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Spatial Limitations: Banks operating under the supervision of the Central Bank of Iraq (government and private banks).

(Bugalla et al., 2012) ⁵ “The New Model of Governance and Risk Management for Financial Institutions.”

This study proposed a new governance and risk management framework for financial institutions consisting of four components designed to enhance risk disclosure and increase stock value through improved control and management practices. H1:

Smart governance has a positive impact on reducing financial risks under artificial intelligence.

(McKinsey & Company, 2025) ¹⁶ – This study outlined essential mechanisms to help financial institutions regulate the use of generative AI within strict governance frameworks. The analysis involved the case studies on international banking institutions and it was determined that both financial and regulatory risks involved in generative models can be significantly reduced through the application of clear-cut regulatory frameworks and the development of specialized human skills. H2:

Smart governance standards have a positive impact on reducing financial risks under artificial intelligence.

(Fritz, 2022) ⁹ – The current study aimed at formulating a systematic government structure into the assimilation of artificial intelligence models in the risk management practice of financial institutions. Based on extensive literature reviews and practical experience gained during the work of several European banks, the results prove that strong governance requires the development of clear policies regulating the risk assessment process, strict standards of testing the models, and the mechanisms aimed to maintain transparency and readability. H3:

Smart governance policies have a positive impact on reducing financial risks under artificial intelligence.

(Moridu, 2023) ¹⁸ – “The Role of Corporate Governance in Managing Financial Risks in Companies Listed on the West Bank Stock Exchange, Indonesia.”

This research study has considered the effects of the governance-related mechanisms, i.e., board independence, the quality of the audit committee and internal controls on the decision-making within the scope of risk management. The results indicate that a strong corporate governance greatly increases an ability of a firm to detect and assess financial risks, which contributes to their improved performance and long-term sustainability. H4:

Smart governance practices have a positive impact on reducing financial risks under artificial intelligence.

(Naguib, 2024) ¹⁹ – The paper examined how information technology (IT) and data governance impacted the performance of digital systems. The data were obtained by using quantitative surveys in the banking institutions and technology companies. The findings demonstrated that there is a positive relationship between advanced information governance and accuracy in financial decision-making to reduce financial risks. H5:

Smart information governance has a positive impact on reducing financial risks under artificial intelligence.

(Xinxian, 2022) ²⁵ – The current paper suggested early warning model based on financial risk management using convolutional neural networks (CNNs). The proposed model based on a large amount of Chinese financial data was faster and more accurate in prediction than the traditional approaches, thus allowing the implementation of intelligent technologies to allow financial institutions to mitigate risks beforehand. H6:

Smart governance technologies have a positive impact on reducing financial risks under artificial intelligence.

(Abid et al., 2021) ¹ – This paper has reviewed the correlation between the risk governance practices and risk taking behavior in Asian banks. It also highlighted the importance of leadership acumen in the enhancement of transparency and decision-making efficiency. The results showed that the managerial competence has a direct impact on the effectiveness of governance and financial risk management. H7:

Smart governance skills have a positive impact on reducing financial risks under artificial intelligence.

1. Smart Governance

Smart governance refers to the application of digital technologies and technological advances such as artificial intelligence (AI), the Internet of Things (IoT), and big data, to improve the efficiency of the government, promote transparency and accountability, and allow citizens to be involved in the decision-making process. Therefore, this paradigm leads to the achievement of the goals of sustainable development and the quality of life increase (Jiang, 2021; Liang, Liu & Wang, 2023; Dameri, 2017). ^{7, 11, 15}

Empirical studies have agreed that smart governance consists of several critical dimensions, that is, standards, policies, practices, information, technologies, as well as competencies. All dimensions play a vital and critical role in the development and execution of smart governance models. These dimensions must be synthesized in order to be able to face complex administrative challenges as well as to enhance sustainable development. The key dimensions are outlined below:

Standards:

The invention of norms and rules that introduce uniformity and quality in the public services is this dimension. Standards aid in compatibility and integration of various systems and platforms that is extremely important in good governance (Melati & Janissek, 2020; Gaulė, 2014). ^{10, 17}

Policies:

Policies are road maps or blueprints that assist in guiding both decision-making and implementation of intelligent governance. They aim at promoting transparency, accountability and citizen participation according to the United Nations Sustainable Development Goals (Erza et al., 2022; Wahab et al., 2020). ^{8, 24}

Practices:

The adoption of new practices aimed at improving the delivery of the public services is a part of smart governance. These are e-governance programs and participative governance system, which promote collaboration among actors and besides ease decision making processes (Gaulė, 2014; Lei, 2019). ^{10, 14}

Information:

Smart governance involves the use of information, which is a constituent of the informed decision-making and strategic planning. Effective information management equips the governments to respond appropriately to the dynamics of various situations and demands by the society (Wahab et al., 2020; Bokolo & Petersen, 2019). ^{4, 24}

Technologies:

Applying technologies and in particular Information and Communication Technology (ICT) would be applicable in transforming the model of governance. They may be automated to offer advanced data analytics and make smart infrastructure development in the populace and monetary institutions (Bokolo & Petersen, 2019; Azambuja et al., 2020). ^{4, 2}

Skills:

This dimension also comprises the skills and expertise that the employees of the public sector are expected to possess so that they may be in a position to design, implement and maintain smart governance initiatives in a successful manner. The perpetual training and capacity building programmes are required with the view of ensuring that the staff can be able to apply the new emerging technologies (Júnior et al., 2020; Scholl & Scholl, 2014). ^{12, 21}

2. Financial Risk Management

Financial risk management can be defined as the scientific approach of identifying, reviewing and minimizing financial risks linked to the financial operations of an organization, and its objective is to mitigate the possible losses as a result of the changes in the variables such as fluctuations in the asset prices, credit risks, and other causes of exogenous shocks which can also include the occurrence of natural disasters (Sari & Indrabudiman, 2024). ²⁰

The principal categories of financial risks are outlined below:

Market risk refers to the changes that occur in the portfolio value of an investment due to the market fluctuations in prices or volatility. This means that any financial markets entity is automatically subjected to this type of risk. Financial institutions increasingly rely on AI technologies to enhance market risk assessment and prediction, thereby improving management efficiency (Vesna, 2021). ²³

Credit risk is the potential economic loss resulting from a counterparty’s failure to meet contractual obligations. The risks are the expected outcomes that will occur in the event of a default by the borrower or a decline in its credit quality. Machine-learning algorithms are used to predict credit events and measure the likelihood of default, therefore improving the accuracy and the effectiveness of the credit-risk management (Vesna, 2021; Sari &Indrabudiman, 2024). ^{20, 23}

Operational risks are often in the form of future losses due to system downtime, human mistakes, fraud, or bad internal process failures. Artificial intelligence implementation in this context is meant to support organizations in the overall operational risk management process by processing large amounts of data, automating classification efforts, and finding patterns of anomalies to eliminate risks to both internal and external stakeholders (Vesna, 2021). ²³

The need to enhance investment portfolio management continues to increase and require the use of AI-driven tools. Those technologies enable the investors to manage the risks and returns more efficiently, to align the investment strategies with the institutional goals, and reduce their vulnerability to market fluctuations (Sari & Indrabudiman, 2024). ²⁰

The target population includes the banks of Iraq which are either in the form of the public bank, the private bank or the foreign bank, under the supervision of the Central Bank of Iraq. The Central Bank is the key player that is involved in making governance policies and reducing financial risk issues that relate to the implementation of new financial technologies. The sample of the study was limited to indicators and financial records in the framework of the Central Bank of Iraq related to governance and risk management during the time frame of 2023-2024.

The study heavily relied on secondary data that was collected through authoritative and verifiable reports published by the Central Bank of Iraq, as well as other international repositories, like the Iraq Digital Report 2024.

Data on the main indicators that include regulatory frameworks, digital policies and regulations, e-wallet penetration, number of payment card users, and the extent of digital infrastructure (ATM and POS terminals) has been provided in Table 1. It also covers IT training programmes and banking systems. The period under analysis was 2023-2024, and data were analysed to determine progress. The given approach provides a precise and objective data collection mechanism because it will be based only on official sources of data that are credible and publicly available, which will also increase the credibility of the final results.

The results presented in Table 1 reveal several key developments

Analysis of the Results of Smart Governance Dimensions under Artificial Intelligence

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Standards: The Central Bank of Iraq has strengthened its regulatory frameworks and national programs to strengthen governance and digital finance. It can be noted that during 2023-2024, the number of licensed electronic payment service providers increased by two, to reach seventeen, which indicates the introduction of stricter compliance requirements. This rise is an indicator of more stricter enforcement of standards according to which, only those providers are granted a license complying with the principles of governance, which promotes trust and transparency and reduces the likelihood of fraud or systemic financial instability.

Analysis of Financial Risk Management under Artificial Intelligence 1.

Market Risks: The market risk is the fluctuation in the interest rates and the general financial market forces which could influence the lending capacity of the banks and financial stability. Non-performing loans ratio decreased to 7.2 per cent in 2024, a decrease compared to the high rates experienced in 2023. This degradation implies that banks have increased their ability to handle market volatility and reduce the harmful impacts, and the overall stability in the sector has been enhanced, and the probability of systemic defaults has been reduced.

1.

More stringent and open regulatory frameworks of smart governance should be set by the Central Bank of Iraq and financial institutions, and they need to be periodically reviewed to keep the pace with technological progress, as well as to reduce the risks of fraud and financial instabilities to the bare minimum.