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Citation: Shiyyab, Fadi Shehab,
Abdallah Bader Alzoubi, Qais
Mohammad Obidat, and Hashem
Alshurafat. 2023. The Impact of
Artificial Intelligence Disclosure on
Financial Performance. International
Journal of Financial Studies 11: 115.
https://doi.org/10.3390/
ijfs11030115
Academic Editors: Albert Y.S. Lam
and Yanhui Geng
Received: 2 August 2023
Revised: 3 September 2023
Accepted: 6 September 2023
Published: 14 September 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
International Journal of
Financial Studies
Article
The Impact of Artificial Intelligence Disclosure on
Financial Performance
Fadi Shehab Shiyyab , Abdallah Bader Alzoubi, Qais Mohammad Obidat and Hashem Alshurafat *
Department of Accounting, Business School, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan;
fadi_shiyyab@hu.edu.jo (F.S.S.); abdallahb@hu.edu.jo (A.B.A.); qais.mf.91@gmail.com (Q.M.O.)
*Correspondence: hashema@hu.edu.jo
Abstract:
This study determines to what extent Jordanian banks refer to and use artificial intelligence
(AI) technologies in their operation process and examines the impact of AI-related terms disclosure on
financial performance. Content analysis is used to analyze the spread of AI and related information
in the annual report textual data. Based on content analysis and regression analysis of data from
115 annual reports for 15 Jordanian banks listed in the Amman Stock Exchange for the period 2014
to 2021, the study reveals a consistent increase in the mention of AI-related terms disclosure since
2014. However, the level of AI-related disclosure remains weak for some banks, suggesting that
Jordanian banks are still in the early stages of adopting and implementing AI technologies. The
results indicate that AI-related keywords disclosure has an influence on banks’ financial performance.
AI has a positive effect on accounting performance in terms of ROA and ROE and a negative impact
on total expenses, which supports the dominant view that AI improves revenue and reduces cost
and is also consistent with past literature findings. This study contributes to the growing body of AI
literature, specifically the literature on AI voluntary disclosure, in several aspects. First, it provides an
objective measure of the uses of AI by formulating an AI disclosure index that captures the status of
AI adoption in practice. Second, it provides insights into the relationship between AI disclosure and
financial performance. Third, it supports policymakers’, international authorities’, and supervisory
organizations’ efforts to address AI disclosure issues and highlights the need for disclosure guidance
requirements. Finally, it provides a contribution to banking sector practitioners who are transforming
their operations using AI mechanisms and supports the need for more AI disclosure and informed
decision making in a manner that aligns with the objectives of financial institutions.
Keywords:
artificial intelligence; voluntary disclosure; content analysis; financial performance;
banking sector; Jordanian banks
1. Introduction
Over the last decades, the study of artificial intelligence (AI) has involved the devel-
opment of intelligent machines that can perform tasks requiring human intelligence. AI
uses computer systems and algorithms to learn, reason, and make decisions based on data
inputs. AI technologies mimic human cognitive abilities and can analyze data, automate
tasks, and assist in various domains (Kok et al. 2002). AI refers to the scientific field and
technology that involves the development of intelligent machines capable of imitating
human behavior and intelligence (Ottosson and Westling 2020).
The significant advancements and practical applications of AI started to gain mo-
mentum in the 21st century with the advent of more powerful computing systems and
the availability of large amounts of data (Ratia et al. 2018;Haenlein and Kaplan 2019).
The specific implementation of AI will vary based on the industry, goals, and available
resources. For example, to harness the potential of AI and gain a competitive edge in their
respective industries, companies have developed in-house AI capabilities, partnered with
AI solution providers, or utilized cloud-based AI platforms (Burström et al. 2021). The
Int. J. Financial Stud. 2023,11, 115. https://doi.org/10.3390/ijfs11030115 https://www.mdpi.com/journal/ijfs
Int. J. Financial Stud. 2023,11, 115 2 of 25
banking sector is experiencing improved efficiency, accuracy, and personalized customer
experiences as a result of AI implementation (Anastasi et al. 2021). AI offers numerous
possibilities for banks to improve operations and drive innovation: data analysis, adaptive
learning platforms, personalized marketing, automating repetitive tasks, chatbots, enabling
natural language processing and voice recognition, and implementing risk-based predictive
maintenance and fraud detection, among others.
AI is the process of human intelligence implemented by machines. AI promotes the
sustainable and effective use of resources (Nikitas et al. 2020). Data-driven companies can
enhance decisions and enable more precise predictions (Anastasi et al. 2021). Specifically, it
is a more advanced digital transformation strategy that generates knowledge from existing
large datasets (Lichtenthaler 2020). Thus, the implementation of AI processes will improve
bank employees’ productivity (Plastino and Purdy 2018). Previous studies indicate that
banks have already recognized cost reduction and revenue generation through enhancing
the quality of the operations process, for example, in terms of lending, security services,
compliance improvements, fraud detection, and new types of services (Burgess 2017;
Kaya 2019;Ryll et al. 2020). Moreover, these customized solutions and services provide
customers with personalized investment strategies, wealth management techniques, and
robo-advisors (Wheeler 2020). Currently, AI plays a vital role in autonomous decision-
making processes, monitors assets and processes in real time, and enables value creation
(Alcácer and Cruz-Machado 2019), and the benefits will increase going forward (Cockburn
et al. 2018). In the rapidly evolving landscape of the banking sector, the integration of
AI holds significant potential for enhancing decision-making processes and improving
financial performance.
While AI can improve financial reporting, it can also lead to biases, lack of trans-
parency, data privacy concerns, and compliance challenges. Organizations may face job
displacement, training gaps, high implementation costs, interoperability challenges, and
ethical concerns (Nguyen 2022). To mitigate these negative effects, organizations should
prioritize responsible AI practices, invest in data quality and governance, and address po-
tential biases in AI models. Staying informed about regulations and ethical considerations
is also crucial (Nguyen and Dang 2023).
There are multiple motivations for conducting this study, including addressing stake-
holders’ concerns regarding the accountability and transparency of AI systems. Transparent
disclosure can attract investors who value technologically informed decision making and
potentially influence a company’s valuation and shareholder composition. Aside from at-
tracting investors, transparent AI disclosure can align with evolving regulatory frameworks.
As regulatory bodies scrutinize the ethical and responsible integration of AI, companies
that disclose their AI practices can demonstrate adherence to these guidelines, contributing
to compliance and a robust corporate reputation (Meiryani et al. 2022).
These findings suggest that AI applications are favorable for the banking sector and
beneficial for both shareholders and stakeholders, as well as for increased efficiency in the
financial sector, which leads to economic benefits. However, quantifying the link between
the use of AI and bank performance is warranted to explore the extent to which AI affects
businesses, consumers, and the whole economy.
Despite the opportunities and benefits of the application of AI, AI disclosure is still
voluntary. The decision of whether to disclose, to what extent, and the type of information
is almost entirely left to the discretion of companies. To date, there is no commonly accepted
practice for the level of AI disclosure. AI applications are relatively new. There are no
known dedicated international reporting standards agreed upon in this area. The existing
AI disclosure practices do not adequately capture the unique impacts of AI. The lack
of a shared vision and reporting standards for AI leads to different disclosure practices
depending on companies’ perceptions (Sætra 2021).
While several studies have been conducted on the benefit of AI in the banking sector,
there remains a research gap concerning the impact of AI disclosure on financial per-
formance. Filling this gap is essential to shedding light on the potential benefits of AI
Int. J. Financial Stud. 2023,11, 115 3 of 25
disclosure. To bridge this research gap, this study provides insights into the current level of
AI-related term disclosure practices among Jordanian banks. We create an AI disclosure
index by analyzing the spread of AI in the data of annual reports and investigating the im-
pact of mentioning AI-related keywords on financial performance to explore the potential
influence of disclosing AI-related terms on the financial performance of these banks.
Clear and ethical communication about AI initiatives can also enhance a company’s
image as a responsible innovator, fostering trust among consumers and partners (Hasan
et al. 2023). On the other hand, a lack of transparency or negative perceptions could
lead to skepticism and a tarnished reputation, potentially affecting financial performance.
In addition, this study is motivated to provide valuable insights that contribute to the
discourse on AI’s role in shaping contemporary business success, offering a comprehensive
perspective that encompasses ethical, regulatory, financial, and reputational considerations.
Depending on the timing of the study, there could be a lack of empirical evidence regarding
the direct relationship between AI disclosure and financial outcomes. Consequently, this
paper aims to answer the following research question:
•Does artificial intelligence disclosure impact financial performance?
Based on a content analysis of 115 annual reports for 15 Jordanian-listed banks from
the period 2014 to 2021, the results show an increase in AI-related keywords disclosure in
the annual reports of Jordanian-listed banks from 2014 to 2021. The results also indicate
that AI-related keywords disclosure has an influence on banks’ financial performance.
AI-related term disclosure has a positive effect on accounting performance in term of ROA
and ROE and has a negative impact on the bank’s total expenses, which supports the
dominant view that AI improves revenue and reduces cost and is consistent with past
literature findings.
This study contributes to the growing body of AI literature. First, it determines the
spread of AI-related term disclosure in Jordanian banks by forming an initial AI-related
term disclosure index. Second, it provides insights into the relationship between AI dis-
closure and financial performance. The findings of this study contribute to policymakers’,
international authorities’, and supervisory organizations’ efforts to address AI disclosure
issues and highlight the need for disclosure guidance requirements. Moreover, the study
provides a contribution to banking sector practitioners who are transforming their opera-
tions using AI mechanisms and supports the need for more AI disclosure and informed
decision making in a manner that aligns with the objectives of financial institutions.
The remainder of this paper is organized as follows. The Section 2reviews the relevant
literature. The Section 3illustrates the research methodology. The Section 4presents and
discusses the study findings. The Section 5concludes the study.
2. Literature Review
2.1. AI Implications in the Banking Industry
The latest tech innovations that have revolutionized digital business are AI, machine
learning, big data analytics, cloud computing, and social media. These innovations are used
in the daily life of modern society. Technology changes physical, tangible items; contributes
to operations enhancement; and promotes competence and ability for future business
solutions (Tekic and Koroteev 2019). AI’s abilities simplify its applications. Firstly, AI can
forecast what is going on via the processing of audio, text, and computational linguistics in
the surrounding environment. Secondly, AI relies on language and meaning through natu-
ral language processing (NLP) and assists humans in complying with machines through
related AI algorithms. Finally, AI software systems can act alone without human contribu-
tion (Purdy and Daugherty 2016;Rao and Verweij 2017;Tákacs et al. 2018;Ottosson and
Westling 2020). Moreover, unlike conventional machines, AI can even enhance itself all
the time due to its self-learning ability based on past operation experiences (Öztemel and
Gursev 2020). The new digital solutions are always changing the competitive strategies
used in business and contributing to new approaches to value creation (Shang and Zhang
2022).
Int. J. Financial Stud. 2023,11, 115 4 of 25
There is increasing feasibility of the utilization of chatbots by the banking industry. For
example, a chatbot utilizes natural language technology to solve the user’s problems (Suhel
et al. 2020). The use of chatbots in the financial sector has changed the problem solving and
answering of consumers’ queries (Hwang and Kim 2021). Chatbots comprehend written
and spoken text and can respond to vague questions and communicate with other portals or
online data stores. Chatbot technologies handle massive numbers of calls from customers
and enhance customers’ satisfaction and confidence in banking services and their perceived
usefulness (Sanny et al. 2020;Eren 2021;Nguyen et al. 2021). Moreover, chatbots handle
more accounts than human advisors at lower operational costs and maximize profits (Patil
and Kulkarni 2019). Furthermore, the 24/7 availability of online assessment chatbots has
made operations more flexible, leading to a decrease in the use of physical bank branches
(Wheeler 2020). Generally, banks utilize software such as UiPath, Automation Anywhere,
Blue Prism, end-user devices, robots, software, and artificial intelligence agents to aid the
process of repetitive banking operations (Vijai et al. 2020).
The introduction of AI enhances and simplifies the process of decision making while
continuing to obey regulations. AI can reduce the number of false contracts, enhance
the prediction of operational resources (Han et al. 2020), and accomplish obedience to
regulatory requirements (Couchoro et al. 2021;Garcia-Bedoya et al. 2020;Kute et al. 2021).
Numerous modern AI techniques have been utilized by banks to reduce fraudulent pro-
cesses, such as data mining, fuzzy logic, machine learning, sequence alignment, and genetic
programming (Raj and Portia 2011). Banks have improved their process speed, accuracy,
and efficiency through the use of autonomous data management (Soni 2019). Predictive
analytics can stop fraud incidents before they happen through several technologies such
as Secure Socket Layer (SSL) for online transactions, encryption data storage, multi-level
authorization, device fingerprinting, malware detection, token passwords, signing trans-
actions, and endpoint protection (Kikan et al. 2019). Banks have utilized deep learning
and artificial neural networks in personalized retail banking to assess their performance in
direct marketing and determine which customers are likely to accept marketing proposals
(Kim et al. 2015;Zakaryazad and Duman 2016). AI enhances the speed of the process,
decreases cost-related issues, reduces operational risks, and improves know-your-customer
processes through the use of chatbots and robot-advisors’ services (Kaya 2019).
2.2. The Impact of AI on Financial Performance
The business community has witnessed an increased interest in the use of AI tech-
niques, with financial services, manufacturing, information services, and banks as the
most beneficial sectors (Bughin et al. 2017;Green et al. 2009). According to executives’
surveys across various industries, investment in AI and focus on collaboration between
employees and machine learning technology could increase banks’ revenue by 34% (Shook
and Knickrehm 2018).
Furthermore, according to the AI McKinsey Global Surveys series since 2004 and the
current state of AI in 2023, AI adoption is increasing globally; the survey’s results show an
increase in companies embedding AI in at least one business function or one business unit.
Firm performance has received great attention from researchers in the accounting
and finance area (Agarwal 2020). In general, researchers examine what might impact the
financial performance of a firm in both positive and negative ways. For example, Almustafa
et al. (2023) found that national governance quality plays an important role in mitigating
the negative impact of the COVID-19 crisis on firm performance. Moreover, Nguyen (2022)
found that new technologies, such as FinTech, might have a negative impact on financial
performance. This finding was supported by Nguyen and Dang (2023), who found that
FinTech development might negatively impact stock price and crash risk.
The Financial Stability Board (FSB) has analyzed the potential financial stability im-
plications of the growing use of AI in financial services. FSB (2017) describes supply and
demand factors that drive AI adoption in the financial sector; supply factors such as techno-
logical advances and the availability of data and infrastructure; and demand factors such
Int. J. Financial Stud. 2023,11, 115 5 of 25
as profitability needs, competition with other firms, and the demands of financial regula-
tion. According to the FSB, there are four sets of AI use cases: “(i) customer-focused
(or ‘front-office’) uses, including credit scoring, insurance, and client-facing chatbots;
(ii) operations-focused (or ‘back-office’) uses, including capital optimization, risk man-
agement model, market impact analysis, trading and portfolio management in financial
markets; and (iv) uses of AI and machine learning by financial institutions for regulatory
compliance (‘RegTech’) or by public authorities for supervision (‘SupTech’)” (FSB 2017).
Furthermore, the Organisation for Economic Co-operation and Development (OECD)
has highlighted the potential impact of AI in specific financial market activities. According
to the OECD (2021), the deployment of AI drives competitive advantages through two main
avenues: first, by improving the firms’ efficiency through cost reduction and productivity
enhancement, therefore driving higher profitability (e.g., enhanced decision-making pro-
cesses, automated execution, gains from improvements in risk management and regulatory
compliance, back-office and other process optimization); and second, by enhancing the
quality of financial services and products offered to consumers (e.g., new product offerings
and high service customization). The use of AI generates new benefits, possibly leading
to a substantial increase in labor productivity, operational workflow efficiency, and new
revenue streams (PwC 2020). It will also strengthen risk management, improve customer
experience, and enhance performance (Gokhale et al. 2019).
AI brings benefits to financial institutions in the form of stability, greater profitability,
efficiencies in the provision of financial services, and systemic risk surveillance and regula-
tion. From the firm’s perspective, AI taking over repetitive bank tasks and autonomous AI
solutions reduce the demand for less skilled labor and improve the efficiency of remaining
staff (Kaya 2019). Thus, the implementation of speed-enhancing processes will improve
bank employees’ productivity (Plastino and Purdy 2018). Previous studies have indicated
that banks have already recognized cost reduction and revenue generation through enhanc-
ing the quality of operations, for example, in terms of lending, security services, compliance
improvements, fraud detection, and new types of services (Burgess 2017;Kaya 2019;Ryll
et al. 2020). Moreover, these customized solutions and services provide customers with
personalized investment strategies, wealth management techniques, and robo-advisors
(Wheeler 2020).
These findings suggest that AI applications are favorable for the banking sector and
beneficial for both shareholders and stakeholders, as well as for increased efficiency in
the financial sector, which leads to economic benefits. The possible impact of AI in most
practices has just started bringing benefits to financial institutions through various channels,
improving efficiency and enhancing revenue sources by offering new services and products.
Nevertheless, quantifying the link between the use of AI and bank performance is hard,
not least due to issues around AI identification. In this context, a question arises about the
extent to which AI affects businesses, consumers, and the whole economy.
2.3. AI Voluntary Disclosures and Financial Performance
The rapid advancements in AI have significantly impacted various sectors, and the
banking industry is no exception. As banks increasingly integrate AI technologies into
their operations, there is a crucial need to address the disclosure practices surrounding
AI implementation (Sætra 2021). Financial services, among some other industries, with
large volumes of customers and operations, depend on data processing in terms of text
and speech; these services have most often adopted AI and disclose more information on
natural language capabilities (AI McKinsey Global Surveys series).
Despite the opportunities and benefits of the application of AI, AI disclosure is still
voluntary. The decision of whether to disclose, to what extent, and the type of information
is almost entirely left to the discretion of companies. To date, there is no commonly accepted
practice for the level of AI disclosure. AI applications are relatively new. There are no
known dedicated international reporting standards agreed upon in this area. The existing
AI disclosure practices do not adequately capture the unique impacts of AI. The lack
Int. J. Financial Stud. 2023,11, 115 6 of 25
of a shared vision and reporting standards for AI leads to different disclosure practices
depending on companies’ perceptions (Sætra 2021).
In the age of Al, opacity is one of the greatest perils facing humans today. S. Lu (2021)
argues that the current disclosure framework under corporate securities law has a limited
impact on reducing opacity in Al systems. He calls for an effective disclosure framework
for AI products and services in order to address AI opacity. It is crucial to ensure that AI
algorithms are harmless by requiring more transparency to mitigate algorithmic opacity.
Accordingly, such an advanced disclosure framework can help enhance transparency to
reduce the risks posed to stakeholders, stabilize capital markets, and promote sustainability
in the long run.
Recently, authorities’ regulators and supervisors have been considering requirements
around AI disclosure. For instance, the OECD addressed AI disclosure issues to promote
innovative and trustworthy use of AI. Based on the OECD AI Principles, ‘there should be
transparency and responsible disclosure around AI systems to ensure that people under-
stand AI-based outcomes and can challenge them’ (OECD 2019). Thus, financial consumers
need to be informed about the use of AI techniques in the delivery of a product, as well
as potential interaction with an AI system instead of a human being, to make conscious
choices among competing products. Moreover, the OECD (2021) further highlights the
need for disclosure requirements that help financial service providers to better assess
whether prospective clients have a solid understanding of how the use of AI affects the
delivery of the product. Similarly, the International Organization of Securities Commissions
(IOSCO) also calls for including more clear information about the AI system’s capabilities
and limitations in such disclosures. IOSCO (2020) proposes guidance on “Appropriate
transparency and disclosures to investors, regulators, and other relevant stakeholders”.
Disclosure requirements, therefore, need to consider any information around the use of AI
techniques that may impact investors, regulators, and other relevant stakeholders, such as
information on algorithmic trading models, data collection, and cross-border cooperation.
AI regulations are under development worldwide. There is a wave of new AI reg-
ulation that will soon have significant implications for AI systems. For example, the US
“AI Disclosure Act of 2023: A Step Towards Algorithmic Transparency” represents an
important step towards AI algorithmic transparency for those interacting with AI systems
to be more informed about what they are interacting with, allowing them to make decisions
accordingly. The scope of the AI Disclosure Act applies to any entity engaged in commerce,
including banks (Section 5, the Federal Trade Commission). The EU AI Act (European
Parliament 2023) also imposes transparency requirements for AI systems used in the EU. It
is a significant piece of legislation that could have a major impact on the development and
use of AI in the European Union. The EU AI Act specifies corresponding requirements for
transparency, documentation, auditing, and obligations. AI systems should be developed
to allow appropriate transparency and provision of information to users (Article 13). Such
acts are still in the early stages of development, only having recently passed, but it is clear
that they have the potential to shape the future of AI in the US and Europe. Becoming
prepared early by establishing the appropriate disclosure procedures is the best way to
ensure compliance with transparency requirements.
2.4. Development of Hypotheses
The prior literature discusses voluntary disclosure based on various theoretical per-
spectives. For example, agency theory focuses on the communication of specialized infor-
mation by managers to enhance firm value and reduce costs of capital (Jensen and Meckling
1976). Voluntary disclosure allows managers to communicate effectively and enhance the
firm’s value since it encompasses various aspects, such as investment opportunities and
financing policies. Agency theory scholars have called for expanding the categories of vol-
untary disclosure as a tool to observe the managers’ actions and improve firm performance
(Barako et al. 2006;Fang and Jin 2012;Cockburn et al. 2018;Haninun et al. 2018;Hassanein
et al. 2019;Albitar et al. 2020). Similarly, the theory of capital needs points out that volun-
Int. J. Financial Stud. 2023,11, 115 7 of 25
tary disclosure helps companies to increase additional funds at lower cost. Since companies
need capital to continue their operations, disclosing more information voluntarily provides
investors with additional information that enables them to make more valuable economic
predictions about the firm (Bertomeu et al. 2011;Bini 2018;Cheynel 2013;Shehata 2014).
From the perspective of signaling theory, companies would disclose more information
than is mandatory in order to attract investors’ attention, reduce information asymmetry
between the company and its stakeholders, and signal their capabilities (Bertomeu et al.
2011;An et al. 2011). Managers voluntarily disclose good news to signal positive outcomes
and may also disclose bad news to demonstrate efforts to address future losses. Managers
of profitable firms prefer to voluntarily disclose significant information to signal profitabil-
ity, boost investor confidence, and improve financial performance (Campbell et al. 2003;
Albitar et al. 2020;Hassanein et al. 2019;Alkaraan et al. 2022). Legitimacy theory also offers
a reasonable explanation for voluntary disclosure. Transparency, through voluntary disclo-
sure, is one of the different strategies that companies adopt to legitimate their practices and
activities to prove compliance with stakeholders’ expectations (Magness 2006;Lightstone
and Driscoll 2008;Bonsón et al. 2021). Organizations can gain social approval for their
actions by engaging in higher levels of transparency (Bonsón and Bednárová2013).
Regarding voluntary AI disclosure, companies want to inform stakeholders about
increased operational efficiency to signal value creation, sustainability, and better competi-
tion in the market related to their AI products and processes. Thus, being more transparent
about new AI technologies would attract potential investors and increase the trustworthi-
ness of other stakeholders. Information about applied AI tools is communicated to different
stakeholders to gain their trust and acceptance and obtain legitimation (Osburg 2017;Tho-
run 2018;Bonsón et al. 2021,2023). Similar to other voluntary disclosure challenges, the
applications of AI models raises important disclosure issues in the banking industry due
to the nature of the business and its operational environment. The growing development
and utilization of AI can influence voluntary disclosure in the banking industry (Saenz
et al. 2020). The competitive landscape in the banking industry impacts the decisions to
disclose information about firms’ use of AI. Firms may elect to disclose more voluntary
information if they believe that will gain a competitive advantage. For example, banks like
to show their expertise in the field (Yu et al. 2017;Krakowski et al. 2022). Therefore, banks
that operate in highly competitive markets may choose to disclose more information about
their AI systems to be competitive with their competitors. According to the AI McKinsey
Global Surveys series, companies are most likely to adopt AI in functions that provide core
value in their industry, and financial services are more likely than other industries to have
adopted AI in service operations, marketing, sales, and risk functions.
AI is found to have a positive impact on a firm’s nonfinancial indicators, such as the
accounting information system efficiency and the success of the workflow within a firm
(Hashem and Alqatamin 2021). Wamba-Taguimdje et al. (2020) stated that AI benefits
organizations, and more specifically, their ability to improve performance at both the
organizational (financial, marketing, and administrative) and process levels. Fethi and
Pasiouras (2010) provided a comprehensive review of 179 academic papers that examined
the impact of AI on different financial indicators of banks. They concluded that one of the
financial indicators that is affected positively by AI is the banks’ financial performance.
Therefore, the following first hypothesis is suggested:
H1. AI-related terms disclosure will be positively related to the banks’ financial performance.
In the same vein, banks that prefer clarity, moral behavior, transparency, and social
responsibility are more likely to disclose information about their AI systems if they believe
that disclosure enhances confidence and credibility among customers, regulators, and
society at large (ElKelish and Hassan 2014). Disclosing information about AI practices,
their algorithm models, data sources, and decision-making operations enhances justice and
trust among customers, regulators, and stakeholders. Moreover, it keeps banks as far as
possible from any reputational dangers that may emerge from AI utilization decisions. For
Int. J. Financial Stud. 2023,11, 115 8 of 25
example, companies that have confidence in their use of AI are more likely to disclose more
related information. Disclosing information about AI use creates a reputation of innovation
and excellence that engages more clients who value advanced AI systems. AI transparency
enhances banks’ reputation, customer loyalty, and profitability (Felzmann et al. 2020). In
addition, the banking industry is highly sensitive to stricter regulations in contrast to other
industries. The banks may disclose more information about their AI systems to show
their compliance with moral standards and gain a competitive edge in markets that value
responsibility (Krakowski et al. 2022). Overall, the tendency of AI voluntary disclosure is
to highlight favorable information to obtain positive effects in terms of economic impact
and to appeal to investors, as this disclosure can help investors gain a clearer picture in
terms of the company’s investments in AI technologies (Bonsón et al. 2021).
Based on various theoretical perspectives, expectations, and the supporting literature,
it is assumed that companies that apply AI in their processes and recognize a financial
benefit already, in terms of increased revenue and decreased costs, are motivated to disclose
positive information about the development, application, and use of AI. It is also anticipated
that companies that voluntarily disclose AI information more often are more likely to have
implemented AI as part of their business strategy and recognized financial benefits. Hence,
we expect the frequency of disclosure of AI-related terms in banks’ annual reports to impact
financial performance in terms of increased revenue and decreased costs compared to
other banks.
The advancement of new technological solutions such as AI has shown its merit
through the impact on organizations’ different financial indicators, and the total cost is one
financial indicator that is expected to be reduced by the adoption of AI techniques (Nguyen
and Dang 2023). Fethi and Pasiouras (2010) indicated that the bank’s total cost would be
minimized by using AI techniques. This evidence is supported by Doumpos et al. (2023),
who reviewed AI techniques in the banking literature and concluded that AI techniques
are expected to enhance financial indicators within the banking industry. Therefore, the
following second hypothesis is suggested:
H2. AI-related terms disclosure will be negatively related to the banks’ total expenses.
3. Research Design
3.1. AI Disclosure
Following the purpose of this study, we use the content analysis method for measuring
levels of AI disclosure and produce a preliminary list of AI keywords. Previous studies
have applied similar processes (e.g., Hassanein et al. 2019;Elmarzouky et al. 2021;Karim
et al. 2021;Alkaraan et al. 2022). In particular, the selection of AI-disclosure-related items is
carried out in three stages.
Firstly, we create an AI disclosure index through a comprehensive review of AI
components that have been mostly mentioned in the finance sector by related professional
organizations such as the FSB (2017), OECD (2019), and IOSCO (2020). For example,
the AI Index Report 2019 highlight the terms most often mentioned in the finance sector,
including “AI”, “Big Data”, “Cloud”, and “Machine Learning” (Perrault et al. 2019), based
on the previously related literature that utilizes various self-constructed disclosure proxies’
measures (Finkenwirth 2021;Zetzsche et al. 2020;Hussainey et al. 2022). In the context of
AI, Omar et al. (2017) applied content analysis to the annual reports of Malaysian publicly
listed companies, searching for the words “Artificial Intelligence”, “Machine Learning”,
and “Big Data”. Cam et al. (2019) conducted a content analysis and classified the AI
applications in USA banks; “Robotic Process Automation”, “virtual agents”, “natural
language text understanding”, “machine learning”, and “computer vision” are mentioned
frequently. Bonsón et al. (2023) conducted content analysis of European listed companies,
and they identified search keywords items including “artificial intelligence”, “machine
learning”, “automat”, and “algorithm”.
According to McWaters (2018), focusing on AI alone is not sufficient to understand
the myriad ways in which it could be used within financial institutions. Advances in any
Int. J. Financial Stud. 2023,11, 115 9 of 25
technology will increase the capabilities of all other technologies that interact with it. Thus,
AI must be understood within the context of all other technologies. We combined a set of
the most often mentioned AI-related terms in the finance sector to identify the preliminary
AI disclosure index. Appendix ATable A1 Column A presents the keywords for AI-related
disclosure terms.
Second, the frequency of AI-related terms for each bank’s annual report is measured
through the computerized content analysis software “Maxqda”. The keywords and the
context of those keywords are also analysed, including the sentence or sentences before
and after the search term, to provide insight into the AI business strategy.
Third, the AI-related terms disclosure keywords are classified into three categories.
The first group combines the keywords related to digital awareness, transformation, and ca-
pabilities. The second group is related to AI applications, products, services, and processes.
The last group is related to AI challenges and threats in terms of information and cyber
security (Appendix ATable A1 Column B presents the classes keywords for AI-related
disclosure terms for each category). Thereafter, the resulting data for the content analysis
are incorporated into multiple regressions.
3.2. Regression Model and Variable Definitions
The following regression model is used to measure the impact of mentioning AI-related
keywords in the annual reports on financial performance.
Performance t = ß0+ ß1AIFRECt−1+ ß2BSIZEt−1+ ß3DEBTt−1+ ß4BDSIZt−1
+ ß5INDPBt−1+ ß6FORSHt−1+ ß7LASHRt−1+ ß8BRNCHt−1
+ ß9BKAGEt−1+ ß10 YEAR + e
(1)
where performance is the bank’s financial performance, assessed by several alternative
estimations. We consider accounting measures of performance, including return on equity
(ROE), return on assets (ROA), and net interest income (NII), in our basic analysis. We
also consider market performance measures, such as price earnings ratio (P/E). ROA and
ROE are widely used in the literature as the more effectively a company’s management
produces revenue from its assets and from shareholders’ capital, the higher they are; thus,
the higher the ROA and ROE, the higher the firm performance (Almustafa et al. 2023;
Hasan et al. 2023). Alternatively, we also consider total expenses (TEXP), assuming that AI
implementation reduces cost, which leads to higher financial performance. NII reflects the
main banking operation’s income from lending and borrowing, which is assumed to be
affected directly by increased revenue and decreased cost of these main services. These
ratios provide a picture of the firm’s financial development; past studies have used them
for evaluating financial performance (Hagel et al. 2013;Heikal et al. 2014;Fatihudin 2018).
AIFREC (‘AI frequency’) is the AI-related terms disclosure frequency measured as the
number of mentioned AI-related terms in each annual report (Alkaraan et al. 2022;Finken-
wirth 2021). The AI practices and AI disclosure choices at each bank are sensitive to many
internal and external factors. The banks’ specific characteristics create incentives for bank
managers for AI implementation and AI disclosure practices according to these features.
The literature provides abundant research on the association between banks’ characteris-
tics and bank performance. In our empirical models, we include three groups of control
variables that reflect banks’ governance features, ownership structure, and cost of capital
attributes, and economic-specific characteristics, many of which are commonly known from
the previous literature to be important factors affecting the banks’ performance measures.
The first group of control variables reflects the internal corporate governance features
at the bank level. We control for board size and board independence (Jensen 1993;Yer-
mack 1996). Independent directors are deemed more effective monitors due to greater
reputational costs (Fama and Jensen 1983;Coles et al. 2008). The board of directors is
considered a core internal corporate governance variable in the literature (see S. Agarwal
(2020) for a review; Dang and Nguyen 2021;Nguyen 2022). Hence, board size (BODSIZE)
is the number of board directors, and independent board (INDD) is the proportion of
Int. J. Financial Stud. 2023,11, 115 10 of 25
independent directors from the board. By including this control, we cater for the case that a
bank’s performance and AI disclosure are products of the board on which they serve.
In the second group, we include controls for bank ownership structure and costs of
capital attribute variables that may affect performance. Theoretically, agency costs of capital
arise from the conflict between shareholders and debt holders of a public company. The
debtholders may also place limits on the use of their capital if they believe that management
will take actions that favor shareholders instead of debtholders. Thus, we control the bank
ownership structure, including the large shareholders’ ratio (LSHAR), which is measured
as the percentage of accumulated large shareholders who own 5% or more of the bank’s
stock. Foreign ownership ratio (FORSH) is used to control for investors’ interest (Al-Gamrh
et al. 2020;Mallinguh et al. 2020;Nguyen 2022). We also control for the debtholders’
structure. The literature has found that debt ratio affects financial performance (Almustafa
et al. 2023;Gander 2012;Shiyyab et al. 2014). The debt ratio (DEBT) is measured as the
ratio of long-term debt to total debt. Large shareholders are predicted to be associated with
AI disclosure and performance.
Third, we control for economic characteristics at the bank level, consistent with what
has been documented in the bank performance literature. First, it has been documented
that bank size and operation complexity have an impact on management activities and,
primarily, bank performance (Almustafa et al. 2023;Nguyen 2022). Therefore, we control
bank size as the natural logarithm of total assets (BSIZE). In addition, we control for the
number of branches and bank age. Despite the widespread use of online banking services,
the banks’ physical presence is still important due to the opportunities for face-to-face con-
tact with the customer (Almustafa et al. 2023). The literature provides evidence that branch
networks still enable the bank to gain a competitive advantage by creating extensive-term
personalized links with their clients, leading to increased profits and reduced loan losses
(Berger and Black 2011;Hirtle 2007;Harimaya and Kondo 2012;Kondo 2018;Monferrer
Tirado et al. 2019). Therefore, we control for the number of branches with BRNCH as a
continuous variable reflecting the number of branches for each bank every year, and we
also include a control for bank age (BKAGE) as the total number of years a corporation has
been in operation (Almustafa et al. 2023;Nguyen 2022;Coad et al. 2018).
Finally, we also account for the impact of the COVID-19 crisis on bank performance;
we use a dummy variable as the independent variable, which is 1 for the COVID-19 period
or 0 otherwise. We define the COVID-19 crisis period as 2020 (Shen et al. 2020;Almustafa
et al. 2023;Hasan et al. 2023). Finally, we also control for years’ effects. We created an
indicator variable representing the year-dummies (YEAR_DUM) to control for year-specific
effects. All variable definitions are presented in Table 1.
Table 1. The variables’ names and definitions.
Variables Definitions
AIFREC The number of mentioned AI-related terms in each annual report
ROA Return on assets
ROE Return on equity
NII Net interest income
P/E Price-to-earnings ratio
TEXP The natural logarithm of total expenses
BKSIZ The natural logarithm of assets
DEBT The long-term debt to total debt
BDSIZ The number of board directors
INDPB The proportion of independent directors on the board
FORSH The ratio of foreign shares to total shares
LASHR The percentage of large shareholders who own more than 5% of total shares
BRNCH The number of branches for each bank every year
BKAGE The total number of years from the date of establishment of the bank
COVID-19 A dummy variable, equal to 1 for the COVID-19 period (the year 2020)
or 0 otherwise
Int. J. Financial Stud. 2023,11, 115 11 of 25
3.3. Sample and Data
Financial analysts use both financial and non-financial information to gain a reliable
picture of companies’ performance, and the annual report is one of the main sources for
the decision-making process of investors in the financial market (Araújo Júnior et al. 2014;
Zhou et al. 2017). Therefore, this research is based on the analysis of 130 annual reports for
all 15 Jordanian-listed banks from the period 2014–2022. Most of the annual reports are
available as a PDF version for all Jordanian banks on their websites. The relevant words
are searched, being those previously mentioned in the context analysis. The newly created
keyword dataset based on the annual reports provides insights into the development of
AI-related mentions.
4. Finding and Discussion
4.1. Descriptive Statistics
Table 2panel A provides an overview of the AI-related terms disclosure for each bank.
The total AI-related keywords disclosure is 2658, while he range is wide between banks. The
highest AI-related keywords disclosure 18% of the dataset belongs to the Jordan Ahli Bank,
12% to Safwa Islamic Bank, and 11% to Bank El Etihad. These banks mention AI-related
terms with highly frequency, while Societe General, International Islamic Arab Bank, and
Invest Bank are banks that mention AI-related terms with low frequency. The remaining
banks’ samples range from 4% to 9% of the dataset. Table 2panel B presents the frequency
of AI-related keywords mentioned in an annual report by year. The AI-related keywords
disclosure varied across years and increased dramatically from 2014 to 2022 (733 related
words in 2022, 73 in 2014). However, approximately 60% of AI-related keywords were
disclosed in the last three years of 2019-2022. This reflects the recent revolution of AI in the
banking industry, especially during COVID-19 time. The increase in AI-related disclosure
in annual reports suggests that banks have made more investments and rely more on AI
implementation.
Table 2. Summary statistics of AI disclosure by bank and by year.
Panel A. AI Disclosure Frequency by Bank Panel B. AI Disclosure Frequency by Year
Bank Name AI Freq Per% Year AI Freq Per%
Jordan Ahli Bank 471 18% 2014 73 3%
Safwa Islamic Bank 311 12% 2015 75 3%
Bank El Etihad 283 11% 2016 122 5%
Bank of Jordan 228 9% 2017 220 8%
The housing Bank for Trade 215 8% 2018 289 11%
Arab Bank 211 8% 2019 297 11%
Jordan Commercial Bank 172 6% 2020 441 17%
Cairo Amman Bank 165 6% 2021 515 19%
Jordan Kuwait Bank 137 5% 2022 733 23%
Jordan Islamic Bank 118 4%
Arab Banking Corporation 113 4%
Arab Jordan Investment Bank 103 4%
Societe General Bank 54 2%
International Islamic Arab Bank 48 2%
Invest Bank 29 1%
Total 2658 100% 2658 100%
The AI-related disclosure keywords are classified into three categories. The first group
combines the keywords related to digital awareness, transformation, and capabilities. The
second group is related to AI applications, products, services, and processes. The last group
relates to AI challenges and threats in terms of information and cyber security. Table 3
presents the AI-related disclosure terms classified into three categories.
Int. J. Financial Stud. 2023,11, 115 12 of 25
Table 3. The AI-related disclosure terms are classified into three categories.
AI-Related Terms/Words Frequency Percentage
AI digital awareness, transformation, and capabilities 640 24.09%
AI application, product, service, and process 886 33.33%
Information and cyber security 1132 42.58%
Total—AI-related terms/words 1925 100%
The first group combining the keywords related to digital awareness, transformation,
and capabilities represents 24% of the total, which highlights the banks’ keenness and
commitment to harnessing the potential of artificial intelligence in their operations and
services. The most frequently recurring keywords are digital transformation (143), fintech
(130), and financial technology (85). The annual reports of banks have mentioned artificial
intelligence 13 times, reflecting a significant interest and focus on this transformative
technology. This indicates a potential awareness within banks’ management regarding
the pivotal role these terms play in attracting attention to the banks’ pioneering role and
achieving a competitive advantage.
The second group related to AI applications, products, services, and processes rep-
resents 33% of the total. This reflects the level of dedication towards essential services
in achieving digital transformation and utilizing artificial intelligence technologies. This
signifies the unwavering commitment to harnessing the power of cutting-edge technologies
and embracing a future where innovation and digital advancements drive progress. The
keywords “robotic automation”, “digital banking”, “mobile banking”, “online banking”,
and “digital services” are of the most importance in the banking industry. This signifies the
transformation and advancements that are shaping the future of banking. Robotic automa-
tion streamlines operations, while digital banking, mobile banking, and online banking
offer convenient access to services. Digital services encompass innovative solutions that
enhance the overall banking experience. Together, these keywords represent the vital role
technology plays in revolutionizing the industry.
The last group is related to AI challenges and threats in terms of information and cyber
security. The extent of interest in this area is evident in the remarkable percentage (44%)
highlighting collective challenges and threats. The most frequently mentioned keywords
in this group are information security, cyber security, electronic security risks, IT security,
electronic banking services, and electronic security policies. This finding demonstrates a
strong emphasis on ensuring robust security measures in the digital landscape. These key-
words highlight the importance of protecting sensitive data, combating cyber threats, and
maintaining secure electronic banking services. They signify a commitment to safeguarding
digital systems and providing secure experiences for individuals and organizations. This
is consistent with previous results and industry surveys. For example, the global joint
survey conducted by the World Economic Forum and the Cambridge Centre for Alternative
Finance indicates that a majority of financial services companies (56%) have implemented
AI technology in terms of risk management domains.
Table 4provides descriptive statistics for the total sample. The mean of the AI-related
terms frequency variable is 20.98, with a minimum of 2 and a maximum of 121. This reflects
that some banks need more disclosure information with regard to AI-related terms. The
bank characteristics also vary according to each bank; for example, BKSIZ varies between
22 and 8441 million, BRNCH ranges between 12 and 211 branches, and the bank age
ranges between 5 and 88 years of operation. These differences are expected to explain bank
performance as well as AI disclosure.
Int. J. Financial Stud. 2023,11, 115 13 of 25
Table 4. Descriptive statistics.
Variable Obs No. Mean Median Sta Dev Min Max
AIFREC 127.00 20.98 14.00 20.63 2.00 121.00
ROE 127.00 7.84 7.92 4.36 −1.42 16.50
ROA 127.00 0.94 0.99 0.52 −0.43 2.05
P/E 127.00 12.87 10.93 9.33 0.00 62.10
NII (in millions) 127.00 174.48 111.89 249.17 9.11 913.23
TEXP (in millions) 127.00 109.71 63.20 165.02 11.78 860.18
BKSIZ (in millions) 127.00 2586 2200 1950 22.00 8441
DEBT 127.00 87.58 87.64 2.92 81.71 93.04
FORSH 127.00 0.07 0.05 0.09 0.00 0.52
LARSHR 127.00 0.67 0.66 0.21 0.30 0.97
BODSIZ 127.00 11.46 12.00 1.88 5.00 13.00
INDPB 127.00 0.41 0.42 0.11 0.18 0.64
BRNCH 127.00 64.78 48.00 49.61 12.00 211.00
BKAGE 127.00 40.69 39.00 18.81 5.00 88.00
AIFREC is the frequency of mentioned AI-related terms in each annual report. ROA is return on assets. ROE is
return on equity. NII is the natural logarithm of net interest income. P/E is the price-to-earnings ratio. TEXP
is the natural logarithm of total expenses. BDSIZ is the number of board directors. INDPB is the proportion of
independent directors from the board. LASHR is the accumulated percentage of large shareholders who own 5%
or more of the bank’s stock. FORSH is the ratio of foreign shares to total shares. DEBT is the ratio of long-term
debt to total assets. BKSIZ is the natural logarithm of assets. BRNCH is a number of branches for each bank every
year. BKAGE is the number of years a corporation has been in operation.
4.2. Correlations
Table 5provides the results of the correlations among variables, with some coefficients
warranting particular attention. Overall, the correlations are relatively small, and the low
inter-correlations among all independent variables indicate that multicollinearity does not
appear to be a problem in the regression model. However, it provides valuable information
regarding the associations between AI disclosure and firm variables. For example, we
observe several associations between AI disclosure and bank characteristics that identify
which bank features are more conducive to AI disclosure practices. AI disclosure practices
are positively associated with corporate governance features in terms of BDSIZ and INDPB
(at 1% and 10%, respectively). Similarly, as expected, AI disclosure is positively associated
with BKAGE %, BKSIZ 5%, and BRNCH 10%. FORSH is positively associated with AI
disclosure, whereas LASHR is negatively correlated with AI disclosure, which indicates
that shareholders are either conservative with regard to AI implementation or they have
symmetric information, which implies weak disclosure in the annual reports. LASHR
is positively associated with BKSIZ, BDSIZ, and INDPB, while it is negatively related
to FORSH. DEBTH is negatively correlated with FORSH, BRNCH, and BKAGE. BDSIZ
is associated with INDPB and BRNCH. As expected, the economic characteristics are
correlated to each other; BKSIZ is linked to BRNCH and BKAGE, all at a 1% significance
level, and to the BDSIZ at a 5% level of significance.
Table 5. Correlations.
AIFRCD BKSIZ DEBTH LASHR FORSH BDSIZ INDPB BRNCH
AIFREC 1.000
BKSIZ 0.220 ** 1.000
DEBTH 0.107 −0.032 1.000
LASHR −0.327 *** 0.178 ** 0.048 1.000
FORSH 0.247 *** 0.266 *** −0.205 ** −0.188 ** 1.000
BDSIZ 0.234 *** 0.173 * −0.283 0.306 *** −0.089 1.000
INDPB 0.149 * −0.041 0.175 0.199 ** 0.073 0.170 * 1.000
BRNCH 0.153 * 0.915 *** −0.155 * 0.132 0.197 ** 0.218 ** −0.192 ** 1.000
BKAGE 0.275 *** 0.684 *** −0.294 *** −0.087 0.151 * 0.322 *** −0.038 0.763 ***
*p< 0.10, ** p< 0.05, *** p< 0.01.
Int. J. Financial Stud. 2023,11, 115 14 of 25
4.3. Regression Model
Multiple regression analysis using ordinary least squares (OLS) has been used. Table 6
reports the results of OLS regressions of AI-related term disclosure on a set of performance
indicator variables. Models 1, 2, 3, and 4 present the results for ROA, ROE, NII, and P/E
ratio, respectively. Model 5 presents the result for total expenses. The models have a
predictive capacity for the dependent variables, in terms of R2, which ranges between 0.27
and 0.64, and F-values are significant at the 1% level.
Table 6. The OLS regressions of AI disclosure on financial performance.
(1) (2) (3) (4) (5)
ROA ROE P/E NII TEXP
AIFREC 0.00427 ** 0.0314 ** 0.0567 0.0172 * −0.00355 **
(2.86) (2.54) (1.25) (1.97) (2.31)
LASHR −1.338 *** −11.48 *** 3.059 −2.376 *** −0.0298
(−5.49) (−5.75) (0.54) (−4.44) (−0.16)
DEBTH −0.0382 ** −0.284 ** −0.204 −0.0850 ** −0.0353 ***
(−2.56) (−2.32) (−0.59) (−2.59) (−3.03)
FORSH 1.312 *** 11.44 *** −18.55 −0.118 1.028 ***
(3.03) (3.22) (−1.86) (−0.12) (3.05)
BDSIZ 0.0226 0.0619 0.266 0.352 *** −0.00208
(0.89) (0.30) (0.45) (6.30) (−0.10)
INDPB −1.401 *** −12.80 *** 7.738 −0.184 0.0754
(−3.81) (−4.25) (0.91) (−0.23) (0.26)
BKSIZ −0.0168 −0.855 −0.194 * −0.498 ** 0.314 ***
(−0.15) (−0.95) (−1.58) (−2.05) (3.66)
BRNCH 0.00591 ** 0.0763 *** 0.0458 0.0493 *** 0.00913 ***
(2.57) (4.05) (0.86) (9.76) (5.10)
BKAGE −0.0158 *** −0.180 *** 0.0319 −0.0865 *** 0.00697 ***
(−3.78) (−5.26) (0.33) (−9.44) (2.45)
Year Dummy Yes Yes Yes Yes Yes
Bank Dummy Yes Yes Yes Yes Yes
_cons 6.123 ** 15.97 *** 22.56 * 19.59 *** 13.21 ***
(2.55) (3.81) (1.91) (3.72) (7.08)
N127 127 127 127 127
R20.466 0.495 0.278 0.632 0.641
adj. R20.425 0.456 0.212 0.579 0.594
t-statistics in parentheses. * p< 0.10, ** p< 0.05, *** p< 0.01. AIFREC is the frequency of mentioned AI-related
terms in each annual report. ROA is return on assets. ROE is return on equity. NII is the natural logarithm of
net interest income. P/E is the price-to-earnings ratio. TEXP is the natural logarithm of total expenses. BDSIZ is
the number of board directors. INDPB is the proportion of independent directors from the board. LASHR is the
accumulated percentage of large shareholders who own 5% or more of the bank’s stock. FORSH is the ratio of
foreign shares to total shares. DEBT is the ratio of long-term debt to total assets. BKSIZ is the natural logarithm of
assets. BRNCH is the number of branches for each bank every year. BKAGE is the number of years a corporation
has been in operation.
Models 1 and 2 show that performance indicator variables such as ROA and ROE
provide an explanation for AI-related term disclosure. In particular, the impact of AI-related
term disclosure is positive and statistically significant, with ROA and ROE at a 5% level
of significance. This indicates that additional AI-related term disclosure is valued and
related to better performance. Moreover, AI-related term disclosure provides an additional
signal for specialist technology knowledge and expertise in modern operational settings
that allow for additional performance. These results signify that AI frequency has an
influence on the bank’s profitability and shareholders’ equity. In the same vein, the impact
of the frequency of AI-related term disclosure on total expenses (TEXP) is negative and
statistically significant at a 5% level of significance, which supports the dominant view
that AI reduces cost and is consistent with past findings. For example, banks are already
Int. J. Financial Stud. 2023,11, 115 15 of 25
strengthening customer relationships and lowering costs by using artificial intelligence
(McKinsey & Company 2021).
The bank-specific features also provide additional insight into banking performance
and can explain the variation between banks’ performances. For example, FORSH is
positively and statistically significantly correlated with bank performance in terms of ROA
and ROE at a 1% level of significance. This result is consistent with previous studies
that found foreign ownership positively affects firms’ financial performance (Al-Gamrh
et al. 2020;Mallinguh et al. 2020) and financial stability in emerging markets (Nguyen
2022). BRNCH is positively associated with bank performance in terms of ROA, ROE,
and NII. BDSIZ is positively and statistically significantly correlated with NII at a 1%
level of significance, while it did not have any significant influences on other measures of
performance.
LASHR is negatively and statistically significantly correlated with bank performance
in terms of ROA, ROE, and NII at a 1% level of significance. This finding is consistent with
previous studies that found large and concentrated shareholdings have a negative impact
on performance (Al-Malkawi et al. 2014;Abdallah and Ismail 2017). DEBTH is negatively
and statistically significantly correlated with bank performance in terms of ROA, ROE, and
NII at a 1% level of significance. This is consistent with Almustafa et al. (2023)’s finding
that DEBTH is negatively and statistically significantly correlated with ROE and ROA.
Unexpectedly, BKAGE is negatively and statistically significantly correlated with the
bank performance measures ROA, ROE, and NII. Loderer et al. (2017) argue that firm
age increases organizational rigidity, monitoring, and corporate control, thus leading to
declining growth opportunities; they found evidence that companies invested less as they
grew older. Similarly, INDPB is negatively and statistically significantly correlated with
the bank performance measures ROA and ROE. BKSIZ is also negatively and statistically
significantly correlated with bank performance measures in terms of PE and NII. This
indicates that the too-big-to-fail problem may exist. The results are consistent with previous
studies that found large firms reduce financial stability and diversification in emerging
markets (Nguyen 2022) and are associated with more risk due to the “too big to fail”
problem (Zardkoohi et al. 2018;Almustafa et al. 2023;Nguyen 2022). Model 5 in Table 6
shows that TEXP is positively and statistically significantly correlated, all at a 1% level,
with each of FORSH, BKSIZ, BRNCH, and BKAGE. However, TEXP is negatively and
statistically significantly correlated with DEBTH, at a 1% significance level.
Finally, we investigate the impact of the COVID-19 crisis on firm performance. Firstly,
we use a dummy variable, which is 1 for the COVID-19 period or 0 otherwise. We further
split the sample into two parts: before and during COVID-19. Overall, we do not observe
any impact on the relationship between AI disclosure and bank performance. Hasan et al.
(2023)’s results indicate that COVID-19 does not necessarily significantly impact business
performance outcomes. They argue that modern technology, such as artificial intelligence,
significantly mitigates the negative impacts. Similarly, Almustafa et al. (2023) argue that
the national governance system has significantly reduced the impact of the COVID-19
crisis on firms’ operations, and government support reduces the effects of economic shock,
especially in banking sectors. Meanwhile, some variables’ coefficients have been changed
as expected due to the COVID-19 crisis impact, as seen in Table A5 in Appendix C.
Although some evidence is provided that AI adoption has a positive effect on account-
ing performance, other performance indicator variables lack significance. These results are
consistent with Nguyen (2022)’s finding that FinTech development negatively affects finan-
cial stability in an emerging market. A possible interpretation is that annual reports do not
provide enough insights into AI implementation. It might be that banks implementing AI
have not mentioned AI-related terms in their annual reports due to a lack of AI disclosure
requirements. Therefore, banks that mentioned fewer AI-related terms have not adopted
AI yet or adopted AI in limited business units. Moreover, the disclosure of AI-related terms
is not enough to achieve financial impact, as the benefit of adopting AI cannot be expected
automatically based solely on the AI disclosure mentions.
Int. J. Financial Stud. 2023,11, 115 16 of 25
The weak relation may be due to the contextual settings in well-established developed
customer relations countries, such that AI disclosure may not fully affect performance.
If the banking operation’s performance is highly developed, stable, and automated, AI
disclosure may not require changes in some banking practices. These AI disclosure benefits
could have had little impact on the bank’s performance.
In line with these findings, the result shows that Jordanian-listed banks increased
AI-related keywords disclosure in their annual reports. Banks developing AI applications
often mention use cases and point out the benefits of AI for improving their operation.
This indicates the importance of integrating AI technologies into business models, leading
to lower cost and higher performance, which is consistent with previous AI results and
industry surveys. Companies have already recognized the contribution of AI adoption for
better overall performance, increased revenue, and decreased cost (AI McKinsey Global
Surveys series; the global joint survey conducted by the World Economic Forum and the
Cambridge Centre for Alternative Finance). Overall, the results align with the tendency of
various theoretical perspectives of AI voluntary disclosure that companies are motivated
to highlight favorable information to obtain positive economic impact and to appeal to
investors.
5. Conclusions
This study examined whether AI-related references in annual reports could be used
as an explanatory variable for financial performance. We analyzed 115 annual reports for
15 Jordanian-listed banks from the period 2014–2021. The analysis of annual reports shows
an increase in the frequency of AI-related terms disclosures since 2014. This development
indicates that Jordanian banks have become more aware of AI adoption, implications, and
benefits. At the same time, there is a weak level of AI-related disclosure in some Jordanian
banks, which indicates that they are still at an early AI implementation stage, at least on
the level of AI disclosure. As the trend of AI adoption is still developing, more efforts are
needed for improvement in the context of voluntary AI disclosures.
Based on the results, the presence of AI-related keywords in a bank’s disclosures
positively impacts its profitability and efficiency, as indicated by improved ROA and ROE.
It also leads to a decrease in total expenses, suggesting that AI is streamlining operational
processes and reducing costs. These findings demonstrate AI’s potential to drive revenue
growth and enhance efficiency in the banking sector.
Based on AI-related mentions in the annual reports of Jordanian banks, this study
shows a positive impact of AI-related term disclosure on accounting performance, and
financial benefits have been realized. To the best of the researchers’ knowledge, this study
is the first in Jordan that links AI-related terms disclosure in annual reports to financial
performance. This study contributes to the existing literature by providing new evidence of
AI voluntary disclosures, specifically offering insights into how Jordanian banks disclose
information related to AI in their annual reports.
This study provides bank executives, annual report users, regulators, and policymak-
ers with a view of AI disclosure’s impact on financial performance and the competitive
advantage of AI disclosure in financial services. The study’s findings are relevant to annual
report providers in that disclosures related to AI are increasing in the banking industry
and are of interest to users. In particular, AI disclosure might be useful to investors and
financial analysts because it helps them to gain a clearer picture in terms of the company’s
investments in AI technologies and the sustainability of their investments. The study
also provides regulators with recent evidence on voluntary disclosures in general and
disclosures on AI that can help regulators assess current trends in AI voluntary disclosures,
understand the challenges and opportunities of AI, and predict future directions in the
adoption and management of AI. Regarding implications for policymakers, we highlight
the importance of establishing a unified AI disclosure framework, making annual reports
more transparent and easier to understand for investors and other stakeholders. As a result,
Int. J. Financial Stud. 2023,11, 115 17 of 25
we support the new AI regulation development worldwide that enhances the quality and
clarity of the AI information presented in annual reports.
According to our results, we provide a topic for future research. The AI adoption
decisions and AI-related terms disclosures may be driven by company culture, corporate
governance, top management leadership, and ownership structure. Future research may
consider more firms’ characteristics and control for other factors that drive the success of
AI implications, adoption, and disclosures. In addition, the benefit of AI implementation
could be reviewed on the business unit level or process level rather than the level of the
banks’ overall performance. Future research could consider the business unit performance
separately.
The results of this study have substantial practical implications for banks and their fi-
nancial performance. This study shows that banks can improve their financial performance
by voluntarily disclosing their AI initiatives. This increases stakeholder trust and attracts
AI-informed investors. It also helps mitigate risks associated with AI and gives banks
a competitive advantage through differentiation. Being transparent about AI practices
also helps with regulatory compliance and can lead to cost reductions and technological
innovation. These findings can guide banks in optimizing their AI-related practices to drive
positive financial outcomes.
To understand AI adoption decisions within an organization, it is important to consider
company culture, corporate governance, leadership, ownership structure, risk appetite, and
change management. These factors shape the organization’s approach to AI and impact
how decisions are formulated and executed. By analyzing these dimensions, organizations
can make informed decisions that align with their unique characteristics and aspirations.
Analyzing the impacts of AI implementation at the business unit or process level can
reveal targeted performance improvements, enhanced decision making, efficiency gains,
cost savings, customization, risk assessment, change management insights, competitive
advantage, strategic alignment, and new performance metrics. This approach provides
a detailed perspective on the transformational effects of AI within an organization and
maximizes its potential benefits.
We identify some limitations of our research in terms of data availability; some of
the annual reports are not published on the banks’ websites or are not available in the
English language. In addition, some annual reports are not available as PDF files but
rather as scanned images, which prevents analyzing these through computerized software.
Future research may also consider other banks’ published information or publications of
third parties rather than annual reports (e.g., bank websites, brochures, and social media
advertisement tools). This paper is limited to the available data. Therefore, caution should
be taken before generalizing the study’s findings.
Author Contributions:
Conceptualization, F.S.S., A.B.A., Q.M.O. and H.A.; methodology, F.S.S.,
A.B.A., Q.M.O. and H.A.; software, F.S.S., A.B.A., Q.M.O. and H.A.; validation, F.S.S., A.B.A., Q.M.O.
and H.A.; formal analysis, F.S.S., A.B.A., Q.M.O. and H.A.; investigation, F.S.S., A.B.A., Q.M.O.
and H.A.; resources, F.S.S., A.B.A., Q.M.O. and H.A.; data curation, F.S.S., A.B.A., Q.M.O. and
H.A.; writing—original draft preparation, F.S.S., A.B.A., Q.M.O. and H.A.; writing—review and
editing, F.S.S., A.B.A., Q.M.O. and H.A.; visualization, F.S.S., A.B.A., Q.M.O. and H.A.; supervision,
F.S.S., A.B.A., Q.M.O. and H.A.; project administration, F.S.S., A.B.A., Q.M.O. and H.A.; funding
acquisition, F.S.S., A.B.A., Q.M.O. and H.A. All authors have read and agreed to the published version
of the manuscript.
Funding: This research received no external funding.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data Available upon request.
Conflicts of Interest: The authors declare no conflict of interest.
Int. J. Financial Stud. 2023,11, 115 18 of 25
Appendix A
Table A1. Keywords search items and list of AI-related disclosure terms.
Column A Column B
AI-related terms disclosure/keywords search item
Digital transformation, fintech, financial technology, modern
technology, AI digital strategy, latest technology, advanced
technology, AI computing technology, digital infrastructure,
digital library, electronic channel, electronic system, Internet
of things, 5g technology, advanced technical experiences,
digital platform, digital technology, 5g networks, blockchain,
smart connection, digital awareness, digital capabilities,
digital culture, digital economy, digital futuristic, digital
transition, augmented reality technology, technical platform,
web technology, machine learning, deep learning,
augmented intelligence, natural language processing (NLP)
Digital awareness, transformation,
and capabilities
Robotics, robo-advisors, automation, digital banking,
mobile banking, online banking, digital services, mobile
apps, electronic payment, Internet banking service, mobile
branches, mobile payment, robots, mobile ATMs, digital
payment, digital identity, smartphones, smart bank websites,
digital product, electronic service, intelligently analyses,
digital wallet, electronic wallet, mobile device service
AI application, product, service,
and process
Information security, cyber security, electronic security, it
risks security, electronic security policies, card security,
cyber risk, electronic security, cybercrime, bank electronic
security, customized electronic security methods, cyber
breach, cyber resiliency, defense technology, cyber
intelligence, electronic attack, global security, information
security breaches, security vulnerabilities
AI information challenges and
cyber security threats
Appendix B
Table A2.
AI-related terms disclosure frequency related to digital awareness, transformation, and
capabilities.
AI Digital Awareness, Transformation, and Capabilities Frequency
Digital transformation 143
Fintech 130
Financial technology 85
Modern technology 13
AI 13
Digital strategy 10
Latest technology 10
Advanced technology 8
AI technology computing 4
Digital infrastructure 3
Digital library 3
Electronic channel 3
Electronic system 3
Int. J. Financial Stud. 2023,11, 115 19 of 25
Table A2. Cont.
AI Digital Awareness, Transformation, and Capabilities Frequency
Internet of things (IoT) 3
Machine learning 3
Each of these words: 5g technology, advanced technical
experiences, digital platform, digital technology, 5g networks,
blockchain, and smart connection
2
Digital capabilities, digital culture, digital economy, digital
futuristic, digital transition, augmented reality technology,
technical platform, and web technology
1
Sub-category total 457
Total words 1925
Percentage 0.24
Table A3.
AI-related terms disclosure frequency related to AI applications, products, services,
and processes.
AI Application, Product, Service, and Process Frequency
Robotic automation 140
Digital banking 85
Mobile banking 80
Online banking 75
Digital services 64
Mobile app 48
Electronic payment 34
Internet banking service 20
Mobile branch 16
Mobile payment 16
Robotic/robot 16
Mobile ATM 13
Digital payment 11
Digital identity 6
Smartphone 6
Smart bank website 4
Digital products, electronic services, intelligent analyses 2
Digital wallet, electronic wallet, mobile device service 1
Sub-category total 644
Total—AI-related terms/words 1925
Percentage 0.33
Int. J. Financial Stud. 2023,11, 115 20 of 25
Table A4.
AI-related terms disclosure related to AI information challenges and cyber security threats.
AI Information Challenges and Cyber Security Threats Frequency
Information security 523
Cyber security 138
Electronic security risks 37
IT security 33
Electronic banking services 31
Electronic security policies 24
Card security 9
Cyber risk 6
Electronic security 5
Cybercrime, bank electronic security, customized electronic security
methods, cyber breach, cyber resiliency, and defense technology 2
Cyber intelligence, electronic attacks, information security breaches,
and security vulnerabilities 1
Sub-total—AI-related terms/words 824
Total—AI-related terms/words 1925
Percentage 43%
Appendix C
Table A5.
The OLS regressions of AI disclosure on financial performance before and during
COVID-19.
Before COVID-19 During COVID-19
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
ROA ROE PE NII TEXP ROA ROE PE NII TEXP
AIFREC 0.00369 ** 0.0266 ** −0.0237 0.0246 −0.00351 * 0.00734 ** 0.0570 *** 0.143 ** 0.0229 −0.00565 *
(2.25) (1.05) (−0.23) (0.26) (−2.05) (−2.04) (−1.93) (2.30) (0.80) (−2.12)
LASHR −1.695 *** −13.67 *** 23.85 −4.252 *** −0.195 −1.140 *** −10.66 *** −4.701 −1.968 *** −0.00448
(−3.39) (−3.19) (1.37) (−4.33) (−0.57) (−3.80) (−4.33) (−0.91) (−2.89) (−0.02)
DEBTH −0.0397 0.127 −0.393 0.0202 −0.0621 *** −0.0446 ** 0.352 ** 0.132 0.0998 ** −0.0220
(−1.50) (0.56) (−0.43) (0.39) (−3.44) (−2.41) (2.32) (0.41) (2.37) (−1.45)
FORSH 1.331 ** 9.133 * −20.27 0.217 0.696 * 1.452 ** 14.92 *** −13.25 0.0715 0.973 *
(2.30) (1.84) (−1.00) (0.19) (1.76) (2.34) (2.93) (−1.23) (0.05) (1.91)
BDSIZ 0.0417 0.145 −1.989 0.537 *** 0.0338 0.0141 0.0912 0.695 0.327 *** −0.00483
(0.61) (0.25) (−0.83) (3.99) (0.72) (0.49) (0.38) (1.39) (4.98) (−0.20)
INDPB −0.736 −6.069 31.93 1.241 −0.169 −1.380 *** −15.17 *** −11.18 −1.740 −0.0312
(−1.19) (−1.14) (1.48) (1.02) (−0.40) (−2.71) (−3.63) (−1.27) (−1.50) (−0.07)
BKSIZ −0.296 −2.087 −5.057 −1.201 *** 0.802 *** 0.0766 −0.246 1.311 −0.309 0.154
(−1.42) (−1.17) (−0.69) (−2.92) (5.62) (0.58) (−0.22) (0.57) (−1.02) (1.41)
BRNCH 0.0123 *** 0.104 *** 0.0812 0.0750 *** 0.00269 0.00354 0.0624 *** 0.0193 0.0403 *** 0.0112 ***
(2.98) (2.94) (0.56) (9.25) (0.95) (1.25) (2.68) (0.39) (6.25) (4.81)
BKAGE −0.0270 *** −0.234 *** 0.255 −0.140 *** 0.00335 −0.0123 ** −0.171 *** −0.0276 −0.0705 *** 0.00929 **
(−3.22) (−3.25) (0.87) (−8.48) (0.58) (−2.40) (−4.07) (−0.31) (−6.05) (2.21)
_cons 11.76 ** 52.21 139.1 39.35 *** 5.314 4.716 −1.974 −29.81 14.73 ** 15.35 ***
(2.36) (1.22) (0.80) (4.02) (1.56) (1.62) (−0.08) (−0.59) (2.22) (6.43)
N45 45 45 45 45 82 82 82 82 82
R20.490 0.518 0.207 0.851 0.951 0.452 0.489 0.215 0.678 0.891
adj. R20.358 0.394 0.104 0.812 0.939 0.384 0.425 0.117 0.637 0.878
*p< 0.10, ** p< 0.05, *** p< 0.01.
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