Adoption of artificial intelligence and machine learning in banking systems: a qualitative survey of board of directors

Abstract

The aim of the paper is twofold. First to examine the role of the board of directors in facilitating the adoption of AI and ML in Saudi Arabian banking sector. Second, to explore the effectiveness of artificial intelligence and machine learning in protection of Saudi Arabian banking sector from cyberattacks. A qualitative research approach was applied using in-depth interviews with 17 board of directors from prominent Saudi Arabian banks. The present study highlights both the opportunities and challenges of integrating artificial intelligence and machine learning advanced technologies in this highly regulated industry. Findings reveal that advanced artificial intelligence and machine learning technologies offer substantial benefits, particularly in areas like threat detection, fraud prevention, and process automation, enabling banks to meet regulatory standards and mitigate cyber threats efficiently. However, the research also identifies significant barriers, including limited technological infrastructure, a lack of cohesive artificial intelligence strategies, and ethical concerns around data privacy and algorithmic bias. Interviewees emphasized the board of directors’ critical role in providing strategic direction, securing resources, and fostering partnerships with artificial intelligence technology providers. The study further highlights the importance of aligning artificial intelligence and machine learning initiatives with national development goals, such as Saudi Vision 2030, to ensure sustained growth and competitiveness. The findings from the present study offer valuable implications for policymakers in banking in navigating the complexities of artificial intelligence and machine learning adoption in financial services, particularly in emerging markets.

Full text

Frontiers in Artificial Intelligence 01 frontiersin.org
Adoption of artificial intelligence
and machine learning in banking
systems: a qualitative survey of
board of directors
AbdullahEskandarany *
College of Business, University of Jeddah, Jeddah, Saudi Arabia
The aim of the paper is twofold. First to examine the role of the board of directors
in facilitating the adoption of AI and ML in SaudiArabian banking sector. Second, to
explore the eectiveness of artificial intelligence and machine learning in protection
of SaudiArabian banking sector from cyberattacks. A qualitative research approach
was applied using in-depth interviews with 17 board of directors from prominent
SaudiArabian banks. The present study highlights both the opportunities and
challenges of integrating artificial intelligence and machine learning advanced
technologies in this highly regulated industry. Findings reveal that advanced artificial
intelligence and machine learning technologies oer substantial benefits, particularly
in areas like threat detection, fraud prevention, and process automation, enabling
banks to meet regulatory standards and mitigate cyber threats eciently. However,
the research also identifies significant barriers, including limited technological
infrastructure, a lack of cohesive artificial intelligence strategies, and ethical concerns
around data privacy and algorithmic bias. Interviewees emphasized the board of
directors’ critical role in providing strategic direction, securing resources, and
fostering partnerships with artificial intelligence technology providers. The study
further highlights the importance of aligning artificial intelligence and machine
learning initiatives with national development goals, such as Saudi Vision 2030,
to ensure sustained growth and competitiveness. The findings from the present
study oer valuable implications for policymakers in banking in navigating the
complexities of artificial intelligence and machine learning adoption in financial
services, particularly in emerging markets.
KEYWORDS
artificial intelligence, machine learning, stakeholder theory, board of directors,
banking sector, Saudi Arabia
1 Introduction
Over the past decade, the nancial service industry has witnessed a signicant advanced
technological transformation, particularly in adopting articial intelligence (AI) and
machine learning (ML; Hilb, 2020; Gonaygunta, 2023). ese advanced technologies have
revolutionized the industry, from enhancing strategic decision-making to strengthening
cybersecurity measures (Johri and Kumar, 2023). On the one hand, AI and ML advanced
technologies have facilitated and promoted positive trends, such as the digital transformation
of business, the development of real-time threat detection, predictive analytics, automated
incident response, advanced fraud detection, continuous learning, and phishing, social
engineering defense, etc. (Kuzior etal., 2022; AL-Dosari etal., 2024). Prior studies reported
that the expressive advancement of AI and ML technologies oers unprecedented
opportunities for eciency, innovation, and competitive advantages, particularly in
OPEN ACCESS
EDITED BY
Alessia Paccagnini,
University College Dublin, Ireland
REVIEWED BY
Luisa Varriale,
University of Naples Parthenope, Italy
Ahmed Mohamed Habib,
Independent Researcher, Zagazig, Egypt
*CORRESPONDENCE
Abdullah Eskandarany
aeskandarany@uj.edu.sa
RECEIVED 28 May 2024
ACCEPTED 11 November 2024
PUBLISHED 27 November 2024
CITATION
Eskandarany A (2024) Adoption of artificial
intelligence and machine learning in banking
systems: a qualitative survey of board of
directors.
Front. Artif. Intell. 7:1440051.
doi: 10.3389/frai.2024.1440051
COPYRIGHT
© 2024 Eskandarany. This is an open-access
article distributed under the terms of the
Creative Commons Attribution License
(CC BY). The use, distribution or reproduction
in other forums is permitted, provided the
original author(s) and the copyright owner(s)
are credited and that the original publication
in this journal is cited, in accordance with
accepted academic practice. No use,
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which does not comply with these terms.
TYPE Brief Research Report
PUBLISHED 27 November 2024
DOI 10.3389/frai.2024.1440051

Eskandarany 10.3389/frai.2024.1440051
Frontiers in Artificial Intelligence 02 frontiersin.org
cybersecurity, while performing nancial transactions (Geluvaraj
etal., 2019; Narsimha etal., 2022). On the other hand, this shi to
advanced technologies of AI and ML has also led to negative
consequences, such as the rise of cybercrime facilitated by increased
digital literacy and the decreasing cost of the technology needed to
commit such crimes. For instance, malicious soware can
bepurchased on the dark web for as little as USD 1, while personal
data can beobtained for just USD 3 (Kuzior etal., 2022). is makes
it alarming for anyone to become a cybercriminal or gain access to
sensitive data at minimal cost. Amid the broader global cybercrime
data, the nancial service industry has seen a signicant surge in
attempted cyberattacks, including phishing attacks, malware,
distributed denial of service (DDoS) attacks, data breaches, account
takeover fraud, mobile banking fraud, and crypto jacking (Ali etal.,
2024). ese cyber-threats resulted in a nancial loss of $110 million
(Grantthornton, 2022). e growing impact of cybercrime and
cyberfraud is reected in statistics showing a signicant increase in
its adverse eects in recent years. In 2023, the nancial service
industry experienced over 20,000 cyberattacks worldwide, resulting
in cumulative losses of USD 2.5 billion and over 12 billion losses
over the past two decades (Statista, 2024). Notably, only the
SaudiArabian nancial service industry lost USD 110 million in
2022, the highest among the Gulf Cooperation Council (GCC)
countries (Grantthornton, 2022). Overall, the nancial service
industry stands out for its economic losses from cyber incidents and
the sheer volume of attacks it endures, accounting for 22.4% of all
attacks. Specically, 70% of these attacks target banks, 16% focus on
insurance companies, and 14% aect other nancial institutions
(IBM Security, 2022). In addition, Gulyás and Kiss (2023) reported
that “over 82% of the nancial institutions believe that the
cybercriminals have become more sophisticated; malware is used in
longer and more complex campaigns” (p.86).
However, to encounter cyberattacks in nancial institutions, the
Saudi Central Bank (SAMA) launched “Project Aber” to utilize AI and
ML advanced technologies to create a central bank digital currency
that could be used for the settlement of cross-border payment
obligations between commercial banks (SAMA, 2020). In addition,
SAMA also proposed and implemented a framework following the
four main domains, namely: (i) “Cyber Security Leadership and
Governance,” (ii) “Cyber Security Risk Management and Compliance,”
(iii) “Cyber Security Operations and Technology,” and (iv) “ird
Party Cyber Security” aims to control cyberattacks in nancial service
industry mainly the commercial banks (SAMA, 2023). Despite these
initiatives, SaudiArabia’s commercial banks face numerous challenges
in fully integrating AI and ML technologies into their operations. In
this regard, Miller (2022) outlined that the banks struggle with
adopting these technologies to enhance cybersecurity due to
regulatory challenges, a lack of technical expertise, and the need for
strategic direction. Gonaygunta (2023) highlighted that integrating AI
and ML into the banking sector to deal with complex problems that
impose signicant strategic directions, where the role of the Board of
Directors (BoDs) becomes essential. In the banking sector, the BoDs
are accountable for empowering institutions to navigate the
complexities of AI and ML adoption ethically and strategically (Vac a
etal., 2022).
erefore, this study aims to examine the critical role of the BoDs
in facilitating the adoption of advanced AI and ML technologies in
Saudi Arabia’s commercial banking sector. e study seeks to
understand how the BoDs can steer the adoption of these advanced
technologies to enhance cybersecurity measures, mitigate risks, and
ensure regulatory compliance. By focusing on Saudi Arabia, this
research addresses a pressing need for guidance on AI and ML
adoption in a region highly targeted by cybercriminals and undergoing
rapid digital transformation.
However, the relevance of this study is twofold. First, AI and
ML are not just tools for operational eciency; they are
increasingly integral to maintaining cybersecurity in the nancial
service industry. Saudi Arabian banks, highly exposed to
cyberattacks, must prioritize these technologies to avoid
sophisticated threats. Second, the role of the BoDs is crucial in
navigating the complex regulatory and ethical challenges of AI and
ML integration. Previous studies have noted the importance of the
BoDs in setting strategic directions, fostering innovation, and
ensuring that AI and ML are adopted ethically and eectively
(Hilb, 2020; Anh, 2021). is study lls this gap in the literature by
examining the BoDs’ role in this critical area of AI and ML,
particularly in Saudi Arabia’s unique regulatory and
business environment.
eoretically, this research draws on stakeholder theory to
examine the role of the BoDs in adopting advanced AI and ML
technologies in SaudiArabian banks. Stakeholder theory emphasizes
the importance of aligning corporate governance practices with the
interests of all stakeholders, including policymakers and shareholders
(Freeman, 1984). In AI and ML adoption, the BoDs must balance
innovation with ethical considerations, such as data privacy,
algorithmic transparency, and bias mitigation (Vaca etal., 2022). By
employing stakeholder theory, this study provides insights into how
SaudiArabian banks can leverage advanced AI and ML technologies
to meet stakeholder expectations, enhance customer satisfaction, and
reduce the risk of cyberattacks. Given the evolving cybersecurity
landscape and the central role of the BoDs in steering technological
adoption, this study aims to investigate the following
research questions.
RQ1. What is the role of the BoDs in facilitating the adoption of
AI and ML in SaudiArabian banking sector?
RQ2. How can AI and ML eectively protect Saudi Arabian
banking sector from cyberattacks?
e abovementioned research questions were addressed through
a qualitative study using in-depth interviews with BoDs of
Saudi Arabian banks. e research seeks to understand their
perspectives on the strategic importance of AI and ML advanced
technologies, the challenges faced in their implementation, and the
regulatory frameworks that shape their adoption. Overall, this study
contributes to the literature on AI and ML in the nancial sector by
providing empirical insights into the role of the BoDs in their
adoption. It oers practical guidance for practitioners and
policymakers in the banking industry on navigating the complexities
of AI and ML integration. Specically, it addresses the need for
strategic leadership in AI and ML adoption, especially in the context
of increasing cybersecurity threats. is study is particularly relevant
for SaudiArabian banks, as it oers actionable insights into how they
can harness AI and ML technologies to stay competitive, enhance
security, and ensure regulatory compliance.

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is paper is structured as follows. Section 2 presents the literature
review and theoretical background. Research methods and results are
presented in Sections 3 and 4, respectively. Sections 5 and 6 discuss
the results, limitations, and future research suggestions.
2 Literature review
2.1 Artificial intelligence in the banking
sector
Cyberattacks have escalated in frequency, severity, and
sophistication (Gilad and Tishler, 2023); among these, the banking
sector has emerged as a primary targeted industry (Perera etal., 2022).
Noticeably, AI has been categorized as a signicant tool in the banking
sector that supports identifying and avoiding cyberattacks (Narsimha
etal., 2022). Banks have increasingly invested in AI-powered services,
such as chatbots and nancial management tools, to enhance
operational eciency and protection. Furthermore, adopting AI to
combat cyber threats has gained traction within the industry
(Geluvaraj etal., 2019). erefore, recent empirical studies on the eld
are presented in Table1.
Nevertheless, leveraging AI in critical infrastructures, the banking
industry presents several challenges, i.e., safety, accuracy,
trustworthiness, and security systems to adopt AI tools. Consequently,
deploying an eective cyber defense system becomes paramount in
bolstering customer trust and ensuring the seamless delivery of
banking services (Englisch etal., 2023). Nonetheless, various security
concerns, challenges, vulnerabilities, and risks continue to emerge,
including the deliberate exploitation of AI technology through
cyberattacks, which could lead to substantial destruction or even
fatalities (Nicholls et al., 2021). us, the various processes and
services of AI tools in the banking sector are presented in Table2.
However, the role of the board of directors in adopting AI tools in
the banking sector is critical for ensuring the successful integration
and utilization of these advanced technologies. e board of directors
is responsible for setting the strategic direction and providing
oversight, including fostering innovations through adopting AI tools
(Vaca etal., 2022). ey must understand the potential benets of AI,
such as enhanced eciency, improved customer service, and better
risk management assessment, and incorporate these into the bank’s
strategic goals. Board governors play a crucial role in resource
allocation, ensuring adequate investment in AI technologies and
related infrastructure (Cerchiello etal., 2022). e board of directors
must also provide the bank with the necessary talent and expertise to
implement and manage AI systems eectively. is involves
supporting training and development initiatives and attracting skilled
data scientists and AI professionals.
2.2 Machine learning in the banking sector
ML has emerged as a transformative technology in the banking
sector, oering unprecedented opportunities for enhancing various
operations and services (Leo etal., 2019; Habib, 2024). ML algorithms,
powered by vast amounts of data, enable banks to extract valuable
insights, automate processes, and improve decision-making
capabilities. However, ML is extensively utilized by nancial
institutions to improve various operations, enhance decision-making,
and provide better customer service (Polireddi, 2024). ML models
have the potential to accurately predict credit defaults by analyzing
historical data on customer behavior, transaction patterns, and
creditworthiness and assess the risk associated with lending to
individual customers or businesses (Patel and Trivedi, 2020). is
allows banks to make more informed decisions when granting loans
and setting interest rates, ultimately reducing the risk of default and
improving overall portfolio performance (Kuzior etal., 2022).
Additionally, fraud detection in the banking sector is another
critical area that ML substantially impacts (Donepudi, 2017). ML
algorithms, however, can analyze vast volumes of transaction data in
real-time, identifying patterns and anomalies indicative of fraudulent
activity. By continuously learning from new data, ML models can
adapt to emerging fraud schemes and enhance the eectiveness of
fraud prevention measures (Chen etal., 2020). erefore, the critical
areas of nancial institutions where ML could beimplemented are
presented in Table3.
However, chatbots and virtual assistants powered by natural
language processing (NLP) and ML algorithms enable banks to
provide personalized and interactive customer support around the
clock (Patel and Trivedi, 2020). ese AI-powered assistants have the
potential to handle routine inquiries, process transactions, and even
oer nancial advice based on individual preferences and behavior,
enhancing the overall customer experience (Rabbani etal., 2023).
3 Theoretical background
AI and ML have signicantly transformed the banking sector by
enabling automated processes, enhancing customer experiences, and
improving cybersecurity. e adoption of AI and ML oers both
positive and negative eects. On the positive side, AI and ML enhance
operational eciency, support risk management, and improve
decision-making through predictive analytics (Narsimha etal., 2022).
However, the adverse eects include concerns about data privacy,
algorithmic bias, and increased vulnerability to sophisticated
cyberattacks if AI systems are compromised (Habib, 2024).
AI applications such as chatbots, fraud detection systems, and
automated customer support tools have been integrated into banks to
improve service quality and security. For instance, banks use ML
algorithms to monitor transaction patterns, detect anomalies, and
prevent fraudulent activities (Gonaygunta, 2023). However, these
technologies also come with risks, including potential misuse of
personal data and the challenges of interpreting AI decisions, which
may lack transparency (Al-Nasser Mohammed and
Muhammed, 2017).
To obtain the objectives of the present study, the stakeholder
theory serves as the theoretical framework for understanding the roles
of internal stakeholders in AI and ML adoption. Stakeholder theory,
introduced by Freeman (1984), emphasizes that organizations should
consider the interests of all parties aected by their decisions. For the
banking sector, these internal stakeholders include Members of the
Board of Directors (BoD), who are responsible for setting strategic
goals and ensuring that AI and ML are adopted ethically and
eectively. e BoD, as internal stakeholders, are critical in ensuring

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Frontiers in Artificial Intelligence 04 frontiersin.org
that AI and ML are implemented in a manner that aligns with
regulatory standards, protects customers, and promotes transparency
(Güngör, 2020). e focus on the BoD’s role in AI and ML adoption
explores their strategic decision-making in enhancing cybersecurity
while managing risks such as data breaches and algorithmic biases
(Vaca etal., 2022).
TABLE1 Relevant empirical studies.
Author (s) Objectives Method Findings
Alraddadi (2023) e study aimed to develop an abstraction framework to
manage and control cybersecurity threats within Saudi banks,
based on the National Institute of Standards and Technology
(NIST) Cybersecurity Framework and the ISO/IEC 27001
standards.
Mixed method e study found that Saudi banks generally have robust
cybersecurity measures, but there were gaps in their
alignment with international standards. e developed
framework helped streamline processes, making them
more ecient and cohesive.
Englisch etal. (2023) e study explored how deep learning can beapplied to
treasury management in banks, improving decision-making
and nancial forecasting.
Quantitative e study concluded that deep learning models
signicantly enhance the accuracy and eciency of
treasury management.
Gilad and Tishler (2023) e study aimed to examine how the quality, covertness, and
intensity of the use of cyber weapons mitigate the risk of
advanced cyberattacks.
Quantitative e study found that high-quality and covert cyber
weapons are more eective in mitigating risks from
advanced cyberattacks.
Gonaygunta (2023) e study explored the factors inuencing the adoption of
machine learning algorithms to detect cyber threats in the
banking industry.
Qualitative e research revealed that critical factors inuencing ML
adoption in banks include technological infrastructure,
data availability, regulatory requirements, and
organizational readiness.
Gulyás and Kiss (2023) e study analyzed the impact of cyberattacks on nancial
institutions, mainly how they aect operational stability and
nancial performance.
Quantitative e study found that cyberattacks signicantly negatively
aect the nancial stability and reputation of institutions.
Johri and Kumar (2023) e study aimed to explore customer awareness regarding
cybersecurity in the Kingdom of SaudiArabia, particularly in
digital banking transformation.
Quantitative e study found that digital transformation in Saudi
banks progresses rapidly, but customer cybersecurity
awareness remains limited.
Narsimha etal. (2022) e study examined the role of articial intelligence (AI) and
machine learning (ML) in defending against nancial fraud in
the banking sector.
Qualitative e study concluded that AI and ML are critical for
detecting nancial fraud and providing faster, more
accurate identication of fraudulent activities.
Perera etal. (2022) is study investigated the factors contributing to reputational
damage in organizations following cyberattacks.
Quantitative e study identied key factors leading to reputational
damage, including negative media coverage and poor
crisis management.
Vaca etal. (2022) e study explored the use of deep learning in nance research,
particularly in analyzing the proles of board members in
nancial institutions.
Qualitative e study found that deep learning validly analyzes board
proles, oering predictive insights into governance
performance.
Cerchiello etal. (2022) e study aimed to assess bank distress by combining news
data with regular nancial data, utilizing articial intelligence
(AI) to improve prediction models for bank failures.
Qualitative e study found that incorporating news data alongside
nancial data signicantly improves the predictive power
of models assessing bank distress.
Anh (2021) e objective was to determine how governance structures
inuence the implementation and eectiveness of AI systems
in banks.
Quantitative e study found a positive association between strong
corporate governance practices and the successful
adoption of AI in the banking sector.
Nicholls etal. (2021) e study provided a comprehensive survey of how deep
learning approaches can combat nancial cybercrime in the
evolving landscape of nancial crime.
Quantitative e study found that deep learning models are highly
eective in identifying complex patterns of nancial
cybercrime, especially when traditional detection
methods fail.
Hilb (2020) e study explored the potential role of articial intelligence
(AI) in shaping the future of corporate governance, focusing on
how AI could inuence governance practices and decision-
making.
Qualitative e study concluded that AI has the potential to improve
governance by providing more data-driven insights for
decision-making, enhancing transparency, and
automating routine governance tasks.
Geluvaraj etal. (2019) e study examined the role of AI, machine learning (ML), and
deep learning in shaping the future of cybersecurity,
particularly in detecting and preventing cyberattacks.
Qualitative e study highlighted that AI and ML are crucial in
proactively defending against cyber threats, with deep
learning providing more advanced capabilities in
identifying emerging risks.

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Frontiers in Artificial Intelligence 05 frontiersin.org
4 Risks and criticisms of AI and ML in
banking
AI and ML applications in banking present risks such as
algorithmic errors, cyber threats, and ethical concerns. Algorithmic
decision-making may introduce biases that negatively aect
customers, particularly in loan approvals and fraud detection areas.
Furthermore, AI-driven systems can become targets for
cybercriminals, who may exploit vulnerabilities in the algorithms or
data infrastructure (Güngör, 2020). e nancial sector’s transition
toward AI and ML adoption in Saudi Arabia is complicated by
regulatory challenges, limited technological expertise, and the need
for robust governance frameworks. Stakeholder theory provides a
suitable lens to assess how the BoD, as key internal stakeholders,
manages these risks while navigating the technological transformation
(Laine etal., 2024). erefore, the present study focuses on AI and ML
applications such as fraud detection systems, automated customer
support, and risk management tools. ese applications illustrate the
growing role of AI and ML in transforming banking operations, but
they also require careful management to ensure security and trust in
nancial systems.
Noticeably, the concepts of stakeholder theory are increasingly
applied in countries like Germany, Sweden, and Japan, where boards
of directors have a signicant role in organizational operations. is
approach empowers all stakeholders to participate in the organization’s
current and future activities. In conclusion, stakeholder theory
provides a suitable framework for understanding the adoption of AI
and ML practices in the context of this study.
5 Method
is study adopts an exploratory qualitative research design to
examine the role of the BoD in AI and ML adoption within
SaudiArabian banks. Using qualitative methods, such as in-depth
interviews, allows for a detailed examination of the perceptions,
strategies, and challenges BoD members face in adopting AI and ML
for cybersecurity. One cybersecurity management professional and
three professional academic researchers have prepared and validated
the interview protocols. However, the experts recommended minor
modications to the interview protocol, and wemodied the items
accordingly. Overall, through the above process, weensure the validity
and reliability of the interview questions.
5.1 Data collection
Participants were recruited through email and face-to-face
communication, followed by a exible scheduling process to
accommodate their availability. Interview durations ranged from
TABLE2 Process and services of AI in the banking sector.
Artificial intelligence tolls Impact
Fraud Detection and Prevention Identies unusual transactions, prevents unauthorized access, and ags suspicious activities for further investigation.
Customer Service and Support Handles common customer inquiries, manages accounts, and provides personalized nancial advice.
Algorithmic Trading Credit, market, and operational risks are evaluated to help make informed decisions and regulatory compliance.
Anti-Money Laundering Monitors large volumes of transactions, identies suspicious activities, and reports them to relevant authorities.
Risk Management Enhances trading strategies, improves execution speed, and increases protability by identif ying market opportunities.
Credit Scoring and Loan Approval Automates the loan approval process, reduces biases in credit scoring, and speeds up decision-making.
Regulatory Compliance and Reporting Automates reporting monitors transactions for compliance, and ags potential regulatory breaches.
Predictive Analytics Forecasts market movements, customer needs, and nancial outcomes to guide strategic planning and decision-making.
Chatbots and Virtual Assistants Oers customer service, handles routine inquiries, and assists with transactions such as balance checks and fund transfers.
Sentiment Analysis Helps in understanding market trends and investor sentiment, inuencing trading strategies and investment decisions.
Compiled by author (2024).
TABLE3 Key areas of financial institutions.
Machine learning Application
Credit Scoring and Risk Assessment Automates the loan approval process, reduces biases in credit scoring, and speeds up decision-making.
Anti-Money Laundering (AML) Compliance Monitors large volumes of transactions, identies suspicious activities, and reports them to relevant authorities.
Fraud Detection and Prevention Identies unusual transactions, prevents unauthorized access, and ags suspicious activities for further investigation.
Market Sentiment Analysis Helps in understanding market trends and investor sentiment, inuencing trading strategies and investment decisions.
Predictive Analytics Forecasts market movements, customer needs, and nancial outcomes to guide strategic planning and decision-making.
Financial Forecasting Supports investment strategies, budget planning, and risk assessment by providing accurate nancial forecasts.
Loan Default Prediction Helps manage loan portfolios and mitigate risks by identifying high-risk borrowers.
Regulatory Compliance Automates reporting monitors transactions for compliance, and ags potential regulatory breaches.
Document and Data Processing Speeds up tasks such as KYC processes, loan applications, and compliance checks by digitizing and analyzing documents.
Compiled by author (2024).

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45 min to 1 h, and the interviews were conducted through Google
Meet for remote participants or in person. Interviews were recorded
with the participant’s consent, ensuring accurate transcription and
analysis. erefore, the interview protocol focused on three key areas
such as (i) the strategic importance of AI and ML in banking, (ii) the
BoD’s role in overseeing AI and ML adoption, and (ii) challenges in
implementing AI and ML to enhance cybersecurity. However,
participants were encouraged to interrupt the interviewer for
clarication and were assured that there were no right or wrong
answers. e interviewer fostered a relaxed, informal atmosphere to
promote open and honest discussion. Participants were informed that
their responses would be treated with condentiality and stored
securely on a password-protected computer. To maintain anonymity,
participant information was identied by a number rather than
their name.
5.2 Data analysis
In this study, weutilize thematic analysis, grounded in a realist
ontology, which assumes that language reects reality and focuses on
participants’ thoughts and statements. e approach to coding and
theme development follows a exible method, as Mahmood etal.
(2023) suggested. According to AL-Dosari etal. (2024), interview
questions are oen recommended to guide the development of
themes, a technique also applied in this study. e interview questions
were designed based on existing theories and relevant literature,
expecting that they would partly inform the signicant themes.
However, the coding and nal theme development were conducted
independently of the interview structure to minimize subjectivity bias
and ensure a more objective analysis.
e codes were developed inductively, meaning the data served as
the analysis’s starting point. Semantic coding was used to capture
explicit meanings in the data, limiting the inuence of researcher
subjectivity. e study was carried out in several stages, as outlined by
Terry et al. (2017): familiarizing with the data, generating codes,
constructing themes, reviewing potential themes, and dening nal
themes. e data organization was facilitated by NVIVO 12 soware,
which helped structure the codes. e study also followed the six-step
qualitative analysis framework by Clarke and Braun (2013),
provided below.
(i) Familiarization with the data: Immersing oneself in the dataset
through comprehensive review and multiple readings.
(ii) Coding: Creating concise labels (coding) to identify pertinent
data elements crucial for addressing research questions.
(iii) Generating initial themes: Using coded data to develop
preliminary themes, then gathering relevant information for
each potential theme to assess its viability.
(iv) Reviewing themes: Evaluating prospective themes against the
dataset, rening or discarding them based on their alignment
with the data and research objectives.
(v) Dening and naming themes: Scrutinizing each theme to
delineate its scope, purpose, and narrative, accompanied by
assigning descriptive labels to subtopics.
(vi) Writing up: Integrating analytical narratives with data excerpts
and contextualizing ndings within existing literature during
the interpretation phase.
5.3 Demographic characteristic
To obtain the objectives of the present study, 17 semi-structured
interviews were conducted with the BoDs from retail banks in
SaudiArabia. Demographic information of the respondents, including
gender, age, position, nationality, and region, was identied. Since the
personal information of the interviewees was condential, the names
were coded from “BoM1” to “BoM17.” “BoM” stands for Board of
Directors, and numbers refer to the order in which interviews were
conducted. erefore, the demographic characteristics of the
respondents are presented in Table4.
6 Results
is section presents the ndings from the qualitative interviews
with 17 BoDs in the banking sector of SaudiArabia. e interviews
focused on the role of BoDs in facilitating the adoption of AI and ML
in the SaudiArabian banking sector to strengthen cybersecurity and
prevent cyberattacks. e ndings are organized around two key
research questions (RQ1 and RQ2), with insights into how AI and ML
are implemented to address cybersecurity and fraud
detection challenges.
Findings on RQ1: RQ1. What is the role of the BoDs in facilitating
the adoption of AI and ML in SaudiArabian banking sector?
In exploring the rst theme of the present study, interviewees
highlighted the BoDs’ strategic role in driving the adoption of AI and
ML within SaudiArabian banks. e BoDs’ inuence was crucial in
setting the vision for digital transformation, allocating resources, and
addressing these advanced technologies’ regulatory and ethical
complexities. is theme centers on the BoDs’ capacity to champion
AI and ML adoption while balancing innovation with regulatory
compliance and moral integrity. However, several interviewees
emphasized that a clear vision from the BoDs is essential for
successfully adopting AI and ML in Saudi banks. One board member
pointed out that the BoDs’ primary responsibility is to set a strategic
agenda that aligns with SaudiArabia’s Vision 2030 goals, pushing for
digitalization and technological advancement across the
banking sector:
“Our role is to ensure that our bank’s strategic goals align with
Vision 2030. AI and ML are at the forefront of these goals, and the
board’s responsible for leading this transformation with clear
directives and well-dened objectives (BoD1).”
Another interviewee echoed the importance of the BoDs in
establishing a strategic roadmap for AI and ML, emphasizing that
without BoDs leadership, the integration of these technologies would
likely stall:
“If the board is not committed to an AI-driven strategy, progress
halts. Weprovide the direction, and our commitment trickles down
through the organization, encouraging buy-in from all levels
(BoD2).”
erefore, interviewees also discussed the BoDs’ role in securing
and allocating resources for AI and ML projects, which oen require
signicant nancial investments. Many Saudi banks face budgetary

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constraints, and prioritizing resources for AI and ML can
be challenging. One board member highlighted the BoDs’
responsibility to secure adequate funding:
“AI and ML adoption requires signicant upfront investment, not
just in technology but also in upskilling our workforce and
strengthening infrastructure. It’s the board’s job to ensure these
resources are available (BoD7).”
Another respondent (BoD8) noted that the BoDs must
beproactive in fostering partnerships with AI technology providers,
which can help oset some of the resource limitations and provide
access to cutting-edge tools and expertise:
“Collaborating with tech providers allows us to leverage their
expertise and access the latest AI solutions without exhausting our
resources. ese partnerships are something the board actively
pursues (BoD8).”
Additionally, the interviewees highlighted that AI and ML
implementation in Saudi banks has signicant regulatory and ethical
challenges. e BoDs’ role in navigating these issues was widely
recognized as critical. With stringent regulations, particularly
around data privacy and anti-money laundering (AML)
requirements, BoDs noted the importance of setting up robust
governance structures. One interviewee stressed this
regulatory aspect:
“In a highly regulated industry like banking, the board has to ensure
that any AI or ML initiative complies with legal requirements.
Wework closely with legal and compliance teams to ensure our tech
use does not expose us to regulatory risks (BoD11).”
e interviewees also mentioned the ethical implications of AI,
including concerns about bias and transparency. Board members see
themselves as gatekeepers who must oversee AI implementations that
uphold fairness and avoid discrimination:
“We cannot aord to have biased algorithms or opaque decision-
making processes. As board members, we are responsible for
ensuring our AI systems are transparent and fair, especially given
the sensitive nature of nancial data (BoD13).”
e BoDs’ role in fostering an organizational culture receptive to
AI and ML was also emphasized. Many interviewees noted that
adopting AI and ML requires a shi in mindset across all levels of the
organization, particularly given the conservative nature of the Saudi
banking sector. One respondent mentioned the importance of change
management and cultural adaptation led by the BoD:
“AI adoption is not just about technology; it’s a cultural shi. e
board must champion this change and create an environment
encouraging innovation and agility (BoD13).”
Another board member shared insights into how the BoDs’
support for training and upskilling initiatives is critical for long-
term success:
“Our workforce needs to beready for the AI era. e board actively
promotes training and development programs to help employees
adapt to these technologies, which is essential for sustainable
adoption (BoD15).”
However, some interviewees reected on the dierences
between the BoDs’ role in SaudiArabia and other countries, noting
TABLE4 Demographic characteristics of the board of members.
No Code Position Gender Qualification Nationality Classification Region Age
1 BoD1 Board of Directors Ma le Postgraduate Saudi Non-Executive East 45
2 BoD 2 Board of Directors Male Postgraduate Saudi Independent Wes t 44
3 BoD 3 Board of Directors Male Postgraduate Saudi Independent Middle 40
4 BoD 4 Board of Directors Male Postgraduate Saudi Independent East 45
5 BoD 5 Board of Directors Male Undergraduate Saudi Independent Middle 51
6 BoD 6 Board of Directors Male Undergraduate Saudi Independent East 43
7 BoD 7 Board of Directors Male Postgraduate Saudi Non-Executive Middle 52
8 BoD 8 Board of Directors Male Postgraduate Saudi Independent East 62
9 BoD 9 Board of Directors Male Undergraduate Saudi Independent We s t 50
10 BoD 10 Board of Directors Male Undergraduate Saudi Independent Middle 59
11 BoD 11 Board of Directors Male Postgraduate Saudi Independent We s t 64
12 BoD 12 Board of Directors Female Postgraduate Saudi Non-Executive Wes t 29
13 BoD 13 Board of Directors Male Postgraduate Saudi Executive We st 57
14 BoD 14 Board of Directors Male Undergraduate Saudi Non-Executive Middle 32
15 BoD 15 Board of Directors Female Postgraduate Saudi Executive West 63
16 BoD 16 Board of Directors Male Postgraduate Saudi Non-Executive Wes t 50
17 BoD 17 Board of Directors Male Undergraduate Saudi Executive West 51
Compiled by author (2024).

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that Saudi boards may face unique cultural and regulatory
challenges. In contrast, banks in countries like the UnitedStates and
Germany may experience fewer regulatory hurdles and a more
exible environment for AI and ML adoption. One board
member commented:
“In places like the U.S., boards have more room to experiment and
innovate with AI. e conservative banking culture and regulatory
caution in SaudiArabia can slow things down. But Vision 2030 is
helping us move in the right direction (BoD16).”
Findings on RQ2: RQ2. How can AI and ML eectively protect
SaudiArabian banking sector from cyberattacks?
e interviewees consistently emphasized that AI-based
solutions are widely adopted in SaudiArabian banks to combat cyber
threats, mainly distributed denial of service (DDoS) attacks. ese
solutions leverage deep learning and articial neural networks,
providing greater exibility and robustness than conventional
systems. AI’s role in cybersecurity within Saudi Arabian banks is
crucial, primarily due to its ability to automate security operations
and streamline processes like network trac monitoring, anomaly
detection, and malicious activity identication. One
participant noted:
“e ability of AI to scale our defenses against DDoS attacks is
unparalleled. We’ve integrated a genetic algorithm for trac
analysis, which allows us to adjust to uctuating demands and
threats much faster than before (BoD3).”
Another interviewee discussed how AI-based Optical Character
Recognition (OCR) systems enhance fraud detection and
compliance eorts:
“We’ve employed AI in our KYC procedures. It has
revolutionized how wehandle document scanning and verication,
reducing manual errors and speeding up compliance
tasks (BoD3).”
AI technologies have enabled banks to meet regulatory
frameworks, such as SAMA’s Counter-Fraud guidelines, which
require comprehensive governance, detection, and prevention
mechanisms. A signicant advantage noted by interviewees is AI’s
ability to mine large datasets in real-time, facilitating swi responses
to threats:
“AI’s ability to handle enormous volumes of transactional data
allows us to stay ahead of cybercriminals, who constantly evolve
their methods (BoD4).”
Furthermore, Machine learning (ML) has also emerged as a
critical tool in cybersecurity. The interviewees reported that
ML-based algorithms, such as deep learning and neural networks,
are highly effective in detecting complex threats like DDoS
attacks. ML models enable banks to analyze vast amounts of log
data, providing real-time insights and enhancing fraud
prevention capabilities beyond traditional systems. One
interviewee stated:
“With ML, wecan identify patterns that would beimpossible to
detect manually. is capability is invaluable in our ght against
fraud, especially in credit card transactions (BoD5).”
However, interviewees also highlighted challenges in
implementing ML-powered systems. A recurring issue was the
absence of a well-dened AI strategy and a lack of comprehensive
technological infrastructure within banks, leading to ineciencies in
fully utilizing ML’s potential:
“e biggest obstacle is our outdated tech foundation. Without a
modern infrastructure, we cannot fully capitalize on ML’s
capabilities (BoD6).”
Ethical concerns regarding ML were also discussed, particularly
around risks of data manipulation and unintended biases. One
interviewee mentioned the dangers posed by ethical hacking and
social engineering:
“Our biggest fear is that ML systems can be exploited by
cybercriminals, who could manipulate datasets to launch more
realistic and damaging attacks. We’ve had instances where ethical
hackers were able to bypass our systems (BoD9).”
Despite these challenges, the interviewees underscored ML’s
strength in providing real-time predictive analytics, especially in
fraud detection:
“e speed at which ML analyzes data is key. It allows us to predict
potential threats and respond before they cause serious damage. is
capability is transformative in credit card fraud detection (BoD11).”
In comparing AI and ML adoption in SaudiArabian banks to
international contexts, interviewees highlighted several cultural,
political, and regulatory dierences. One interviewee noted that the
relatively conservative banking culture in Saudi Arabia, combined
with stringent regulatory controls, has slowed AI and ML integration
compared to more liberal markets in Western Europe or
North America:
“We’re cautiously optimistic, but our pace of AI and ML adoption is
hindered by cultural factors and regulatory caution, especially
compared to the UnitedStates or Europe (BoD15).”
However, SaudiArabia’s Vision 2030 initiative is viewed as a
turning point. With the government’s focus on digital
transformation, SaudiArabian banks expect that the pace of AI and
ML integration will accelerate, supported by additional resources
and infrastructure:
“Vision 2030 is encouraging us to advance our digital capabilities.
Weanticipate that with governmental support, wecan bridge the
infrastructure gap and accelerate AI and ML adoption to meet
international standards (BoD13).”
ese insights underscore the inuence of external factors on AI
and ML adoption within Saudi Arabian banks, highlighting the

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challenges and opportunities associated with the country’s digital
transformation eorts.
7 Discussion
e ndings of this study underscore the critical role that articial
intelligence (AI) and machine learning (ML) play in enhancing
cybersecurity within Saudi Arabian banks. By investigating the
perceptions of the board of directors (BoD), this study highlights both
the opportunities and challenges associated with AI and ML adoption.
e results suggest that AI and ML are indispensable tools for modern
banking, oering advanced threat detection, fraud prevention, and
compliance with regulatory standards.
However, the study also points to signicant barriers, such as the
absence of a cohesive AI strategy, limitations in technological
infrastructure, and ethical concerns. Several SaudiArabian banks lack
a comprehensive AI strategy, which limits the full realization of these
technologies’ potential (Perera etal., 2022). e BoD’s involvement in
creating a clear, unied strategy for AI adoption is essential to
overcome this barrier. In this view, Tariq etal. (2021) highlighted that
the outdated infrastructure and insucient data foundations hinder
banks’ ability to utilize advanced AI and ML technologies fully. e
ndings indicate a need for signicant investments in technological
infrastructure, led by strategic guidance from the BoDs, to support
advanced AI and ML initiatives. Furthermore, AI and ML systems
carry algorithmic bias risks and cyber exploitation vulnerability,
necessitating stronger ethical oversight and legal frameworks (Gandhi
etal., 2024). e present study identied that the BoDs must ensure
that AI systems are transparent, fair, and compliant with regulations,
as ethical governance is crucial for sustaining public trust in AI-driven
banking solutions.
7.1 Practical implications
e practical implications of this study are substantial for both
banks and regulatory bodies like the Saudi Arabian Monetary
Authority (SAMA). AI and ML have emerged as vital technologies for
enhancing cybersecurity, with signicant fraud detection and
compliance applications. AI solutions like IBM AI and deep-learning
OCR tools can streamline compliance with tasks like Know Your
Customer (KYC) verication and fraud detection, providing scalable
solutions that align with regulatory demands (Polireddi, 2024). ese
insights suggest that banks need to integrate advanced AI and ML
tools to remain competitive in a rapidly evolving nancial landscape.
e ndings reinforce the need for SaudiArabian banks to partner
with third-party AI providers to access cutting-edge technologies and
mitigate internal limitations. Additionally, upskilling the workforce is
essential for eectively managing these complex AI systems (Rabbani
etal., 2023). e BoDs’ role in supporting workforce development
initiatives is crucial to ensure a well-prepared workforce that can adapt
to the technological advancements associated with AI and ML
adoption. For regulatory authorities like SAMA, this study highlights
the importance of developing a framework that guides ethical AI use
in the banking sector. SAMA’s regulatory oversight ensures that AI
adoption aligns with anti-money laundering (AML) and fraud
detection standards, fostering a more secure banking environment
(Gandhi etal., 2024).
7.2 Managerial implications
e results of this study oer several managerial insights for
banking executives, especially the BoDs, in overseeing AI and ML
integration. e BoDs’ proactive role in promoting digital
transformation and aligning AI strategies with broader organizational
goals is essential. is study also outlined the importance of
strategically diverse BoDs, which enhances decision-making in critical
areas such as cybersecurity and regulatory compliance (Vaca etal.,
2022). Such diversity is benecial for strategic planning and aligns
with the goals of Saudi Vision 2030, which promotes digital innovation
within the nancial sector (Alraddadi, 2023). Additionally, the BoDs
must address ethical considerations in AI applications, including
privacy concerns, algorithmic bias, and transparency. Managers must
establish robust ethical frameworks to prevent data misuse and ensure
fair and transparent decision-making processes (Rodrigues et al.,
2022). e BoDs’ inuence in promoting a culture of ethical AI
adoption can also guide organizations toward sustainable,
responsible innovation.
7.3 Theoretical implications
From a theoretical perspective, this study extends stakeholder
theory by illustrating the inuence of internal stakeholders (BoDs) on
AI and ML adoption in the banking sector. Consistent with Freeman’s
(1984) stakeholder theory, the ndings emphasize the need to address
the interests and responsibilities of all parties impacted by
technological advancements (Güngör, 2020; Rodrigues etal., 2022).
In AI and ML, adoption goes beyond operational eciency; it requires
a balance of ethical and regulatory considerations impacting
customers and regulatory authorities.
e BoDs’ role in setting a strategic vision for AI and ML adoption
aligns with previous research that stresses the importance of
governance structures in facilitating technology integration (Vac a
et al., 2022). e ndings suggest that diverse BoDs can provide
broader perspectives on risk management and innovation, which
enhances decision-making for AI-related initiatives. Furthermore, this
study highlights the ethical risks associated with AI, such as data
privacy and algorithmic bias, extending the discussion on the need for
robust governance frameworks to mitigate these risks (Perera
etal., 2022).
7.4 Limitations and future research agenda
is study is not without limitations. e present study is
instructive concerning the role of AI and ML in enhancing
cybersecurity within the banking sector in SaudiArabia, but there are
a few limitations. First, there might bebiases in the responses of expert
interviews and, therefore, in the qualitative data, sample sizes of
interviewees may limit the generalization of ndings within banking
in SaudiArabia. us, the other critical dimensions of technological

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innovation and risk management within the banking sector may
beshadowed if the focus is strictly on AI and ML applications in
banks’ cybersecurity systems. ese could be signicantly better
addressed in future research endeavors if a mixed-method approach
is adopted that taps into qualitative insights from expert interviews,
therefore informing a quantitative data analysis of cybersecurity
metrics and performance indicators. An eective mixed method,
hence, may combine qualitative insights from expert interviews with
quantitative data on cybersecurity metrics and performance indicators
on AI and ML technology by deepening our understanding of how
eective they can be in minimizing cyber threats and improving
overall cybersecurity postures in the banking industry of SaudiArabia.
Moreover, it will also be interesting to nd out whether potential
future research further expounds on how banks’ implementation of
AI and ML solutions in cybersecurity poses challenges and barriers to
issues that emanate from data privacy, regulatory compliance, and
resources. Realization of the diculties, therefore, would allow banks
to use AI technologies to maximize the return on improved
cybersecurity outcomes. Consequently, it would benecessary to have
long-term case histories to document the ever-evolving nature of
applications of AI and ML in the cybersecurity domain and appraise
their long-term eectiveness in quelling emerging security threats.
Within this line, longitudinal studies could also investigate the
scalability and sustainability of AI-powered solutions in cybersecurity,
including their attendant implications for the resilience of the
SaudiArabian banking sector against evolving cyber risks. Finally,
research should also identify possible synergies that can exist between
AI, ML, and other new technologies, such as blockchain and quantum
computing, in the pursuit of strengthened cybersecurity defenses and
enabled new, innovative risk management strategies. With this
all-inclusive approach to technological innovation and risk mitigation,
the SaudiArabian banking sector is bound to bethe new vanguard in
cybersecurity resilience, protecting continued trust and condence
from its customers and stakeholders within the digital era.
8 Conclusion
AI-powered solutions have enabled banks to streamline KYC
procedures and improve internal fraud detection mechanisms,
bolstering overall risk management capabilities. However, despite the
signicant benets oered by AI and ML technologies, banks in
SaudiArabia face several challenges in their implementation and
utilization. ese challenges include the lack of a well-dened AI and
ML strategy, inadequate technology infrastructure, and concerns
related to data privacy and ethical considerations. Moreover, the
reliance on third-party AI and ML solutions and the shortage of
skilled cybersecurity professionals poses additional obstacles to
eective cybersecurity management. Addressing these challenges will
becrucial for the SaudiArabian banking sector to harness the full
potential of AI and ML in bolstering cybersecurity defenses. is will
require concerted eorts from industry stakeholders and policymakers
to develop robust AI and ML governance frameworks, invest in
cybersecurity education and training programs, and foster
collaboration between banks and technology providers. Moreover, AI
and ML-powered security solutions can autonomously adapt and
evolve in response to cyber threats, continuously learning from past
incidents and rening their detection capabilities. By leveraging ML
models to analyze historical attack data and identify patterns, nancial
institutions can enhance their ability to detect and mitigate known
and unknown cyber threats, bolstering their overall resilience against
cyber-attacks. While AI and ML promise to strengthen cybersecurity
in the SaudiArabian banking sector, their successful implementation
hinges on overcoming various organizational, technical, and
regulatory challenges. By embracing a proactive and collaborative
approach to cybersecurity management, SaudiArabian banks can
strengthen their resilience against cyber threats and safeguard the
integrity and trust of the nancial system.
Data availability statement
e raw data supporting the conclusions of this article will
bemade available by the authors, without undue reservation.
Ethics statement
Ethical approval was not required for the studies involving
humans because the individuals from dierent nancial institutions
in Saudi Arabia accepted to be interviewed for research purposes. e
studies were conducted in accordance with the local legislation and
institutional requirements. e participants provided their written
informed consent to participate in this study.
Author contributions
AE: Conceptualization, Data curation, Formal analysis, Funding
acquisition, Investigation, Methodology, Project administration,
Resources, Soware, Supervision, Validation, Visualization, Writing
– original dra, Writing – review & editing.
Funding
e author(s) declare that no nancial support was received for
the research, authorship, and/or publication of this article.
Conflict of interest
e author declares that the research was conducted without any
commercial or nancial relationships that could be construed as a
potential conict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their aliated organizations,
or those of the publisher, the editors and the reviewers. Any product
that may beevaluated in this article, or claim that may bemade by its
manufacturer, is not guaranteed or endorsed by the publisher.

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