Artificial Intelligence (AI) is the utilization of computer systems and technology to imitate human intelligence. This intricate undertaking combines hardware and software components with the goal of replicating human thought processes. Accurately defining AI can be challenging, as many tasks that require intelligence, such as problem-solving, communicating, planning, programming, and driving, are performed by humans [1,2,3]^[1][2][3]. If a machine can perform these activities, it can be considered to possess AI. AI’s achievements rely on algorithms, statistical models, and programs that process and analyze extensive data [4,5,6,7,8]^{[4][5][6][7][8]}. Some common AI applications include expert systems, natural language processing, speech recognition, and machine vision. In recent years, there has been considerable interest in the potential of AI and robotics to revolutionize various sectors, including public and government domains.

Integrating AI into the financial industry will revolutionize financial services by enabling the development of new business models, personalized services, and cost savings [13]^[9]. AI’s analytical capabilities can enhance the quality of goods and services offered to customers, improving efficiency and reducing costs in compliance, fraud detection, and anti-money-laundering measures. Additionally, AI can drive innovation and transform financial service providers into data- and AI-based firms. However, AI’s potential impact on financial institutions and the United Nation’s Sustainable Development Goals (SDGs) [14]^[10] requires careful examination. Leveraging AI in alignment with the SDGs presents significant opportunities, particularly in the societal domain. Using AI responsibly and ethically is crucial to ensuring positive contributions to sustainable development and facilitating transparency and security measures. Integrating AI, AI ethics, and the SDGs is essential to fully harness AI’s potential and maximize economic, environmental, and societal benefits while minimizing ethical concerns. This calls for collaborative efforts that are inclusive, harmonious, and interdisciplinary, ensuring that technological advancements support human progress while safeguarding the environment and society as a whole. The Kingdom of Saudi Arabia (KSA) is renowned for its extensive and advanced financial industry in the Middle East [15]^[11]. Key players in this industry include Saudi National Bank (SNB), Al Rajhi Bank, Riyad Bank, and AlBilad Bank, all operating under the regulation of the Saudi Central Bank (SAMA). SAMA [16]^[12] has implemented measures, such as Islamic banking, to promote growth. Tadawul [17]^[13], the largest stock exchange in the Middle East and North Africa, is home to companies like Saudi Aramco and SABIC. SAMA and Tadawul are committed to incorporating technology-driven sustainability capabilities to enhance customer service. Undoubtedly, the Saudi financial sector is poised for significant growth in the coming years, supported by established standards. AI technologies are increasingly being deployed in finance, enhancing the competitive advantages of financial firms in the country. For instance, financial firms are developing AI predictive models to analyze historical data, mitigate risks, predict cash flow, refine credit scores, and detect fraud. Financial firms have contributed to the country’s economic prosperity as outlined in Saudi Vision 2030 [18]^[14]. However, Saudi Arabia’s IT infrastructure and technological advancements have not received sufficient attention due to its past reliance on oil.

2. The Current State of Deploying AI in the Financial Industry in KSA

AI is revolutionizing the global financial sector by automating regular tasks, optimizing efficiency, and increasing our understanding of customer behavior. KSA has seen an impressive recent upsurge in AI adoption in its financial sector. The industry has been quick to adopt AI technology. Swain and Gochhait’s study [20]^[15] investigated AI’s effects on Middle Eastern financial institutions, they carried out a literature review that explored topics related to AI integration, blockchain, cloud computing, and data security in Islamic banking. Additionally, they sought potential solutions to tackle any challenges. The study’s results indicated that the use of cloud computing flourished during the pandemic in Islamic banking and is still growing. Cloud services provide updates to secure data capture, storage, and interpretation processes. Their research concluded that cloud computing could be hugely beneficial in fortifying the structure and networks within Islamic banking. SAMA launched a regulatory sandbox initiative in 2018 as an AI implementation, allowing fintech companies to test innovative products and services in a safe environment; this encourages innovation and broadens the use of AI in the financial sector. During COVID-19, it became apparent that AI and IoT were essential in the banking sector. This resulted in urban financial institutions transitioning to using robotics and AI to automate banking processes, along with many fintech companies [16]^[12]. The study highlighted that banks are less likely to experience cyberattacks when using AI technologies and improve compliance. Furthermore, Saudi banks have applied AI technology to improve their services, such as chatbots for customer service inquiries and fraud detection systems. For instance, Saudi National Bank (SNB) implemented chatbots that are accessible 24/7 to provide customers with quick responses and an AI-based fraud detection system to analyze customer behavior and transactions. Many financial organizations increasingly use AI-powered customer service chatbots to provide personalized assistance. However, Al-Ghamdi and Al-Shehri [22]^[16] illustrated a shortage of skilled AI professionals and expertise, hindering its full implementation within the industry. They also noted the need for regulatory frameworks to ensure AI’s ethical and responsible use within the finance domain. 

3. Leveraging AI in the Saudi Financial Industry

3.1. Benefits and Limitations

AI technologies can potentially revolutionize the financial sector by enhancing efficiency, cutting costs, and improving the customer experience. However, they also present new risks and drawbacks that must be carefully considered. One of AI’s primary advantages in finance is its ability to process massive volumes of data and furnish insights that humans might overlook. These data can train ML algorithms, which can then predict future actions. Another benefit is that AI can automate repetitive or routine tasks and processes. Chatbots and robo-advisors are examples of such tools, which can respond to customer inquiries quickly and accurately [24^[17][18],25], freeing up human resources for more complex tasks. Additionally, AI technologies are useful for risk management and fraud detection because they analyze large amounts of data in real-time [24,25]^[17][18]. The earlier malicious activities are detected, the less likely financial institutions will suffer losses or damage to their reputation. In addition, customers’ experience can be improved with AI technologies. For example, chatbots and virtual assistants can give personalized recommendations according to individual needs and preferences [24^[17][18],25], which could lead to increased customer satisfaction and loyalty. Nevertheless, some limitations must be considered when deploying AI technologies in financial services. The first is biased decision-making due to inappropriate data used to train algorithms. To avoid this, financing organizations must ensure their data are diverse [24,25]^[17][18]. Data privacy and security breaches must be avoided; measures should be taken to ensure that data are securely stored against cyber threats [20]^[15]. Nonetheless, research has suggested that AI offers solutions that would have been impossible without such technology [25]^[18].

3.2. Challenges

Despite AI’s continuous evolution in the KSA financial sector, certain challenges must be tackled. One of the primary difficulties Saudi financial institutions face is the shortage of qualified AI specialists and experts. As reported by AlBarrak et al. [26]^[19] there is an insufficient number of AI professionals in KSA, which impedes the emergence and implementation of AI technologies in the financial sector. This lack of skilled professionals is exacerbated due to the high demand for AI expertise in other industries like healthcare and energy. Apart from this, the cost is another challenge financial institutions in KSA encounter when implementing AI technologies. AI technologies demand considerable investment in infrastructure, hardware, and software, which can be too costly for certain institutions, especially smaller ones [27]^[20]. Additionally, maintaining and upgrading AI systems can also be expensive. Data quality and availability are also major challenges financial institutions confront when applying AI technologies. Good-quality data are necessary for algorithms to be trained or developed; unfortunately, many financial entities struggle to obtain quality data because of data fragmentation, silos, and the absence of standardization [27,28]^[20][21]. Furthermore, financial institutions may need to establish regulatory frameworks that guarantee the ethical and responsible use of AI technologies within the sector. Insufficient clear directives and regulations create ambiguity and prohibit adoption and usage [28]^[21].

4. Ethical and Regulatory Considerations

4.1. Ethical Considerations

Regarding AI applications in finance, several ethical issues must be considered. For instance, an AI algorithm’s accuracy is only as unbiased as the data on which it is trained; if the data are biased, the outcome will be biased [29,30]^[22][23]. This can potentially lead to unfair or discriminatory practices in lending, investments, or other monetary activities. Another ethical dilemma is how AI will affect job prospects. As technology advances, there are concerns that it could replace workers in data analyses and customer service roles. Organizations need to consider how these changes could negatively impact their employees and devise strategies to minimize such impacts as they could cause economic disruptions for displaced employees. Finally, there are concerns related to protecting customers’ personal information. AI models require substantial amounts of confidential financial data to work effectively, and these must be secure and comply with all related data privacy regulations. There is also the risk that an AI model could be hacked, leading to financial fraud or other illegal activities.

4.2. Regulatory Considerations

While utilizing AI technologies, it is essential that financial institutions comply with data protection laws such as the Saudi Arabian Data Protection Law and the General Data Protection Regulation (GDPR) of the European Union [21,22,31]^[16][24][25]. Moreover, customer consent must be obtained prior to using personal data for AI-related activities. Accountability is a necessary consideration; financial institutions must be accountable for the decisions made by their AI systems, and customers must have access to redress mechanisms if they are adversely affected [21,22,31]^[16][24][25]

5. Significant AI Components and Their Algorithms

5.1. Significant Components

Developing AI systems that meet the financial industry’s needs requires careful consideration of several critical components, including data quality, data security, explainability, and human oversight. Data quality: High-quality data is a critical component of any AI system, and financial institutions must ensure that their data are clean, accurate, and up-to-date [28,29]^[21][22]. Data quality is essential for building accurate machine learning models that can make informed decisions. Data security: Financial institutions must also consider the security of their data when developing AI systems. Data breaches can have severe consequences for financial institutions, including damage to their reputation, legal liability, and loss of customer trust. Financial institutions must ensure their AI systems are secure and comply with relevant regulations [32,33,34,35,36,37]^{[26][27][28][29][30][31]}. Explainability: It is a critical component of AI systems in the financial industry, where decisions can have significant consequences. Financial institutions must ensure that their AI systems can explain how decisions are made and provide insights into the factors that influence those decisions [32]^[26]. This AI component is necessary for building trust with customers and regulators and improving the accuracy of AI systems. Human oversight: AI systems in the financial industry must have human oversight to ensure they make fair, unbiased, and ethical decisions [29,30]^[22][23]. Financial institutions must ensure that their AI systems are aligned with their organizational values and have mechanisms in place to monitor and correct any biases that may arise. Financial institutions must invest in infrastructure and talent to ensure their AI systems are secure, reliable, and aligned with organizational values.

5.2. Algorithms

AI algorithms can help financial institutions enhance customer services, manage risks, detect fraud, and improve investment decisions. AI algorithms have become an integral part of the financial industry. Among the algorithms on which AI can be utilized in the finance industry and that are well-known are ML and Deep Learning (DL). Figure 21 below illustrates the relationship of AI with ML and ML with DL: