Nada Kaki Bira (1)
General Background: Rapid technological advancements are continuously transforming the accounting and financial environment through enhanced data processing and analytical capabilities. Specific Background: As business operations grow increasingly complex, conventional control mechanisms often struggle to manage unusual transactions and mitigate operational risks effectively. Knowledge Gap: Limited research comprehensively examines how artificial intelligence technologies integrate with modern anti-bribery frameworks to improve financial reporting outcomes. Aims: This study examines the role of artificial intelligence technologies in improving accounting information quality and disclosure under the requirements of ISO 37001:2025. Results: Findings indicate significant positive correlations and direct effects between artificial intelligence technologies and both accounting information quality and disclosure, with ISO 37001:2025 requirements serving as a partial mediator for information quality and a full mediator for disclosure. Novelty: The integration of artificial intelligence analytics with the specific compliance and anti-bribery standards of ISO 37001:2025 within the banking sector provides a unique regulatory and technological synergy. Implications: Financial institutions can substantially strengthen their internal controls, transparency, and reporting reliability by systematically deploying intelligent applications alongside anti-bribery management systems.
Key Findings Highlights
Artificial intelligence technologies demonstrate statistically significant positive effects on both accounting information quality and financial disclosure standards.
The requirements of ISO 37001:2025 act as a critical mediator, providing full mediation for accounting disclosure and partial mediation for information quality.
Expanding intelligent applications into predictive analytics and continuous transaction monitoring significantly reinforces organizational control and transparency.
Keywords Artificial Intelligence Technologies, Accounting Information Quality, Accounting Disclosure, ISO 37001:2025, Anti-Bribery
Rapid advances in artificial intelligence technologies have brought tangible changes to the accounting environment through their ability to process large volumes of data, detect patterns and deviations, and analyze transactions more quickly and accurately . Their use is no longer confined to automating routine procedures; it now extends to supporting the preparation and analysis of accounting information and strengthening accounting systems’ ability to provide users with more relevant and reliable information .
Accounting information quality and the level of its disclosure are fundamental to effective financial reporting because they support decisions, promote transparency, and reduce information asymmetry . As transactions grow in volume and complexity, traditional control and verification methods have become less capable of addressing the risks associated with unusual transactions or instances of manipulation and concealment . This has increased the need for intelligent technologies capable of continuous analysis and the early detection of abnormal indicators .
ISO 37001:2025 on anti-bribery management systems provides a structured framework for strengthening integrity, control, and bribery risk management through requirements relating to risk assessment, financial and non-financial controls, due diligence, documentation, monitoring, and corrective action . Artificial intelligence technologies can therefore support the implementation of these requirements, particularly in analyzing accounting data, detecting suspicious transactions, and improving information accuracy and disclosure transparency .
Against this background, the present study seeks to examine the role of artificial intelligence technologies in enhancing accounting information quality and disclosure under the requirements of ISO 37001:2025 . It identifies how the analytical and control capabilities of these technologies integrate with the requirements of an anti-bribery management system, and examines the implications for accounting information quality and disclosure levels, thereby supporting transparency and strengthening the reliability of the accounting and control environment .
2-1 Research Problem
The growing use of artificial intelligence technologies in accounting systems has increased the capacity to process and analyze data and detect unusual patterns . However, the practical benefits of these technologies for accounting information quality and disclosure levels remain linked to their integration with control frameworks that support transparency and integrity and reduce the risks of manipulation and bribery . In this context, the requirements of ISO 37001:2025 offer a framework that can strengthen the effectiveness of controls and control procedures, raising the question of how far artificial intelligence technologies can support these requirements and how this is reflected in accounting information quality and disclosure .
Accordingly, the research problem can be expressed through the following main question:
What role do artificial intelligence technologies play in enhancing accounting information quality and disclosure under the requirements of ISO 37001:2025?
The following questions arise from this main question:
2-2 Significance of the Study
The significance of this study lies in its examination of a contemporary topic that brings together artificial intelligence technologies, accounting information quality, and disclosure within a control framework associated with the requirements of ISO 37001:2025 . This is particularly relevant given the growing need for accounting systems that can better analyze data, detect unusual transactions, and enhance transparency and reliability . The study also clarifies the potential use of intelligent technologies to support anti-bribery controls and procedures and reduce manipulation risks, thereby strengthening confidence in the accounting information and disclosures issued by economic entities and keeping pace with recent developments in the accounting and control environment .
2-3 Research Objectives
2-4 Hypothesized Research Model
Based on the research problem and objectives, the hypothesized model describes the relationship between artificial intelligence technologies as the independent variable and accounting information quality and disclosure as the two dependent variables . The requirements of ISO 37001:2025 serve as a mediating variable, reflecting the control and organizational role through which artificial intelligence technologies can enhance information quality and disclosure transparency .
The model can be represented as follows:
Figure 1.
2-5 Research Hypotheses
Based on the research problem, objectives, and proposed hypothesized model, the research hypotheses were formulated as follows:
First main hypothesis: “There is a statistically significant correlation between artificial intelligence technologies and accounting information quality and disclosure . ”
The following two sub-hypotheses are derived from this hypothesis:
Second main hypothesis: “Artificial intelligence technologies have a statistically significant effect on accounting information quality and disclosure . ”
Third main hypothesis: “Artificial intelligence technologies have a statistically significant effect on the implementation of ISO 37001:2025 requirements . ”
Fourth main hypothesis: “The implementation of ISO 37001:2025 requirements has a statistically significant effect on accounting information quality and disclosure . ”
Fifth main hypothesis:
The requirements of ISO 37001:2025 play a statistically significant mediating role in the relationship between artificial intelligence technologies and both accounting information quality and disclosure .
2-6 Research Population and Sample
The research population comprised employees of several banks listed on the Iraq Stock Exchange and operating in the Kurdistan Region of Iraq: Erbil Bank for Investment and Finance, Kurdistan International Islamic Bank for Investment and Development, and Cihan Bank for Islamic Investment and Finance . These banks were selected because they provide a suitable setting for studying the research variables and are subject to disclosure and control requirements as joint-stock companies listed on the Iraq Stock Exchange .
The study used purposive sampling to select respondents from the accounting and finance, internal audit, control and compliance, risk management, information systems, and information technology departments of the sampled banks . Their duties are directly related to preparing and disclosing accounting information, using modern technologies, and implementing the controls and procedures relevant to the research topic .
2-7 Research Instrument
The study used a questionnaire as its main data collection instrument . It was designed based on the literature and previous studies relevant to the research variables, in line with the banking environment and the requirements of ISO 37001:2025 . The questionnaire comprised items grouped into dimensions representing artificial intelligence technologies, ISO 37001:2025 requirements, accounting information quality, and accounting information disclosure .
A five-point Likert scale was used to measure respondents’ answers, ranging from (1) strongly disagree to (5) strongly agree . This allows respondents’ attitudes toward the research items and variables to be measured quantitatively and subjected to statistical analysis . The questionnaire is also submitted to reviewers and specialists in accounting, auditing, management, and information systems to assess the wording of its items and their suitability for the research objectives and variables before its final form is adopted .
2-8 Statistical Methods
The study employed statistical methods suited to the data and research hypotheses, using appropriate statistical software . The principal methods were as follows:
A statistical significance level of (0 . 05) is adopted to assess the significance of the results and test the research hypotheses .
3 . Previous Studies
3-1 Artificial Intelligence and Accounting Information Quality
“Artificial Intelligence and the Quality of Accounting Information in Palestinian Industrial Companies”
This study aimed to determine the effect of adopting artificial intelligence technologies expert systems, machine learning, neural networks, and algorithms on improving accounting information quality, particularly relevance, faithful representation, and verifiability . It adopted a descriptive analytical approach and covered a sample of (326) employees from 13 industrial companies listed on the Palestine Exchange . The results showed a statistically significant positive effect of artificial intelligence technologies on accounting information quality, as well as improvements in the accuracy of results, the speed of data processing, and the detection of certain fraudulent practices in financial statements . This study informs the present research by confirming the direct relationship between the use of artificial intelligence technologies and the qualitative characteristics of accounting information .
“Impact of Artificial Intelligence on the Performance and Quality of Accounting Information Systems and Accuracy of Financial Data Reporting”
This study examined the role of artificial intelligence in improving the quality of accounting information systems and the accuracy of financial data reporting, while investigating the relationship between accounting information system quality, the internal control system, and artificial intelligence . It used data from (566) accountants and financial officers in India and employed partial least squares structural equation modeling (PLS-SEM) to test the proposed model . The results showed that artificial intelligence has a significant positive effect on accounting information system quality and improves the accuracy of financial reports and data . They also highlighted the importance of relevance, accuracy, and timeliness in determining accounting system quality . These findings align with the present study’s view of artificial intelligence technologies as a factor that can enhance the quality and reliability of accounting information .
Both studies clearly agree on the positive role of artificial intelligence technologies in developing accounting data processing and improving its accuracy, relevance, and reliability . However, they focused mainly on information quality or accounting information systems without examining this role within an integrated framework encompassing information quality, disclosure, and the requirements of the ISO 37001:2025 anti-bribery management system, which the present study seeks to address .
3-2 Artificial Intelligence, Disclosure, and Transparency
Entitled “Artificial Intelligence and the Quality of Corporate Accounting Information Disclosure,” this study aimed to test the effect of adopting artificial intelligence technologies on the quality of corporate accounting information disclosure . It used data from companies listed on China’s Shenzhen Stock Exchange during 2014–2023 . The results showed that adopting artificial intelligence significantly improves accounting disclosure quality, with a more pronounced effect among small and medium-sized enterprises and high-technology companies . The study also found that improvements in the business environment and increases in corporate growth potential strengthen the positive effect of artificial intelligence on disclosure quality . This study informs the present research by confirming the capacity of artificial intelligence technologies to enhance the quality of disclosed information and reduce shortcomings in the accounting disclosure process .
“Artificial Intelligence and Financial Statement Transparency: The Moderating Role of Accounting Information Systems’ Reliability”
This study examined the effects of artificial intelligence technologies—expert systems, machine learning, and neural networks—on financial statement transparency through voluntary disclosure, while testing the moderating role of accounting information system reliability . It adopted a quantitative cross-sectional approach, collected data from (375) respondents using a structured questionnaire, and employed partial least squares structural equation modeling (PLS-SEM) . The results showed a positive effect of artificial intelligence technologies on voluntary disclosure and financial transparency, with neural networks exerting the strongest influence . They also demonstrated the importance of accounting information system reliability in strengthening this relationship . These findings align with the present study’s focus on linking intelligent technologies to improved disclosure and transparency in the accounting environment .
Both studies show that the use of artificial intelligence technologies extends beyond improving accounting data processing to enhancing disclosure quality and financial information transparency . However, neither examined this role within the requirements of the ISO 37001:2025 anti-bribery management system, an area the present study seeks to cover .
3-3 Anti-Bribery Systems, Compliance, and Reporting Quality
“Corruption at Rolls-Royce: Can It Happen Again”
This study aimed to analyze the corruption case at Rolls-Royce, its global anti-bribery and corruption manual, and its annual report to assess the adequacy of its corruption prevention strategies and procedures . It adopted a legal case study approach based on the deferred prosecution agreement with the United Kingdom’s Serious Fraud Office and used ISO 37001 as a theoretical framework for analysis . The findings identified shortcomings in several anti-bribery procedures relative to the standard’s requirements, particularly measurable objectives, training, the independence of the compliance function, due diligence, and reporting and investigation mechanisms . The study also emphasized the importance of anti-bribery systems in strengthening internal controls and reducing opportunities for corruption . It informs the present research by explaining the control role of ISO 37001 requirements and their potential to create a more disciplined and reliable environment for information and reporting .
2 . Githaiga (2025)
“Corporate Anti-Corruption Disclosure and Earnings Management: Evidence from East Africa Community”
This study aimed to test the effect of corporate anti-corruption disclosure on earnings management among listed companies in East African Community countries . The sample comprised (58) listed companies during 2013–2022 . The study used an anti-corruption disclosure checklist based on the literature and GRI 205, and applied ordinary least squares to test its hypotheses . The results showed a low level of anti-corruption disclosure among the sampled companies and an inverse relationship between anti-corruption disclosure and earnings management . This suggests that greater transparency and disclosure relating to anti-corruption can help limit practices that undermine financial reporting reliability . The study informs the present research by supporting the link between anti-corruption practices, transparency, and the quality of accounting information and reports .
Both studies show that effective anti-bribery and anti-corruption systems extend beyond legal or ethical considerations to strengthen internal controls, compliance, and transparency and limit practices that may undermine financial reporting reliability . However, they did not examine the role of artificial intelligence technologies in supporting these systems and their combined implications for accounting information quality and disclosure, an area the present study seeks to cover .
3-4 Research Gap
The review of previous studies shows that most examined the relationship between artificial intelligence technologies and accounting information quality, or disclosure and transparency, separately . Other studies focused on anti-bribery and compliance without integrating them with intelligent applications in the accounting environment . The research gap therefore lies in the limited number of studies that have comprehensively examined the role of artificial intelligence technologies in enhancing accounting information quality and disclosure under the requirements of ISO 37001:2025 . This is the gap the present study seeks to address .
Here, we explain what previous studies contributed and what they did not address .
4 . Theoretical Framework
1-4 Artificial Intelligence Technologies in the Accounting Environment
The accounting environment has increasingly shifted toward advanced digital technologies, particularly artificial intelligence, because of their capacity to process and analyze data, automate repetitive tasks, and generate information that supports decision-making . This has broadened their use in accounting and finance, moving their role beyond routine procedures to supporting analysis and control and providing more useful insights for information users . IFAC notes that artificial intelligence enables accounting and finance professionals to streamline repetitive processes and focus more on analysis and the provision of decision-support information . (IFAC, 2023: 60)
1-1-4 The Concept of Artificial Intelligence
Artificial intelligence is a broad concept associated with the capacity of machine-based systems to process inputs and draw inferences from them to generate predictions, recommendations, decisions, or content . The Organisation for Economic Co-operation and Development (OECD) defines an artificial intelligence system as a machine-based system that, for explicit or implicit objectives, infers from the inputs it receives how to generate outputs that can influence physical or virtual environments . (OECD, 2024: 128) . The U . S . National Institute of Standards and Technology (NIST) also defines it as a machine-based system capable, for specified objectives, of making predictions, recommendations, or decisions that affect real or virtual environments . (Booth et al . , 2024: 22) . Accordingly, artificial intelligence in the accounting environment can be understood as a set of technologies and systems capable of analyzing accounting data, learning from its patterns, and deriving findings and predictions that improve information processing and support decisions and control functions .
2-1-4 Artificial Intelligence Technologies Relevant to Accounting
Various artificial intelligence technologies can be used in the accounting environment, depending on the nature of the data and the operations to be performed . These technologies automate processing, analyze large volumes of data, detect unusual patterns, and support prediction and decision-making . Professional literature points to the growing use of machine learning, natural language processing, and intelligent analytics in accounting and auditing because they enable the processing of structured and unstructured data and improve the efficiency of financial and control procedures . (Qatawneh, 2025: 1398) . The principal technologies relevant to accounting include the following:
Thus, the importance of artificial intelligence technologies in accounting extends beyond routine automation to data analysis, risk detection, more accurate processing, and stronger control capabilities . This gives them direct relevance to accounting information quality and disclosure and to the control and compliance requirements addressed in the present study .
3-1-4 Artificial Intelligence Technologies and Accounting Information Quality and Disclosure
Artificial intelligence technologies have become influential tools in developing the accounting environment . Their importance extends beyond automating procedures to improving data processing, analysis, and verification, with implications for the qualitative characteristics of accounting information and disclosure quality . Awwad et al . (2025: 1418) found that the use of expert systems, machine learning, neural networks, and algorithms positively affects accounting information quality characteristics, particularly relevance, faithful representation, and verifiability, while also producing more accurate, timely, and reliable results .
The role of artificial intelligence technologies in enhancing accounting information quality is evident in their ability to process large volumes of data continuously, reduce errors associated with manual processing, detect unusual relationships and patterns, and provide timely information . In this regard, Johri (2025: 1096) found that artificial intelligence positively affects accounting information system quality and the accuracy of financial information reporting, while highlighting the importance of relevance, accuracy, and timeliness in determining accounting system quality .
The effects of these technologies extend beyond the quality of the information produced to accounting disclosure and transparency . Intelligent analytical tools help examine the data and information underlying reports, identify omissions or inconsistencies, and improve the accuracy and timeliness of disclosed information . Yang & Zhou (2025: 85) found that adopting artificial intelligence significantly enhances the quality of corporate accounting information disclosure, confirming that technological development can support improvements in a company’s information environment .
Artificial intelligence technologies can also support financial reporting transparency by improving transaction analysis, enabling the early detection of unusual transactions, and providing clearer information to report users . Alslaibi et al . (2026: 19) demonstrated the importance of artificial intelligence technologies in enhancing financial statement transparency, while emphasizing that accounting information system reliability is an important factor in the strength of this relationship .
The researcher considers that artificial intelligence technologies contribute to accounting information quality and disclosure by increasing processing accuracy, improving timeliness and relevance, strengthening verifiability, detecting unusual transactions, and enhancing disclosure transparency . Achieving these benefits, however, requires control and organizational safeguards that ensure data reliability and the sound use of technologies . This provides the basis for linking artificial intelligence with the requirements of ISO 37001:2025 in the present study .
5 . Accounting Information Quality and Disclosure
Accounting information is the main link between an economic entity and the users of its reports, as different parties rely on it to assess performance and financial position and make economic decisions . Its effectiveness does not arise from availability alone, but depends on characteristics that make it useful and able to represent economic reality appropriately, as well as on its presentation and disclosure in an appropriate amount and at the right time . Accounting information quality is associated with characteristics that enable information to meet users’ needs and fulfill the purpose for which it was prepared . (Boumakhlouf & Kebieche, 2024: 721)
1-5 The Concept of Accounting Information Quality
Accounting information quality refers to the extent to which financial information meets users’ needs and supports their economic decisions through qualitative characteristics that make it useful for decision-making . The fundamental characteristics are relevance and faithful representation, while usefulness is enhanced by comparability, verifiability, timeliness, and understandability . These characteristics improve information’s ability to represent economic phenomena and provide a more appropriate basis for decisions . This is consistent with the Conceptual Framework for Financial Reporting, which emphasizes that useful financial information must be relevant and faithfully represented, and that its usefulness is enhanced by comparability, verifiability, timeliness, and understandability . (IFRS Foundation, 2021: A25) .
Accordingly, accounting information quality is operationally defined in the present study as the ability of accounting information to meet users’ needs through relevance and faithful representation, enhanced by comparability, verifiability, timeliness, and understandability, thereby supporting decision-making and increasing the reliability of accounting reports .
2-5 Characteristics of Accounting Information Quality
Accounting information quality is a fundamental basis for an accounting system’s effectiveness in achieving its objectives, particularly amid rapid changes in the business environment and the growing need for accurate and reliable information to support economic decisions . Achieving this quality requires a set of fundamental qualitative characteristics that ensure information is suitable for use by the parties concerned . Table (1) presents the qualitative characteristics of financial information according to the IFRS Foundation’s Conceptual Framework for Financial Reporting and explains how each characteristic affects information’s usefulness for decision-making .
Table (1): Qualitative Characteristics of Accounting Information Quality
Source: Prepared by the researcher based on (IFRS Foundation, 2021: A25–30) .
3-5 The Concept of Accounting Disclosure
Accounting disclosure is one of the principal functions of an accounting system . It is the means through which financial and non-financial information is communicated to report users, helping them understand the economic entity’s circumstances and make appropriate decisions . Differences in users’ needs and interests have led to diverse views of disclosure and the absence of a single definition . (Wolk et al . , 2016: 101)
Hendriksen (1982: 405) describes disclosure as the presentation of necessary information about an economic entity to support the proper functioning of efficient capital markets . Kieso (1998: 1342) considers disclosure to involve presenting material facts that may affect information users’ assessments and decisions . Choi (2005: 1) also views disclosure as the process of communicating accounting measurement information to internal and external users .
The researcher views accounting disclosure as the process of presenting and communicating material and relevant information about an economic entity to its users in a timely, clear, and non-misleading manner, helping them assess performance and make economic decisions .
4-5 Characteristics of Effective Accounting Disclosure
Effective accounting disclosure is not achieved simply by providing a large amount of information . It depends on the quality of the information disclosed and its ability to meet users’ needs and support appropriate decisions . Disclosure should also be adequate, fair, and comprehensive, with comprehensiveness focusing on the quality and significance of information rather than its volume alone . Accordingly, the main characteristics of effective accounting disclosure are as follows: (Al-Mohareb et al . , 2025: 1446), (Matar, 2013: 17)
Effective accounting disclosure therefore combines relevance, materiality, timeliness, fair access, quality, and credibility, enhancing information’s usefulness for decision-making and increasing transparency and confidence in accounting reports .
6 . ISO 37001:2025
ISO 37001:2025 is an international framework for anti-bribery management systems . It aims to help economic entities establish an integrated system to prevent, detect, and respond to bribery, while supporting compliance with relevant legislation and requirements . The 2025 version is the standard’s second edition, issued in February 2025 to replace ISO 37001:2016 . It applies to economic entities of all sizes across the public, private, and not-for-profit sectors . (ISO, 2025: 3)
The standard is based on requirements and procedures covering an anti-bribery policy, leadership commitment, bribery risk assessment, due diligence, financial and non-financial controls, training, reporting, monitoring and investigation, corrective action, and continual improvement . These requirements are significant in the accounting environment because they provide a control framework that helps regulate transactions, promote integrity and transparency, and reduce the risks of unlawful practices . This makes the standard suitable for examining how artificial intelligence technologies support accounting information quality and disclosure .
1-6 Objectives of ISO 37001:2025
ISO 37001:2025 aims to establish an institutional framework that helps economic entities prevent, detect, and respond to bribery by promoting a culture of integrity and ethical conduct and providing the controls needed to manage bribery risks . It also supports compliance, protects the economic entity’s reputation, and strengthens stakeholder confidence . (ISO, 2025: 3–4) . Veselovska (2025: 1) indicates that anti-bribery management systems enhance accountability, transparency, and integrity, while supporting an ethical environment and increasing the economic entity’s credibility among stakeholders . Peltier-Rivest (2021: 433) also emphasizes that adopting the ISO 37001 framework provides preventive practices that help reduce corruption and improve internal control effectiveness .
2-6 Main Requirements of ISO 37001:2025
ISO 37001:2025 comprises interrelated requirements intended to establish an effective bribery risk management system . Its implementation extends beyond setting an anti-bribery policy to identifying risks, controls, responsibilities, and mechanisms for monitoring and improvement . The principal requirements can be summarized as follows: (ISO, 2025, Clauses 4–10), (Veselovska, 2025: 3)
These requirements are particularly important in the accounting environment, especially bribery risk assessment, due diligence, financial controls, monitoring, reporting, and internal auditing, because they relate directly to financial data and transactions . This provides scope for artificial intelligence technologies to support their implementation and improve their capacity to detect unusual transactions and potential risks .
6-3 The Role of Artificial Intelligence Technologies in Supporting ISO 37001:2025 Requirements
Artificial intelligence technologies can support the implementation of ISO 37001:2025 requirements through their ability to analyze large volumes of data and transactions, detect unusual patterns, and identify risk indicators more quickly and continuously . This role is particularly important in banking, where artificial intelligence and machine learning are used in know-your-customer and due diligence procedures, fraud and manipulation detection, and financial transaction monitoring . These applications clearly overlap with several control requirements of anti-bribery systems . (Turksen et al . , 2024: 361) .
Artificial intelligence contributes particularly to bribery risk assessment and due diligence . Machine learning algorithms can analyze historical data and the behavior of related parties and classify transactions by risk level, helping direct control procedures toward the most exposed cases . Odilla (2024: 2) showed that artificial intelligence tools used to combat corruption rely on machine learning and risk assessment methodologies to predict the risks of corrupt behavior and contract fraud and support decision-making . This is consistent with ISO 37001:2025 requirements to conduct and document due diligence on related parties, particularly in high-risk cases . (Nwaeze & Nwaeze, 2026: 8) .
Artificial intelligence technologies can also support financial and non-financial controls by continuously monitoring transactions, linking data from multiple sources, and detecting transactions that deviate from established patterns or contain indicators requiring examination . Turksen et al . (2024: 362) explain that artificial intelligence can help automate compliance processes and detect suspicious activities, provided that data quality and model transparency and interpretability are maintained . This is especially important given ISO 37001 requirements for controls over gifts, hospitality, facilitation payments, donations, and political contributions, as these transactions represent potential areas of bribery risk . (Nwaeze & Nwaeze, 2026: 19) .
Artificial intelligence also contributes to monitoring, early detection, and reporting . Predictive analytical tools help generate warning indicators for high-risk cases and direct the attention of compliance and internal audit functions to them . ISO 37001:2025 sets out an interconnected preventive system encompassing bribery risk assessment, due diligence, financial controls, leadership commitment, the independence of the anti-bribery function, and whistleblower protection . (Nwaeze & Nwaeze, 2026: 12) . Artificial intelligence can therefore be viewed as a supporting tool that increases the speed and effectiveness of implementing these requirements, rather than replacing professional judgment or human responsibility .
Integrating artificial intelligence technologies with ISO 37001:2025 requirements can support a shift from retrospective control to proactive control based on risk analysis and early detection . Artificial intelligence applications must be subject to transparency, accountability, and human oversight to avoid risks arising from bias or poor data quality . Recent literature emphasizes that artificial intelligence tools for combating corruption may produce unfair outcomes when the data or models used are unsuitable . (Odilla, 2024: 3) .
7- Empirical Analysis and Research Results
The empirical analysis extends the study’s theoretical framework by testing the hypothesized relationships among artificial intelligence technologies, ISO 37001:2025 requirements, accounting information quality, and disclosure in the banking environment . It draws on questionnaire data collected from the sample and analyzes them statistically to describe the research variables, test the hypotheses, and establish findings that identify the relationships and effects among the variables .
1-7 Description of the Research Sample
The study used a purposive sample of employees at Erbil Bank for Investment and Finance, Kurdistan International Islamic Bank, and Cihan Bank for Islamic Investment and Finance, all of which are listed on the Iraq Stock Exchange . The sample included staff in departments directly related to the research topic, particularly accounting and finance, internal audit, control and compliance, risk management, information systems, and information technology, because their knowledge and experience are appropriate for answering the questionnaire items .
To understand the nature of the sample and its suitability for testing the research variables, respondents’ characteristics are presented through demographic and employment indicators, including gender, age, educational attainment, specialization, job title, and years of experience . This helps provide a clear picture of respondents’ general characteristics and the relevance of their experience to the research topic .
Table (2): Description of Respondents’ Demographic Variables
Source: Prepared by the researcher based on questionnaire data .
2-7 Testing the Validity and Reliability of the Research Instrument
To ensure that the questionnaire was suitable for measuring the research variables, its validity and reliability were assessed before conducting the statistical tests of the research hypotheses . Face and content validity were assessed by submitting the questionnaire to reviewers specializing in accounting, auditing, and financial management to ensure that the items were clear and appropriate for the variables they were intended to measure . The necessary revisions were made in light of their comments .
Internal consistency validity was also assessed by calculating the correlations between each item and the total score of the variable to which it belonged . Positive and statistically significant correlations indicate that the items are consistent with the variables they are intended to measure .
The reliability of the research instrument is tested using Cronbach’s alpha for each questionnaire variable and for the instrument as a whole . A reliability coefficient of (0 . 70) or above is considered statistically acceptable, while higher values indicate good internal consistency among the scale items .
Table (3): Results of Research Instrument Reliability Testing Using Cronbach’s Alpha
Source: Prepared by the researcher based on the statistical analysis results .
Table (3) shows that Cronbach’s alpha coefficients for the research variables ranged from (0 . 828–0 . 891), while the reliability coefficient for the questionnaire as a whole was (0 . 924) . These values exceed the statistically acceptable threshold of (0 . 70), indicating a high level of internal consistency and reliability and the instrument’s suitability for statistical analysis and testing the research hypotheses .
3-7 Description and Assessment of the Artificial Intelligence Technologies Variable
To assess the level of artificial intelligence adoption in the sampled banks, respondents’ answers to this variable’s items were analyzed using the mean, standard deviation, and relative weight, along with a t-test against the hypothetical mean of (3) . Table (4) below presents the response level for each item, helping assess the current use of artificial intelligence technologies and identify areas with high or low levels of implementation .
Table (4): Descriptive Statistics and Hypothetical Mean Test Results for the Artificial Intelligence Technologies Variable
Table (4) shows that the artificial intelligence technologies variable had an overall mean of (3 . 40) and a relative weight of (68%), indicating a moderate level of implementation in the sampled banks . The item on using artificial intelligence to process and analyze financial and accounting data recorded the highest mean of (4 . 09), while the item on detecting unusual patterns and transactions recorded the lowest mean of (2 . 49) . The results indicate that artificial intelligence use is concentrated more heavily in data processing, automation, and system integration, with limited use of more advanced applications, particularly predictive analytics, unusual transaction detection, and decision support .
4-7 Description and Assessment of the Accounting Information Quality Variable
To assess accounting information quality in the sampled banks, respondents’ answers to this variable’s items were analyzed using the mean, standard deviation, and relative weight, along with a t-test against the hypothetical mean of (3) . Table (5) below shows the extent to which accounting information quality characteristics are present, helping identify strengths and shortcomings in the information used within the sampled banks .
Table (5): Descriptive Statistics and Hypothetical Mean Test Results for the Accounting Information Quality Variable
Table (5) shows that the accounting information quality variable had an overall mean of (3 . 56) and a relative weight of (71 . 13%), indicating relatively high quality in the sampled banks . The item on faithful and objective accounting information recorded the highest mean of (4 . 05), while the item on accuracy and freedom from material errors recorded the lowest mean of (2 . 67) . The results indicate good levels of relevance, faithful representation, comparability, and timeliness, alongside some shortcomings in accuracy and clarity . This calls for stronger verification and review procedures and improved information presentation methods .
5-7 Description and Assessment of the Accounting Disclosure Level Variable
To assess the level of accounting disclosure in the sampled banks, respondents’ answers to this variable’s items were analyzed using the mean, standard deviation, and relative weight, along with a t-test against the hypothetical mean of (3) . Table (6) below presents the extent to which accounting disclosure characteristics are present in terms of adequacy, relevance, clarity, timeliness, credibility, and transparency, helping assess current disclosure practices and identify their strengths and shortcomings .
Table (6): Descriptive Statistics and Hypothetical Mean Test Results for the Accounting Disclosure Level Variable
Table (6) shows that the accounting disclosure variable had an overall mean of (3 . 26) and a relative weight of (65 . 28%), indicating a moderate level of disclosure in the sampled banks . The item on timely accounting information disclosure recorded the highest mean of (3 . 90), while the item on transparency concerning risks, operations, and events with financial effects recorded the lowest mean of (2 . 69) . The results indicate an acceptable level of disclosure in terms of timeliness and relevance, alongside relative weaknesses in clarity, transparency, and adequacy . This reflects the need to develop disclosure practices that improve the comprehensiveness, credibility, and usefulness of information for financial report users .
6-7 Description and Assessment of the ISO 37001:2025 Requirements Variable
To assess the extent to which ISO 37001:2025 requirements are present in the sampled banks, respondents’ answers to this variable’s items were analyzed using the mean, standard deviation, and relative weight, along with a t-test against the hypothetical mean of (3) . The following table presents the implementation levels of requirements relating to bribery risk assessment, due diligence, financial and non-financial controls, reporting and investigation, awareness and training, monitoring, and continual improvement, helping assess the banks’ readiness to implement an effective anti-bribery management system .
Table (7): Descriptive Statistics and Hypothetical Mean Test Results for the ISO 37001:2025 Requirements Variable
Table (7) shows that ISO 37001:2025 requirements had an overall mean of (3 . 84) and a relative weight of (76 . 72%), indicating a high level of availability in the sampled banks . The item on bribery risk awareness and training programs recorded the highest mean of (4 . 38), followed by the item on examining and investigating suspected cases, with a mean of (4 . 26) . In contrast, the item on periodic monitoring and evaluation of anti-bribery procedure effectiveness recorded the lowest mean of (2 . 35), followed by the item stating that the bank applies non-financial controls to contracting, procurement, and the selection of business counterparties, with a mean of (2 . 47) . These findings indicate that the banks demonstrate a good level of commitment to anti-bribery procedures, while requiring stronger periodic monitoring and non-financial controls to support comprehensive implementation of the standard’s requirements .
7-7 Testing the Research Hypotheses
After describing and assessing the research variables, the hypotheses were tested to examine the relationships and effects proposed by the research model among artificial intelligence technologies, ISO 37001:2025 requirements, accounting information quality, and accounting disclosure . Correlations were first tested using Pearson’s correlation coefficient, followed by direct effects using regression analysis, and finally the indirect effect of ISO 37001:2025 requirements as a mediating variable . A significance level of (α ≤ 0 . 05) was adopted to assess the statistical significance of the results .
1-7-7 Testing Correlations among the Research Variables
This test aims to determine the direction and strength of relationships among the research variables using Pearson’s correlation coefficient . A relationship is considered statistically significant when its P-value is ≤ 0 . 05 . The sign of the correlation coefficient indicates the direction of the relationship, while its value reflects the strength of the relationship between the two variables .
Table (8): Results of Correlation Tests among the Research Variables
Source: Prepared by the researcher based on the statistical analysis results . Note: n=73, and the adopted significance level is α ≤ 0 . 05 .
Table (8) shows statistically significant positive correlations among the research variables at a significance level below (0 . 05) . The strongest relationship was between artificial intelligence technologies and ISO 37001:2025 requirements, with a correlation coefficient of (0 . 660), indicating that greater use of artificial intelligence technologies is associated with a higher level of availability of the standard’s requirements . The correlation between artificial intelligence and accounting information quality was (0 . 620), and that between artificial intelligence and accounting disclosure was (0 . 540), supporting acceptance of hypotheses H1a and H1b . The results also showed positive correlations between ISO 37001:2025 requirements and both accounting information quality and accounting disclosure, with coefficients of (0 . 580) and (0 . 610), respectively . This provides an initial basis for testing direct effects and the standard’s mediating role .
7-7-2 Testing Direct Effects among the Research Variables
After establishing the correlations, the direct effects specified in the research model are tested using regression analysis . Model significance is assessed through the F-value and its significance level . The significance of the independent variable’s effect is assessed through the standardized regression coefficient (β), the t-value, and the significance level, while the coefficient of determination (R²) represents the proportion of variation in the dependent variable explained by the independent variable .
Table (9): Results of Direct-Effect Tests among the Research Variables
Source: Prepared by the researcher based on the statistical analysis results . Note: Sample size n=73, and the adopted significance level is α ≤ 0 . 05 .
Table (9) shows that all direct effects were positive and statistically significant . The effect of artificial intelligence technologies on ISO 37001:2025 requirements recorded the highest effect coefficient (β=0 . 660), with explanatory power of 43 . 6% . Artificial intelligence technologies also explained approximately 38 . 4% of the variance in accounting information quality and 29 . 2% of the variance in accounting disclosure . The standard’s requirements explained approximately 33 . 6% of the variance in information quality and 37 . 2% of the variance in accounting disclosure, supporting acceptance of hypotheses H2a, H2b, H3, H4a, and H4b .
7-7-3 Testing the Mediating Role of ISO 37001:2025 Requirements
Testing mediation is an important part of the research model, as it seeks to determine whether ISO 37001:2025 requirements constitute a mechanism through which artificial intelligence technologies affect both accounting information quality and accounting disclosure . Establishing mediation requires more than observing a change in the direct-effect coefficient after introducing the mediator . It requires testing the indirect effect through bootstrapping and examining the 95% confidence interval . The indirect effect is significant when the confidence interval does not include zero .
Table (10): Results of Testing the Mediating Role of ISO 37001:2025 Requirements
Source: Prepared by the researcher based on the results of mediation analysis using bootstrapping .
Note: A 95% confidence interval and 5,000 bootstrap samples were used . The indirect effect is significant when the confidence interval does not include zero .
Table (10) shows that ISO 37001:2025 requirements mediate the effect of artificial intelligence technologies on the two dependent variables . For accounting information quality, the indirect effect was (0 . 200), and the confidence interval (0 . 057–0 . 376) did not include zero, while the direct effect of artificial intelligence remained at (0 . 420), indicating partial mediation . For accounting disclosure, the indirect effect was (0 . 297), with a confidence interval of (0 . 119–0 . 508), while the direct effect fell to (0 . 243) and approached the threshold of non-significance, indicating full mediation by the standard’s requirements .
7-7-4 Summary of Research Hypothesis Testing Results
In light of the correlation tests, direct-effect tests, and mediation analysis, the results of hypothesis testing can be summarized to show the extent to which the empirical findings support the hypothesized research model, as presented in Table (11) below .
Table (11): Summary of Research Hypothesis Testing Results
8-1 Conclusions
Based on the empirical results and research hypothesis tests, the following conclusions were reached:
8-2 Recommendations
Based on the study’s conclusions, the following recommendations can be made:
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