The chapter begins with the key variables'
descriptive statistics, correlation analysis, and regression results.

4.1. Preliminary Analysis

Table 2 provides definitions and descriptive statistics of the variables used in the
present study. The descriptive statistics highlight the diversity in bank characteristics,
profitability, and AI readiness, providing a foundation for analyzing how these factors
interact.

Table 2: Descriptive statistics

Mean Min Max SD Skew Kurt
ROA: Earnings 1.882 -3.490 17.590 3.219 3.029 14.970
CAP: Capitalization 16.389 3.390 86.478 15.288 2.576 9.079
ASQ: Asset Quality 10.993 0.000 99.630 17.044 3.424 15.724
EFF: Efficiency 0.026 -0.001 0.188 0.022 4.267 26.969
SIZ: Bank Size 9.778 7.471 12.269 0.897 -0.163 2.468
ECG: Economic Growth 0.654 -19.748 10.429 5.230 -1.353 6.146
SC: AI Readiness 51.974 19.330 72.395 12.573 -0.426 2.905
GOV: Governance 54.476 10.021 82.534 19.519 -0.348 1.981
TEC: Technology 38.395 17.353 56.666 8.466 0.089 3.364
DAT: Data and 63.050 27.112 82.771 14.023 -0.376 2.292
Infrastructure
Notes: Source: Author’s own

• ROA: Earnings have an average value of 1.882, with a minimum of -3.490 and a
maximum of 17.590. The standard deviation of 3.219 indicates moderate variability
around the mean. The positive skewness of 3.029 shows that most banks have lower
earnings, while a few have extremely high earnings. The banking industry has a few
very profitable banks, but the majority are less profitable, according to the high
kurtosis of 14.970, which suggests many extreme values. This data does, however,
also highlight the sector's potential for expansion and advancement, providing some
hope for the future.
• CAP: Capitalization averages at 16.389, ranging from 3.390 to 86.478. The high
standard deviation of 15.288 indicates significant variability. The positive skewness
of 2.576 indicates that most banks have lower capitalization, while a tiny percentage

31
 have very high capitalization. Given the sharp peak and many extreme values
revealed by the kurtosis of 9.079, it appears that a small number of powerful banks
control a sizable portion of the banking industry. This focus focuses on the
application of risk management and diversification measures, informing
stakeholders about industry risk management procedures.
• ASQ: The average asset quality of 10.993, with values ranging from 0.000 to
99.630, shows significant variability. According to the positive skewness of 3.424,
some banks have exceptionally high asset quality, while the majority have lower
asset quality. The high kurtosis of 15.724 indicates a significant number of extreme
values, underlining the urgent need for better asset management practices to reduce
variability in how banks manage their assets.
• EFF: Efficiency has a mean of 0.026, with a minimum of -0.001 and a maximum
of 0.188. The low standard deviation of 0.022 indicates low variability. The strong
positive skewness of 4.267 shows that most banks operate with low efficiency,
while a few are extremely efficient. The very high kurtosis of 26.969 indicates a
significant number of extreme values, suggesting that efficiency is challenging for
many banks, with only a few achieving high efficiency.
• SIZ: Bank Size averages at 9.778, ranging from 7.471 to 12.269. The low standard
deviation of 0.897 indicates low variability. The slight negative skewness of -0.163
suggests a slight tendency towards larger banks. The moderate kurtosis of 2.468
indicates a balanced distribution of bank sizes in the sector, providing a sense of
stability and reassurance.
• ECG: Economic Growth averages 0.654, ranging from -19.748 to 10.429. A high
standard deviation of 5.230 indicates a high degree of variability. The majority of
the time have moderate economic growth, according to the negative skewness of -
1.353, but sometimes see a sharp decline. Significant extreme values are suggested
by the high kurtosis of 6.146, which denotes economic instability with sporadic
sharp downturns.
• SC: AI Readiness averages 51.974, ranging from 19.330 to 72.395. The moderate
standard deviation of 12.573 indicates moderate variability. The slight negative
skewness of -0.426 suggests a slight tendency towards higher AI readiness. The
moderate kurtosis of 2.905 indicates a reasonably even distribution, suggesting that
most banks are moderately prepared for AI integration.

32
• GOV: Governance ranges from 10.021 to 82.534, with a mean of 54.476. There is
substantial variability, as evidenced by the high standard deviation of 19.519. A
slight tendency towards higher governance scores is indicated by the -0.348
negative skewness. A fairly even distribution is indicated by the moderate kurtosis
of 1.981, which implies that governance practices are generally good across nations.
• TEC: Technology averages 38.395, ranging from 17.353 to 56.666. The moderate
standard deviation of 8.466 indicates moderate variability. The near-normal
distribution indicated by the skewness of 0.089 suggests a balanced technology
adoption across countries. The moderate kurtosis of 3.364 indicates a reasonably
even distribution.
• DAT: Data and Infrastructure averages 63.050, ranging from 27.112 to 82.771. The
moderate standard deviation of 14.023 shows moderate variability. The slight
negative skewness of -0.376 suggests a slight tendency towards higher scores. The
moderate kurtosis of 2.292 indicates a reasonably even distribution, suggesting that
most countries have good data and infrastructure capabilities.

Table 3 presents the Pearson correlation coefficients for the variables under study.

Table 3: Correlation matrix

ROA CAP ASQ EFF SIZ ECG SC GOV TEC DAT
ROA 1.00
CAP 0.18* 1.00
ASQ 0.11* 0.41* 1.00
EFF 0.72* 0.06 0.16* 1.00
SIZ -0.22* -0.50* -0.43* -0.03 1.00
ECG 0.04 -0.10* -0.26* -0.02 0.13* 1.00
SC -0.30* -0.26* -0.38* -0.24* 0.51* 0.37* 1.00
GOV -0.17* -0.31* -0.38* -0.16* 0.44* 0.45* 0.92* 1.00
TEC -0.29* -0.22* -0.29* -0.26* 0.47* 0.25* 0.93* 0.83* 1.00
DAT -0.39* -0.12* -0.32* -0.28* 0.46* 0.21* 0.85* 0.59* 0.74* 1.00
Notes: SC stands for AI Readiness, CAP – capitalization, ASQ – asset quality, EFF – management
efficiency, SIZ – bank size, ECG – economic growth, and IDS – bank specialization dummy. Standard
errors in parentheses. * p < 0.10, ** p < 0.05. Source: Author’s own

Several key relationships warrant attention. ROA (Return on Assets) is positively

correlated with CAP (Capitalization) at 0.18 and ASQ (Asset Quality) at 0.11,
indicating that banks with higher capitalization and asset quality earn more. It is also
highly positively correlated with EFF (Efficiency) at 0.72, indicating that more efficient
banks earn more. Negative correlations with SIZ (Bank Size) at -0.22, SC (AI
Readiness) at -0.30, GOV (Governance) at -0.17, TEC (Technology) at -0.29, and DAT

33
(Data and Infrastructure) at -0.39 indicate that larger banks and those operating in
countries with higher AI readiness, governance, technology, and data infrastructure
scores tend to have lower earnings. These relationships provide strategic insights into
bank performance drivers so that decision-makers can make well-informed decisions.

For instance, a capital-intensive bank will likely possess more significant funds for
investing in high-asset quality. A big bank, on the other hand, is less likely to maintain
high asset quality due to its operational size. The audience understands and relates to
the data thanks to these illustrations. Given that CAP (capitalization) and ASQ (asset
quality) have a positive correlation of 0.41, banks with higher capitalization would have
higher-quality assets. Negative correlations with SIZ (Bank Size) at -0.50, ECG
(Economic Growth) at -0.10, SC (AI Readiness) at -0.26, GOV (Governance) at -0.31,
TEC (Technology) at -0.22, and DAT (Data and Infrastructure) at -0.12 indicate that
larger banks and those operating in countries with higher scores in economic growth,
AI readiness, governance, technology, and data infrastructure tend to have lower
capitalization.

A bank with high asset quality may attract more investors and have a larger
capitalization. However, because of the size of its operations, a larger bank might find
it more challenging to maintain high asset quality. These applicable ramifications make
the analysis more interesting by assisting the audience in comprehending the data's
practical significance. ASQ (Asset Quality) shows positive correlations with EFF
(Efficiency) at 0.16 and CAP (Capitalization) at 0.41, indicating that better asset quality
is associated with higher efficiency and capitalization. Negative correlations with SIZ
(Bank Size) at -0.43, ECG (Economic Growth) at -0.26, SC (AI Readiness) at -0.38,
GOV (Governance) at -0.38, TEC (Technology) at -0.29, and DAT (Data and
Infrastructure) at -0.32 suggest that larger banks and those operating in countries with
higher scores in economic growth, AI readiness, governance, technology, and data
infrastructure tend to have lower asset quality.

EFF (Efficiency) has a strong positive correlation with ROA (Return on Assets) at 0.72,
indicating that more efficient banks tend to have higher earnings. Positive correlations
with ASQ (Asset Quality) at 0.16 suggest that higher efficiency is associated with better
asset quality. Negative correlations with SIZ (Bank Size) at -0.03, SC (AI Readiness)

34
at -0.24, GOV (Governance) at -0.16, TEC (Technology) at -0.26, and DAT (Data and
Infrastructure) at -0.28 indicate that larger banks and those operating in countries with
higher scores in AI readiness, governance, technology, and data infrastructure tend to
have lower efficiency.

SIZ (Bank Size) shows negative correlations with ROA (Return on Assets) at -0.22,
CAP (Capitalization) at -0.50, ASQ (Asset Quality) at -0.43, EFF (Efficiency) at -0.03,
and positive correlations with ECG (Economic Growth) at 0.13, SC (AI Readiness) at
0.51, GOV (Governance) at 0.44, TEC (Technology) at 0.47, and DAT (Data and
Infrastructure) at 0.46. This suggests that larger banks tend to have lower earnings,
capitalization, asset quality, and efficiency but are more likely to operate in countries
with higher scores in economic growth, AI readiness, governance, technology, and data
infrastructure.

ECG (Economic Growth) shows positive correlations with SIZ (Bank Size) at 0.13, SC
(AI Readiness) at 0.37, GOV (Governance) at 0.45, TEC (Technology) at 0.25, and
DAT (Data and Infrastructure) at 0.21, indicating that higher economic growth is
associated with larger banks and countries with higher scores in AI readiness,
governance, technology, and data infrastructure. Negative correlations with CAP
(Capitalization) at -0.10, ASQ (Asset Quality) at -0.26, and EFF (Efficiency) at -0.02
suggest that higher economic growth is associated with lower capitalization, asset
quality, and efficiency.

SC (AI Readiness) shows positive correlations with SIZ (Bank Size) at 0.51, ECG
(Economic Growth) at 0.37, GOV (Governance) at 0.92, TEC (Technology) at 0.93,
and DAT (Data and Infrastructure) at 0.85, indicating that higher AI readiness is
associated with larger banks and countries with higher scores in economic growth,
governance, technology, and data infrastructure. Negative correlations with ROA
(Return on Assets) at -0.30, CAP (Capitalization) at -0.26, ASQ (Asset Quality) at -
0.38, and EFF (Efficiency) at -0.24 suggest that higher AI readiness is associated with
lower earnings, capitalization, asset quality, and efficiency.

GOV (Governance) shows positive correlations with SIZ (Bank Size) at 0.44, ECG
(Economic Growth) at 0.45, SC (AI Readiness) at 0.92, TEC (Technology) at 0.83, and

35
DAT (Data and Infrastructure) at 0.59, indicating that higher governance scores are
associated with larger banks and countries with higher scores in economic growth, AI
readiness, technology, and data infrastructure. Negative correlations with ROA (Return
on Assets) at -0.17, CAP (Capitalization) at -0.31, ASQ (Asset Quality) at -0.38, and
EFF (Efficiency) at -0.16 suggest that higher governance scores are associated with
lower earnings, capitalization, asset quality, and efficiency.

TEC (Technology) shows positive correlations with SIZ (Bank Size) at 0.47, ECG
(Economic Growth) at 0.25, SC (AI Readiness) at 0.93, GOV (Governance) at 0.83,
and DAT (Data and Infrastructure) at 0.74, indicating that higher technology scores are
associated with larger banks and countries with higher scores in economic growth, AI
readiness, governance, and data infrastructure. This underscores the increasing role of
technology in banking. Negative correlations with ROA (Return on Assets) at -0.29,
CAP (Capitalization) at -0.22, ASQ (Asset Quality) at -0.29, and EFF (Efficiency) at -
0.26 suggest that higher technology scores are associated with lower earnings,
capitalization, asset quality, and efficiency.

DAT (Data and Infrastructure) shows positive correlations with SIZ (Bank Size) at
0.46, ECG (Economic Growth) at 0.21, SC (AI Readiness) at 0.85, GOV (Governance)
at 0.59, and TEC (Technology) at 0.74, demonstrating that better scores in data and
infrastructure are correlated with bigger banks and economies scoring higher in
economic growth, readiness for AI, governance, and technology. It indicates the role of
data and infrastructure in bank operations. The negative correlations against ROA
(Return on Assets) at -0.39, CAP (Capitalization) at -0.12, ASQ (Asset Quality) at -
0.32, and EFF (Efficiency) at -0.28 reveal that better data and infrastructure scores
correlate with lower return on assets, capitalization, asset quality, and efficiency.

Overall, these correlations suggest that larger banks and those operating in countries
with higher scores in AI readiness, governance, technology, and data infrastructure tend
to have lower earnings, capitalization, asset quality, and efficiency. Economic growth
is positively associated with larger banks and countries with higher scores in AI
readiness, governance, technology, and data infrastructure but negatively associated
with capitalization, asset quality, and efficiency.

36
4.2. Empirical Analysis
Table 4 presents the results of random effects (RE) regression models designed to
assess the impact of AI readiness (SC) on bank profitability, measured by return on
assets (ROA). The effect of AI Readiness (SC) on bank performance is an evolving
process. At first, in models M1 to M5, it has a strong negative effect, implying that
increased AI readiness could be bringing initial problems or burdens.

Table 4: Regression outputs – Aggregate variable (SC)

M1 M2 M3 M4 M5 M6 M7 M8 M9
SC -0.06** -0.09** -0.09** -0.10** -0.08** 0.01 0.01 0.02 0.01
(0.01) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
L.CAP -0.01 -0.05** -0.04** -0.05** -0.04** -0.04** -0.04** -0.04**
(0.01) (0.02) (0.02) (0.02) (0.01) (0.01) (0.01) (0.01)
L.ASQ 0.03** 0.03** 0.02 -0.01 -0.01 -0.01 -0.01
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
L.EFF 47.89** 55.86** 50.65** 50.44** 51.39** 50.72**
(13.89) (13.88) (14.24) (14.26) (14.25) (14.29)
L.SIZ -1.14** -0.83** -0.83** -0.78** -0.84**
(0.33) (0.26) (0.26) (0.26) (0.26)
ECG -0.08** -0.08** -0.08** -0.21**
(0.02) (0.02) (0.02) (0.11)
IDS 0.02 3.03 -0.01
(0.51) (1.98) (0.50)
IDS x SC -0.06
(0.04)
ECG x SC 0.01
(0.00)
Constant 5.35** 7.25** 7.69** 6.89** 17.04** 9.26** 9.25** 7.86** 9.44**
(0.69) (1.02) (1.14) (1.15) (3.15) (2.53) (2.58) (2.70) (2.55)
Obs 710 492 372 372 372 343 343 343 343
Notes: SC stands for AI Readiness, CAP – capitalization, ASQ – asset quality, EFF – management
efficiency, SIZ – bank size, ECG – economic growth, and IDS – bank specialization dummy. Standard
errors in parentheses. * p < 0.10, ** p < 0.05. Source: Author’s own

This negative relationship can be explained by high implementation costs. The upfront
costs associated with developing and implementing AI technologies, including
infrastructure investments, data acquisition, and personnel training, may initially
outweigh any potential revenue gains. Moreover, banks may require time to effectively
integrate AI technologies into their existing business processes and realize the full
benefits of these investments. A learning curve associated with AI adoption could result
in short-term inefficiencies and reduced profitability. Besides, resistance to change
within the organization, coupled with a lack of alignment between AI initiatives and
overall business strategy, could hinder the effective implementation of AI technologies
and limit their impact on profitability. In addition, as discussed by Tarawneh et al.
(2024), Fintech companies are substituting traditional banking services through

37
innovative, ease-of-use online solutions. With this increased competition, banks'
market share, as well as their profitability, have been squeezed. This could be due to
several factors, including:
• AI-Driven Disintermediation Margin Compression. Non-bank disruptors, such as
fintech companies and big tech players, use AI to offer lower-cost, hyper-
personalized financial products (such as lending, payments, and wealth
management). This disrupts the revenue models of incumbent banks: (a) FinTech
uses AI to bypass legacy infrastructure so that loans are approved faster and
dynamic pricing programs that erode the interest margins of incumbent banks (Lazo
& Ebardo, 2023); (b) Large-tech firms integrate AI into platforms (e.g., e-
commerce platforms), commodifying customer touchpoints and reducing the ability
of banks to earn revenue from transaction data (Lazo & Ebardo, 2023).
• Customer Expectations and Market Share Erosion. Non-bank competitors reframe
customer expectations regarding speed and personalization, forcing banks to divert
resources towards AI-enabled customer retention: (a) Fintechs using AI-powered
chatbots and predictive analytics market to younger, tech-savvy customer segments,
eroding banks' deposit bases (Lazo & Ebardo, 2023); (b) Banks need to invest in
AI-powered solutions (e.g., biometric authentication and fraud detection) to match
non-bank service levels, contributing to the costs of operations (Rao et al., 2024).
• Regulation and Transparency Concerns. Banks face uneven regulatory burdens
compared to non-bank rivals, complicating the adoption of AI: (a) Non-banks are
in less regulated conditions, allowing faster implementation of AI (Lazo & Ebardo,
2023); (b) Banks' AI initiatives entail costly compliance with explainability and
anti-discrimination requirements, delaying implementation and reducing agility
(Lazo & Ebardo, 2023).

Nevertheless, when additional variables are added to models M6 to M9, the pure effect
of AI readiness turns out to be insignificant, which may reflect a delicate interaction of
various factors that could dominate its effect.

Capitalization (L.CAP) always has a negative and significant impact on performance

in models M3 to M9. This implies that greater capitalization in the previous period is
connected with lower current performance, potentially due to cautious policy or

38
declining returns on additional capital. Next, Asset Quality (L.ASQ) is significantly
and positively correlated with performance in models M3 and M4, suggesting that
increased asset quality in the previous period leads to increased current performance.
The significance is lost in later models, suggesting that other variables are more
powerful over time.

Management Efficiency (L.EFF) is an exceptional determinant. The positive and

statistically significant influence of L.EFF in all the models ranging from M4 to M9
indicates that certain effective management practices, broadly including cost control,
strategic planning, and risks, will improve the performance of a particular bank.
Meanwhile, Bank Size (L.SIZ) has a negative and significant correlation with
performance in models M5 through M9. Larger bank size in the previous period is
associated with lower current performance, perhaps due to inefficiencies or challenges
in handling larger institutions.

For models M6 to M9, Economic Growth (ECG) is found to have negative and
significant performance impacts. Such an unexpected result infers that in periods of
higher economic growth, the banks might be involved in riskier ventures that do not
directly lead to better performance in the short term.

Bank Specialization (IDS) has no significant direct influence on performance in models

M7 to M9. Neither is the interaction term between bank specialization and AI readiness
significant, i.e., the combined impact of these variables is not significant on
performance.

In Model M6, the emphasis is placed on analysis to examine the potential moderating
role of AI readiness (SC) on the relationship between economic growth (ECG) and bank
profitability (ROA). The results show a statistically significant and positive interaction
term coefficient that proves that AI readiness moderates the ECG-ROA relationship.
The moderating variable alters the direction or magnitude of a relationship between an
independent and dependent variable. In the present instance, AI readiness modifies the
relationship between economic growth and bank profitability such that the impact of
economic growth on ROA varies with the level of AI readiness of the bank.

39
While the economic growth (ECG) independently has a negative effect on ROA—
potentially due to increased competition, greater risk-taking, or other forces that deplete
profitability while the economy expands—the availability of high AI readiness (SC)
works to reduce this negative impact as shown in Figure 1. AI-prepared banks are better
positioned to ride out the difficulties and seize the opportunities economic growth
presents. This may be because AI enables them to enhance risk management functions,
optimize operating efficiency, identify and chase new market segments, or tailor
products and services to evolving customer demands more effectively than their less
technology-advanced competitors. Therefore, AI enables banks to utilize the benefits
of economic growth more and mitigate potential negatives.

Generally, the results suggest that AI readiness, capitalization, asset quality,

management efficiency, bank size, and economic growth are all significant
determinants of bank performance. Good management practices always have a strong
positive impact, while larger bank size and higher capitalization have negative impacts.
The negative impact of economic growth may be due to riskier behavior during
expansion periods. The interaction terms suggest that the overall impacts of these
variables are not significant.

Table 5: Regression outputs - With components (Part 1)

M1 M2 M3 M4 M5
GOV -0.01 -0.01 0.01 -0.01 -0.02
(0.01) (0.01) (0.01) (0.01) (0.01)
TEC -0.03 0.06* 0.08** 0.08**
(0.03) (0.03) (0.03) (0.03)
DAT -0.11** -0.11** -0.12**
(0.01) (0.02) (0.02)
L.CAP 0.01 -0.02
(0.01) (0.02)
L.ASQ 0.02
(0.01)
Constant 3.21** 3.89** 6.48** 6.80** 7.90**
(0.46) (0.84) (0.85) (1.09) (1.18)
Observations 710 710 710 492 372
Notes: GOV stands for Government pillar, TEC– Technology pillar, DAT – Data and infrastructure pillar,
CAP – capitalization, ASQ – asset quality, EFF – management efficiency, SIZ – bank size, ECG –
economic growth, and IDS – bank specialization dummy. Standard errors in parentheses. * p < 0.10, ** p
< 0.05. Source: Author’s own

Tables 5-6 explore the impact of individual components of AI readiness—governance

(GOV), technology (TEC), and data (DAT)—on bank profitability. The RE cross-

40
model comparison of the estimates of all the models, from M1 to M11, offers revealing
information on the determinants of bank performance. In this respect, the method takes
into consideration factors that comprise governance, technology, and data
infrastructure.

The overall implication is that governance (GOV) does not directly influence bank
performance since most of the coefficients are insignificant across models M1-M10.
Nonetheless, in M11, the coefficient on governance now turns positive and significant,
which implies governance may have a positive impact when other factors are controlled
for. That is to say, for better performance, specific good governance may prevail,
though its effect may be masked by other variables when using less comprehensive
models.

Table 6: Regression outputs - With components (Part 2)

M6 M7 M8 M9 M10 M11
GOV -0.02 -0.02 -0.01 0.01 0.01 0.02*
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
TEC 0.08** 0.08** 0.09** 0.01 0.01 0.04
(0.03) (0.03) (0.03) (0.03) (0.03) (0.04)
DAT -0.13** -0.13** -0.12** -0.03* -0.03* -0.04*
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02)
L.CAP -0.02 -0.02 -0.03* -0.03** -0.03** -0.03*
(0.02) (0.02) (0.02) (0.01) (0.01) (0.01)
L.ASQ 0.02 0.02 0.01 -0.01 -0.01 0.01
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
L.EFF 49.22** 49.22** 57.62** 52.65** 52.49** 53.11**
(13.66) (13.66) (13.71) (14.21) (14.23) (14.25)
L.SIZ -0.98** -0.80** -0.79** -0.74**
(0.31) (0.26) (0.26) (0.26)
ECG -0.06** -0.06** -0.06**
(0.03) (0.03) (0.03)
IDS 0.19 2.66
(0.50) (2.27)
IDS x GOV -0.02
(0.02)
IDS x TEC -0.06
(0.06)
IDS x DAT 0.02
(0.03)
Constant 6.95** 6.95** 15.41** 9.64** 9.48** 8.11**
(1.17) (1.17) (2.95) (2.51) (2.55) (2.72)
Observations 372 372 372 343 343 343
Notes: GOV stands for Government pillar, TEC– Technology pillar, DAT – Data and infrastructure pillar,
CAP – capitalization, ASQ – asset quality, EFF – management efficiency, SIZ – bank size, ECG –
economic growth, and IDS – bank specialization dummy. Standard errors in parentheses. * p < 0.10, ** p
< 0.05. Source: Author’s own

41
Technology (TEC), at the outset, is insignificant for model M2. For models M3 to M8,
the technology coefficients are positive and significant, suggesting that better
technology scores track better performance for the bank. This also proves that the
banking sector would have to invest in and develop technologically, making banks
competitive and efficient. The significance of technological impact decreases from
models M9 to M11, probably because other determining factors are included.

Data and Infrastructure (DAT) consistently shows a negative and significant impact on
bank performance across models M3 to M11. This unexpected finding implies that
poorer performance is linked to higher data and infrastructure scores. Infrastructure and
data investments may initially result in expenses that impair performance before
producing favorable returns. On the other hand, it might point to difficulties in
efficiently leveraging these investments.

Capitalization (L.CAP) mostly shows an insignificant impact on performance, except

in models M8 to M11, where the coefficients become negative and significant. This
suggests that higher capitalization in the previous period is associated with lower
current performance, possibly due to conservative strategies or diminishing returns on
additional capital.

On average, asset Quality (L.ASQ) does not have a significant direct effect on
performance; the coefficients are mostly insignificant. This suggests that asset quality
in the previous period does not have a significant effect on current performance.

Management Efficiency (L.EFF) is a significant factor because it consistently shows

positive and very high coefficient values across models M6 to M11. This suggests that
efficient management is essential for financial institutions to succeed because high
efficiency in the past is a good predictor of better performance in the present.

In models M8 through M11, Bank Size (L.SIZ) significantly and negatively affects
performance. Lower performance now is correlated with larger bank sizes in the past,
possibly as a result of inefficiencies or challenges managing larger banks. This suggests
that larger banks benefit from economies of scale but face challenges that could impair
their performance.

42
ECG has a negative and significant effect on performance in the models M9 to M11.
This surprising result means that even though banks increase their exposures during
periods of high economic growth, those exposures do not translate at such times into
better performance. This may mean that economic growth would spur competition and
risk-taking, adversely impacting the bank's performance.

Bank Specialization (IDS) is also insignificant at the direct level in models M10 and
M11. Furthermore, the interaction terms IDS x GOV, IDS x TEC, and IDS x DAT,
representing the interactions between bank specialization and governance, technology,
and data and infrastructure, are statistically insignificant in model M11. This indicates
that the combined impact of the interaction variables on the bank's performance is
negligible.

In summary, the results suggest that technology and management efficiency are crucial
factors positively influencing bank performance. On the other hand, data investments,
as well as infrastructure investments, could negatively affect performance over the short
term because these would take some period to generate returns. Higher bank size and
capitalization generally adversely impact performance, and adverse impacts due to
economic growth indicate riskier behavior during growth phases. Governance only
impacts when other factors are controlled for, and the interaction terms indicate that the
joint effects of these variables are not statistically significant.

Table 7 presents the random effects (RE) estimates with interactions between economic
growth (ECG) and AI dimensions (DAT, TEC, GOV) for models M1 to M3. These
results are helpful in comprehending the drivers of bank performance.

Capitalization (L.CAP) has a negative and substantial influence on bank performance

in all specifications. This suggests that higher capitalization during the previous period
is associated with worse performance today, either due to conservative behavior or
declining returns on additional capital. Asset Quality (L.ASQ) has little influence on
performance in all the models, so asset quality from the previous period is not a
significant direct influence on the current period’s performance.

43
Table 7: Regression outputs - Interactions with ECG
M1 M2 M3
L.CAP -0.04** -0.04** -0.03**
(0.01) (0.01) (0.01)
L.ASQ -0.01 -0.01 -0.01
(0.01) (0.01) (0.01)
L.EFF 48.84** 51.72** 50.99**
(14.20) (14.27) (14.24)
L.SIZ -0.67** -0.84** -0.87**
(0.26) (0.26) (0.26)
IDS 0.10 0.01 0.04
(0.50) (0.49) (0.50)
ECG -0.37** -0.21* -0.10
(0.11) (0.13) (0.07)
DAT TEC GOV
AI Dimension -0.03** 0.01 0.01
(0.02) (0.02) (0.01)
ECG x AI Dimension 0.01** 0.01 0.01
(0.00) (0.00) (0.00)
Constant 9.84** 9.46** 9.25**
(2.53) (2.55) (2.55)
Observations 343 343 343
Notes: GOV stands for Government pillar, TEC– Technology pillar, DAT – Data and infrastructure pillar,
CAP – capitalization, ASQ – asset quality, EFF – management efficiency, SIZ – bank size, ECG –
economic growth, and IDS – bank specialization dummy. Standard errors in parentheses. * p < 0.10, ** p
< 0.05. Source: Author’s own

Management Efficiency (L.EFF) is an important variable, with consistently positive

and highly significant coefficients in all models. This implies that higher efficiency in
the previous period strongly predicts better current performance, underlining the
importance of good management practices in influencing bank performance. Bank Size
(L.SIZ) is negatively and significantly related to performance in every model. Larger
bank sizes in the previous period were associated with lower current performance,
possibly due to inefficiencies or challenges in managing larger banks. This suggests
that while larger banks will have economies of scale, these also come with complexities
that are likely to impair performance. Bank Specialization (IDS) does not exert a
significant direct impact on performance in either of the models, indicating that there
is no significant effect of specialization on bank performance.

Economic Growth (ECG) is negatively and significantly correlated with performance

in model M1, implying that greater economic growth is accompanied by poorer current
performance. This intuitive result may be an indication of the fact that when there is
high economic growth, banks are involved in riskier projects that do not necessarily
reflect in better performance earlier on. The role of economic growth becomes less

44
significant in models M2 and M3, implying that its effect is being controlled by other
variables.

Data and Infrastructure (DAT) shows a negative and significant influence on

performance in model M1, suggesting that higher data and infrastructure scores are
associated with decreasing performance. It may suggest that expenditures on
infrastructure and data will have upfront costs that will dampen performance before
eventually yielding positive benefits. Meanwhile, Technology (TEC) does not have a
strong impact on performance in model M2, indicating that technology scores do not
directly have a major contribution to bank performance in this model. Next,
Governance (GOV) does not have a strong impact on performance in model M3,
indicating that governance scores do not directly have a major contribution to bank
performance in this model.

In model M1, the coefficient on economic growth and data and infrastructure (ECG x
DAT) is positive and significant, indicating that some moderation to the adverse impact
of economic growth on performance through greater scores in data and infrastructure-
explain that improved data and infrastructure can enable banks to counter the challenges
of economic growth more effectively.

The interactions between economic growth and technology (ECG x TEC) and
economic growth and governance (ECG x GOV) are positive but insignificant in
models M2 and M3, respectively. This suggests that while these interactions have a
positive influence, they do not have a substantial impact on performance.

In general, the results show that management efficiency is a significant variable that
positively affects bank performance while larger bank size and higher capitalization
negatively affect it. The negative consequence of economic growth may be represented
by riskier behavior in growth but is mitigated by better data and infrastructure. The
technology-growth and government-growth interactions are not statistically significant
and suggest that the combined effects of the two are weak. Finally, the findings
highlight the importance of good management practices and the potential moderating
influence of infrastructure and information in overcoming challenges presented by
economic growth.

45
 (a) ECG on ROA at levels of SC (b) ECG on ROA at levels of DAT

(c) ECG on ROA at levels of TEC (d) ECG on ROA at levels of GOV

Figure 1: Marginal Effects

Source: Author’s own

As indicated by margin plots in Figure 1, the negative effect of ECG on ROA is

mitigated as AI Readiness, Data and Infrastructure, Technology, and Governance
improve. This effect is observed as AI Readiness reaches level 60; Data and
Infrastructure goes to level 65; the Technology factor improves to level 40 but loses its
power afterward; and as the Governance component reaches level 70.

4.3. Discussion of Results

This study investigates the relationship between government AI readiness and banking
performance in the MENA region, focusing on 167 banks (51 Islamic, 116
conventional) across 14 countries from 2020-2023.

The findings indicate a negative relationship between AI readiness and bank

profitability (ROA) in the MENA region. This suggests that, at least initially, greater
AI readiness in a country does not directly translate to higher bank profits, answering
the first research question and refusing the first hypothesis that AI readiness has a
positive impact on banking profitability.

46
AI readiness moderates the relationship between economic growth and bank
profitability. While economic growth might negatively affect ROA, higher AI readiness
can reduce this negative impact, answering the second research question and accepting
the second hypothesis that AI readiness has a positive impact on economic growth-bank
profitability links in the MENA region.

The study finds no significant difference in the behavior of Islamic and conventional
banks concerning AI readiness and its impact on profitability.

Hence, the study implications for the banking sector are:

1. Strategic AI Deployment and Infrastructure Investment. Negative initial
correlations imply short-term pressures on profitability for banks due to the massive
up-front investments required for AI adoption and increased competition from
NBFIs. Key strategic implications are:
• Phased AI Implementation: A gradual rollout of AI solutions in high-impact
domains such as operational efficiency, fraud prevention, and customer care
would alleviate the short-term ROA drag. This approach would allow banks to
take on costs gradually and demonstrate early success stories.
• Strategic Partnerships: Financial collaboration with FinTech firms and
technology providers can decouple the cost burden and accelerate the
development and implementation of AI solutions tailored to banking needs.
Strategic partnerships will also bring specialized expertise and innovative
technologies within reach.
• Data Governance and Infrastructure: Strategic investment in robust data
governance frameworks and the upgrading of legacy IT is critical to enable AI
technologies to their full potential. High-quality data and scalable infrastructure
are essential for proper model training, consistent performance, and regulatory
compliance.
2. Regulatory and Policy Implications. The research recommendations highlight
policymakers creating an innovation-led and supportive regulatory environment
favorable to AI adoption in banking, along with the resolution of risks and ethics.
Key regulatory implications are:

47
 • Incentives for AI Investment: Governments can encourage AI adoption by
providing tax exemptions, grants, or subsidies as incentives to banks to spend
on AI infrastructure and technology.
• Regulatory Sandboxes: Establishing regulatory sandboxes allows banks to pilot
AI-driven products and services in a sandboxed environment, promoting
innovation without regulatory risks.
• Data Privacy and Security: Strong data privacy and security regulations are
essential to build trust and ensure the proper use of AI in banking. Clear
guidelines on data collection, storage, and usage can enhance consumer
confidence and promote sustainable adoption of AI.
3. Economic Growth and AI Readiness: The study sets the moderating role of AI
readiness for the relationship between economic growth and bank profitability. This
suggests that AI readiness can buffer the negative impact of economic decline on
bank performance. Economic policy implications are:
• Investment in Digital Infrastructure: Governments must invest in digital
infrastructure, i.e., high-speed internet and cloud computing, to enhance AI
readiness and complement economic growth.
• Education and Training: Investing in education and training programs to
establish a skilled workforce that can deploy and run AI technologies to achieve
the maximum economic potential of AI adoption is essential.
• Fostering Innovation Ecosystems: Fostering innovation ecosystems to drive
collaboration between banks, FinTech firms, research institutions, and
government agencies can accelerate AI adoption and drive economic growth.
4. Financial Stability and Risk Management: The application of AI in banking has
implications for risk management and financial stability. Banks must handle the
risks of AI, including model risk, data bias, and cybersecurity threats, with care.
The most significant risk management implications are:
• Model Validation and Monitoring: Having effective model validation and
monitoring processes in place to ensure the accuracy, reliability, and fairness of
AI models is essential to managing model risk.
• Data Governance and Ethics: Building strong data governance processes and
ethical guidelines to reduce data bias and ensure the effective use of AI is
required to maintain public trust and underpin financial stability.

48
 • Cybersecurity and Data Protection: Investing in cybersecurity technologies and
data protection measures to protect against cyber-attacks and data breaches is
essential to safeguard sensitive customer data and the integrity of the financial
system.

Key implications for the Islamic banking sector are as follows:

1. Policy Design and Regulatory Frameworks. The findings underscore the
significance of targeted AI policy interventions in MENA countries. While the
negative correlation between AI readiness and short-term profitability suggests that
upfront costs of implementation outweigh benefits, policymakers must render
strategies more advanced to align AI adoption with Maqasid al-Shari’ah (purposes
of Islamic law), e.g., promoting economic justice ('Adl) and public welfare
(Maslaha). Hence, it requires the following:
• Subsidized AI Infrastructure: Governments can subsidize the cost of AI
adoption for Islamic banks, particularly those adhering to the principles of
ethical finance, to ease the short-term pressure on profitability.
• Cross-Sector Collaboration: Fostering cooperation between Islamic financial
institutions and tech firms to co-create AI solutions specifically designed to
address distinctive challenges such as Shari’ah auditing and Riba-free risk
modeling.
2. Strategic Reorientation for Islamic Banks. The results that no significant distinction
exists between AI responsiveness between Islamic and traditional banks is a vital
note: AI implementation cuts across bank models. Islamic banks, however, must
strategically introduce AI while upholding their ethical mandate:
• Ethics-Efficiency Trade-off: Identify AI applications promoting heightened
transparency (e.g., explainable AI to Shari’ah governing boards) and client
trust, e.g., Zakat calculators based on AI or anti-fraud software blended with
Gharar (legal uncertainty) avoidance.
• Cost-Benefit Repurposed: Phased implementation of AI disruptive fields like
operations automation and credit risk analysis for Qard al-Hasan.
• Development of Talent: Invest in dual-skilled human capital—AI experts
trained in the principles of Islamic finance—to bridge the difference between
technology aptitude and moral compliance.

49
3. Investor Aspects of Ethical Finance. The moderating role of AI preparedness in the
economic growth-profitability link offers actionable implications for investors:
• ESG-Integrated AI Metrics: Incorporate AI readiness in ESG (Environmental,
Social, Governance) screening for Islamic investments, preferably tools that
reduce maysir (gambling-like speculation) in algorithmic trading or increase
financial inclusion.

50
CHAPTER 5: CONCLUDING REMARKS

The current research examines the impact of AI readiness, quantified by the

Government AI Readiness Index, on banking profitability in the MENA region, using
panel data analysis for 2020 to 2023. Addressing the main research question—to what
degree does AI readiness affect banking profitability in the MENA region—the
empirical evidence identifies a statistically significant negative relationship between
government AI readiness and banking profitability. This means that, at least in the short
term, investments in AI readiness may not necessarily show up in the form of increased
bank profitability. There may be several reasons for this unexpected outcome.
Tarawneh et al. (2024) suggest that this relationship may be attributed to increased
competition from NBFIs and may be several reasons, like the high cost of implementing
AI technologies. The findings also answer the second question about the moderation
rule of AI readiness in an economic growth-profitability relationship and find that a
higher level of AI readiness reduces the negative relationship between economic
growth and profitability.

The findings highlight the complexities of embracing AI in the banking sector,

particularly in the MENA region. Addressing the second research question, the study
also shows the indirect effect of AI readiness on bank profitability via economic
growth. While individually economic growth has a negative effect on ROA, potentially
due to increased competition, greater risk-taking, or other forces that deplete
profitability while the economy expands, and better AI readiness reduces this negative
impact. While short-term impacts may be negative, well-educated and strategic
investments in AI readiness remain critical to the long-term competitiveness and
efficiency of MENA banks. There is a need for further studies to thoroughly learn the
adoption dynamics of AI and determine the most effective ways of harvesting its
dividends. The "take-home message" is that AI readiness is a determining factor that
influences the profitability of the banking sector in the MENA region, and it needs to
be taken seriously.

The implications of this work are multiple. The MENA banking sector must adopt AI
on a phased basis with high-impact projects in operational effectiveness, anti-fraud, and

51
customer service to enable the prevention of short-term profitability pressures. Strategic
partnerships with FinTech firms and technology vendors can apportion costs and
facilitate quicker development of bespoke AI solutions. At the same time, robust data
governance frameworks and improved IT infrastructure are necessary to facilitate data
quality, model training, and regulation compliance. Policymakers need to craft a
facilitatory regulatory environment through incentives like tax relief, grants, and
regulatory sandboxes, along with data privacy and security, to foster trust. Digital
infrastructure and employee skills training investment are necessary to enhance AI
readiness and economic growth, as well as to collaborate between banks, FinTech, and
government entities to spur innovation. Efficient risk management through examining
models, minimizing data bias, and upholding cybersecurity is necessary to preserve
financial stability and maintain people's confidence.

In Islamic banks, the utilization of AI must be harmonized with Maqasid al-Shari’ah,

which prioritizes economic justice and the public good. The government can subsidize
AI implementation, while partnerships with tech firms can offer solutions for specific
challenges like Shari’ah auditing and Riba-free risk modeling. Islamic banks must
reconcile ethics and efficiency through the implementation of AI applications that
enhance transparency and client trust, such as explainable AI for Shari’ah governing
boards or Zakat calculators. Gradual implementation in operational automation and
Qard al-Hasan credit risk analysis optimizes resource utilization. Investment in double-
competent professionals—AI experts with Islamic finance education—bridges the gap
between technological capability and ethical compliance. As an investor, AI readiness
can be a prime indicator of economic resilience, with ESG screening focusing on
instruments that maximize financial inclusion. Future studies must examine why
readiness for AI does not impact Islamic banks and sector-related differently and study
how we can use AI to enhance efficiency in Waqf management or Sukuk issuing.

This paper contributes to the Islamic finance literature by proposing a framework to

evaluate AI maturity through an Islamic lens, emphasizing governance (hisbah), data
ethics (Amanah), and equitable access (Ihsan).

Despite its potential contribution to the body of knowledge, this study has some
limitations. The comparatively brief period of the analysis (2020-2023) cannot grasp

52
the long-term impacts of AI readiness for banking performance. Future research may
extend the analysis period to more years and add other dimensions better to understand
the linkage between AI readiness and banking profitability. Moreover, qualitative
research would provide valuable in-depth insights on the threats and opportunities for
banks in the MENA region when adopting AI technologies.

53
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APPENDIX: LIST OF DUAL BANKING COUNTRIES
Table A: List of sample countries with Islamic banks (IB) and conventional banks (CB)
Country CB IB Total
Bahrain 7 8 15
Egypt 12 1 13
Iraq 12 12 24
Jordan 9 3 12
Kuwait 9 5 14
Lebanon 10 1 11
Libya 5 1 6
Oman 4 1 5
Qatar 4 3 7
Saudi Arabia 7 4 11
Syria 11 2 13
Tunisia 11 2 13
UAE 12 5 17
Yemen 3 3 6
Grand Total 116 51 167

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