A B S T R A C T

The financial sector encounters numerous challenges such as cyber threats, fraud, and regulatory compliance. Traditional methods of safeguarding financial transactions and assets are becoming increasingly insufficient against advanced cyber-attacks. This thesis examines the transformative impact of Artificial Intelligence (AI) on financial security. It investigates various AI-driven innovations, their applications in asset protection, and risk mitigation, while also considering the ethical and regulatory implications. AI is reshaping financial risk management by offering advanced tools and techniques for identifying, assessing, and mitigating risks. This article explores the innovations and applications of AI-driven financial risk management, emphasizing its transformative effect on traditional risk management practices. We discuss various Artificial intelligence technology, such as natural language processing, predictive analytics, and machine learning and their applications in enhancing financial stability, regulatory compliance, and operational efficiency. As cyber threats grow more sophisticated, traditional network security approaches are becoming inadequate due to scalability issues, slow response times, and the inability to detect advanced threats. This highlights the need for research into more efficient security methods to protect against diverse network attacks. Cybercriminals use AI for data poisoning and model theft to automate attacks, emphasizing the need for AI-based cybersecurity techniques. This study introduces a cybersecurity technique based on AI for financial sector management (CS-FSM) to map and prevent unforeseen risks. By utilizing AI technologies like the K-Nearest Neighbor (KNN) algorithm with the Enhanced Encryption Standard (EES), the suggested approach improves data privacy, scalability, risk reduction, data protection, and attack avoidance, significantly improving the performance of cybersecurity systems in the financial sector

DOI: https://doi.org/10.103/xxx @ 2024 C5K Research Publishing

1. Introduction 

The financial sector is a major target for cybercriminals because it deals with high-value transactions and handles sensitive information. The evolution of cyber threats has outpaced traditional security measures, necessitating a shift towards more advanced technologies. AI has emerged as a powerful tool in this landscape, offering capabilities that go beyond human capacity in detecting, preventing, and responding to financial security threats. The financial industry is experiencing a major transformation due to technological advancements, with Artificial Intelligence (AI) emerging as a key factor in revolutionizing financial risk management. Traditional risk management methods, which predominantly depend on historical data and human judgment, are increasingly being enhanced or replaced by AI-driven techniques (Li et al., 2021) 

In an environment where risks abound, AI stands out as a beacon of proactive intelligence in financial services. Traditional risk management methods, often reactive and subject to human bias, are being replaced by AI-driven models that are predictive, accurate, and constantly learning. This transformation marks a significant shift from the past, ushering in a new era where financial institutions can manage risks with unprecedented agility and confidence. AI’s impact on risk management is profound, as it leverages vast datasets and sophisticated algorithms to identify patterns and anomalies that human analysts might miss. This capability goes beyond data analysis, extending to predicting future trends and identifying potential threats, allowing financial institutions to take a proactive approach to risk management.

Artificial Intelligence (AI) is transforming financial risk management with applications that improve efficiency and decision-making. Machine learning is vital in risk assessment, allowing systems to analyze extensive datasets, recognize patterns, and make predictions. Predictive analytics uses AI algorithms to forecast market trends, evaluate credit risks, and foresee potential financial threats. AI-driven risk modeling employs advanced algorithms to simulate complex scenarios, providing a deeper understanding of potential risks. AIpowered risk prediction algorithms deliver real-time insights into possible financial challenges, supporting proactive risk management strategies (Brynjolfsson & Mcafee, 2017).

This report presents an overview of AI's role in cybersecurity within the financial services sector and examines its implications for financial institutions. Although the primary focus is on AI's application in cybersecurity and the associated risks, many findings and best practices are relevant to other AI use cases as well. The Treasury plans to continue investigating AI's impact on financial services in the coming months and years. This article explores the role of AI in financial risk management, emphasizing its innovations in protecting assets.

2. The Role of AI in Financial Security 

Artificial Intelligence (AI) is essential for improving financial security because it offers sophisticated instruments and methodologies to protect assets, prevent fraud, and manage risks. AI-driven technologies, such as machine learning and predictive analytics, enable financial firms to instantly evaluate enormous volumes of data in order to spot trends and abnormalities that could indicate fraudulent activities or security breaches. This proactive approach allows for the early detection and mitigation of potential threats, significantly reducing the likelihood of financial loss. Additionally, AI enhances regulatory compliance by automating complex processes and ensuring that financial institutions adhere to stringent regulatory standards. By leveraging sophisticated algorithms and continuous learning capabilities, AI not only improves the accuracy and efficiency of financial security measures but also adapts to evolving threats, ensuring robust protection in an increasingly digital financial landscape (Ahmed). AI's role in financial security can be categorized into several areas is shown in (Figure -1).

Fig. 1. Artificial Intelligence’s role in financial security  

2.1. Enhancing Fraud Detection and Prevention with AI

Artificial Intelligence (AI) is revolutionizing fraud detection and prevention in the financial sector by offering real-time analysis and predictive capabilities that far surpass traditional methods. AI algorithms can identify deviations from normal transaction patterns, flagging potentially fraudulent activities as they occur. This allows financial institutions to respond immediately and mitigate any potential damage. AI excels in analyzing user behavior, creating profiles based on typical activities and transaction patterns. By continuously monitoring these profiles, AI can detect unusual activities that may indicate compromised accounts. For instance, if a user's account shows a transaction from an unusual location or a purchase far exceeding their typical spending, the AI system will flag it as suspicious. This personalized behavioral analysis makes it more difficult for fraudsters to bypass security measures.

Fraud detection Model performance is shown in fig.2. AI models also leverage historical data to predict potential fraud risks, enabling financial institutions to implement proactive measures. By examining past transactions and identifying patterns associated with fraudulent behavior, AI can forecast where and how fraud is likely to occur. This predictive capability helps institutions adjust fraud detection rules and enhance monitoring for high-risk transactions. Additionally, AI reduces the incidence of false positives, a common issue with traditional rule-based systems, by refining its comprehension of the differences between normal and aberrant behavior through machine learning. This results in fewer false alarms and a smoother experience for legitimate users while maintaining robust security (Odeyemi et al., 2024).

maintaining robust security (Odeyemi et al., 2024). 

Fig. 2. Fraud detection Model performance

Moreover, AI's ability to continuously learn and adapt is crucial in the ever-evolving landscape of financial fraud. As AI systems are exposed to more data and different types of fraud, they update their algorithms to recognize and counteract emerging threats, ensuring continued effectiveness against evolving fraud tactics. One of the most critical applications of AI in financial security is fraud detection and prevention. Traditional fraud detection systems often rely on predefined rules and historical data, which can be slow to adapt to new fraud tactics. AI, however, brings a dynamic approach by continuously learning from new data and identifying patterns that point to possible fraud shows in Table 1.  

Machine learning algorithms are at the forefront of this innovation. These algorithms analyze vast amounts of transaction data to identify anomalies and deviations from normal behavior. For instance, if a user who typically makes small, local purchases suddenly makes a large, international transaction, the AI system can flag this as suspicious. Additionally, AI systems can cross-reference multiple data points, such as device information, geolocation, and transaction history, to assess the likelihood of fraud more accurately. Behavioral biometrics is another AI-driven innovation enhancing fraud prevention. By analyzing how users interact with their devices – including typing speed, mouse movements, and touch patterns – AI systems can create unique user profiles. Any significant deviation from these patterns can trigger an alert, indicating potentially fraudulent activity. This method adds an additional layer of security, making it harder for fraudsters to mimic legitimate users (Kotagiri, 2023).

Table1. Quantitative Analysis's Outcomes for an AI-Powered Fraud Detection System(Kotagiri, 2023).  

2.2. AI-Driven Risk Management and Security in Finance

Artificial Intelligence (AI) is transforming risk management within the financial sector by delivering more accurate risk assessments and predictions, ultimately leading to more informed decision-making and improved financial stability. One of AI’s primary strengths lies in its ability to process vast volumes of data efficiently, identifying potential risks and assessing their impact with a level of precision unattainable by traditional methods. This advanced data analysis capability is pivotal for financial institutions as they navigate the complexities of modern markets and strive to mitigate risks effectively (Daiya, 2024). 

AI-driven risk management systems excel in evaluating creditworthiness, a critical function for lending institutions shown in fig 3. Traditional credit scoring models typically rely on a limited set of factors, such as credit history, income, and employment status. While these factors provide a snapshot of a borrower’s financial health, they often fail to capture the full picture, especially for individuals with non-traditional income sources or limited credit histories. AI-enhanced credit scoring models, however, can analyze a far broader variety of informational elements, such as past transaction history, social media activity, and even utility bill payments. This comprehensive approach allows for a more nuanced and accurate assessment of a borrower’s creditworthiness, enabling lenders to extend credit to a wider audience while maintaining rigorous risk standards(Cao, 2020)

Fig. 3. AI-driven Risk managements framework

2.2.1. AI-Powered Credit Risk Assessment

One of the critical areas where AI significantly enhances risk mitigation is in credit risk assessment. Traditional credit scoring models rely heavily on historical financial data, which may not always provide a complete picture of a borrower’s creditworthiness. AI, on the other hand, can analyze a much broader set of data points, including transactional data, social media activity, and even behavioral patterns. Machine learning algorithms are particularly adept at identifying patterns and correlations that human analysts might overlook. By processing vast amounts of data, these algorithms can predict the likelihood of default with greater accuracy. For instance, AI can assess the risk of lending to a small business by analyzing its cash flow patterns, customer reviews, and industry trends. Lenders may make better decisions thanks to this thorough research, which lowers default risk and enhances the performance of the credit portfolio as a whole. (Saxena, 2024)

2.2.2. Predictive Analytics for Market Risk Management

Market risk management is another area where AI has made significant inroads. The financial markets are characterized by extreme volatility that is attributed to several causes such as investor sentiment, geopolitical events, and economic indicators. Traditional risk management tools often struggle to keep up with the dynamic nature of markets. AI-driven predictive analytics offers a solution by analyzing historical and real-time data to forecast market trends and potential risks. AI algorithms can process and analyze massive datasets from various sources, such as stock prices, economic reports, and news articles. By identifying patterns and trends, AI can predict potential market movements and provide early warnings of market risks. For example, AI is able to forecast market responses to news events and assess investor mood by analyzing opinion on social media. This allows financial institutions to adjust their investment strategies proactively, mitigating potential losses and capitalizing on emerging opportunities (Rahmani et al., 2023).

2.2.3. Enhancing Agility and Strategic Decision-Making in Market Risk Management with AI

Furthermore, AI’s ability to provide real-time risk assessments supports more agile and responsive risk management. Traditional risk management practices often involve periodic reviews and updates, which can lead to delayed responses to emerging risks. AI-driven systems, however, offer continuous monitoring and analysis, enabling financial institutions to detect and respond to risks as they arise. This real-time insight helps mitigate potential losses and maintain financial stability even in volatile market conditions. The benefits of AI in risk management extend beyond just improving accuracy and speed. AI also facilitates more strategic decision-making by providing a deeper understanding of risk factors and their interrelationships. For example, AI can model complex scenarios that account for multiple variables, offering insights into how different risk factors might interact and impact the institution’s overall risk profile. This holistic view supports more informed and strategic decision-making, helping financial institutions balance risk and reward more effectively (Yazdi et al., 2024).

2.2.4. Enhancing Operational Risk Management

Operational risk, which includes risks arising from internal processes, systems failures, and human errors, is another critical area where AI-driven solutions are making a substantial impact. AI can enhance operational risk management by automating routine tasks, monitoring processes in real-time, and identifying anomalies that could indicate potential issues. Robotic Process Automation (RPA) powered by AI can streamline and standardize repetitive tasks, reducing the likelihood of errors and improving efficiency. For instance, AI can automate data entry, transaction processing, and compliance checks, ensuring that these tasks are performed accurately and consistently. This not only reduces operational risks but also frees up human resources to focus on more strategic activities. In addition to automation, AI can monitor operational processes in real-time, using machine learning algorithms to detect unusual patterns and anomalies. For example, AI can analyze transaction data to identify discrepancies or deviations from established norms, flagging potential fraud or system malfunctions. By providing early detection and real-time monitoring, AI enables financial institutions to address operational risks promptly and effectively (Carvalho et al., 2022).

Moreover, the integration of AI into risk management processes can lead to significant cost savings. By automating routine risk assessment tasks, AI reduces the need for extensive manual analysis, allowing risk management professionals to focus on higher-level strategic planning and decision-making. This not only improves operational efficiency but also ensures that human expertise is applied where it is most valuable. AI enhances risk management by providing more accurate and comprehensive risk assessments, processing large volumes of data to identify potential risks, and offering real-time insights that support agile decision-making. AI-driven credit scoring models and market risk analysis tools are just two examples of how AI is revolutionizing the financial sector, enabling institutions to manage risks more effectively and maintain financial stability in an increasingly complex and dynamic environment. As AI technology continues to advance, its role in risk management is likely to grow, offering even more sophisticated tools and techniques to navigate the everevolving landscape of financial risks.

Table 2. Creating the Financial Future: Using AI to Enhance Risk Management in the Global Financial Sector

2.3. Enhancing Cybersecurity with AI 

Artificial Intelligence (AI) significantly strengthens cybersecurity by detecting and responding to threats more swiftly and accurately than traditional methods. This enhancement is particularly evident in AI-powered Intrusion Detection Systems (IDS), which can identify and counter cyber threats in real-time, thereby minimizing potential damage. These advanced systems continuously monitor network traffic, user behavior, and other relevant data points to detect anomalies that might indicate a security breach. Unlike traditional IDS, which often rely on predefined rules and signatures, AI-powered IDS employ machine learning algorithms to recognize patterns and learn from past incidents, enabling them to identify even the most sophisticated and novel threats (Kaur et al., 2023).

One of the key strengths of AI in cybersecurity is its ability to gather and analyze threat intelligence data from a multitude of sources. This data includes information from network logs, user activity, external threat databases, and even dark web forums where cybercriminals discuss their strategies. By consolidating and analyzing this vast amount of information, Artificial intelligence (AI) systems are able to offer deep insights into new risks and weaknesses. By taking a proactive stance, organizations can remain ahead of possible attacks, updating their security measures based on the latest intelligence. Consequently, AI-driven threat intelligence not only enhances the detection capabilities but also helps in anticipating and preventing attacks before they can cause significant harm.

Fig.4. AI-driven enhancing cybersecurity

AI-driven automation is another crucial factor in enhancing cybersecurity. Traditional response strategies often involve manual intervention, which can be slow and error-prone, especially during large-scale attacks. AI, however, enables rapid and automated responses to security incidents, significantly reducing the time it takes to address and mitigate threats has shown in fig.4. For example, AI can automatically isolate affected systems, apply patches, or reconfigure network settings to prevent the spread of malware. This level of automation is particularly beneficial in environments with limited cybersecurity personnel, as it ensures that threats are dealt with promptly and efficiently, minimizing their impact on the organization (Mishra, 2023).

Moreover, AI can enhance the overall efficiency of cybersecurity operations by reducing the burden of routine tasks on human analysts. Tasks such as monitoring logs, analyzing alerts, and updating threat databases can be automated with AI, allowing cybersecurity professionals to focus on more complex and strategic aspects of security management. This not only improves the effectiveness of the cybersecurity team but also reduces the risk of human error, which is often a significant factor in security breaches. AI’s ability to learn and adapt is another critical advantage in the cybersecurity domain. As cyber threats evolve, so do the methods used by AI systems to detect and respond to these threats. Machine learning algorithms can continuously refine their models based on new data and emerging threat patterns, ensuring that the cybersecurity defenses remain robust and effective. This dynamic adaptability is crucial in combating cybercriminals, who are constantly developing new tactics to bypass security measures.

Furthermore, AI can play a pivotal role in identifying insider threats, which are often challenging to detect with traditional methods. By analyzing user behavior and access patterns, AI systems can identify anomalies that may indicate malicious activities by employees or contractors. This capability is particularly important in protecting sensitive data and preventing breaches from within the organization.

2.4. Regulatory Compliance with AI  

Artificial Intelligence (AI) plays a crucial role in ensuring regulatory compliance within financial institutions by automating compliance processes and continuously monitoring for potential violations. The integration of AI-powered regulatory technology (RegTech) has revolutionized how financial institutions manage their compliance obligations, significantly reducing the manual burden and enhancing accuracy. RegTech solutions employ AI to automate routine compliance checks, such as monitoring transactions, auditing financial activities, and ensuring adherence to complex regulatory requirements. This automation reduces the possibility of human error, which can result in expensive fines and reputational harm in addition to saving time and resources. (Kaur et al., 2023).

Anti-Money Laundering (AML) and Know Your Customer (KYC) process enhancement is one of the main uses of AI in regulatory compliance. Traditional methods of AML and KYC involve extensive manual checks and documentation, which can be time-consuming and prone to inaccuracies. AI, however, can process vast amounts of data at high speed, quickly identifying suspicious activities that may indicate money laundering or other illicit financial behaviors. Artificial intelligence (AI) systems can identify potentially fraudulent actions for additional investigation by examining trends and abnormalities in transaction data, ensuring that financial institutions remain compliant with AML regulations(AlShabandar et al., 2019). In the context of KYC, AI systems streamline the process of verifying customer identities. This involves analyzing multiple data sources, such as identity documents, credit history, and other personal information, to confirm the legitimacy of customers. AI-powered systems can perform these verifications more efficiently and accurately than manual methods, reducing the time required for onboarding new customers and enhancing the overall customer experience. Additionally, AI can continuously monitor customer behavior, updating risk profiles in real-time and ensuring ongoing compliance with KYC requirements. This dynamic monitoring is particularly important in detecting and preventing fraud, as it allows financial institutions to respond promptly to any changes in customer behavior that may indicate risk. Table 3. covers the major elements involved in ensuring regulatory compliance when using AI technologies.

Moreover, AI's predictive capabilities are invaluable in regulatory compliance. By leveraging machine learning algorithms, AI can anticipate potential compliance issues before they arise, enabling proactive measures to be taken. For example, AI can predict which transactions are likely to trigger regulatory scrutiny based on historical data and current trends, allowing financial institutions to address these risks preemptively. This forward-looking approach not only helps in maintaining compliance but also in mitigating risks associated with regulatory breaches. The use of AI in regulatory compliance also extends to managing and interpreting vast amounts of regulatory data. Regulatory frameworks are often

complex and subject to frequent updates, making it challenging for financial institutions to stay current with the latest requirements. AI systems can automatically parse and interpret regulatory texts, extracting relevant information and ensuring that compliance processes are aligned with the latest regulations. This capability reduces the need for extensive manual review and helps institutions remain agile in adapting to regulatory changes. Furthermore, AI enhances reporting and transparency in regulatory compliance. By automating the generation of compliance reports, AI ensures that these documents are accurate, comprehensive, and submitted in a timely manner. This not only satisfies regulatory requirements but also provides valuable insights into the institution’s compliance status and areas that may require attention.

Table 3. Regulatory Compliance with AI  

3. AI-Driven Investment Strategies

AI is revolutionizing investment strategies by providing sophisticated tools for portfolio management and risk assessment. Algorithms powered by AI examine enormous volumes of financial data to find investment opportunities and enhance portfolios. Robo-advisors are a prominent example of AI in investment management. These platforms use machine learning algorithms to analyze an investor’s financial goals, risk tolerance, and market conditions. Based on this analysis, robo-advisors provide personalized investment recommendations and automatically adjust portfolios to optimize returns while minimizing risks. AI also enhances quantitative trading strategies. High-frequency trading algorithms leverage AI to analyze market data and execute trades at lightning speed, capitalizing on market inefficiencies. These algorithms can process vast amounts of data in real-time, identifying trends and executing trades faster than any human trader(Zhang, 2023)

4. Advancements in Insurance and Claims Processing

The insurance sector is experiencing significant transformation due to AI-driven innovations in claims processing and risk assessment. These advancements are revolutionizing how insurers operate, enhancing both efficiency and accuracy in evaluating insurance claims. By leveraging AI, insurance companies can dramatically reduce processing times, which leads to improved customer satisfaction. AI-powered systems have the capability to analyze vast amounts of claims data to identify patterns and detect fraudulent claims, a task that would be labor-intensive and error-prone if done manually. Machine learning algorithms play a crucial role in assessing the validity of claims. These algorithms compare new claims with historical data to identify anomalies, thus helping insurers mitigate losses from fraudulent activities and ensuring that legitimate claims are processed swiftly(Zanke, 2021).

In underwriting, AI brings a new level of sophistication to risk assessment. Traditional methods of risk evaluation often rely on limited data sets, but AI can analyze a broader range of information, including medical records, social media activity, and lifestyle factors. This comprehensive analysis allows for more accurate pricing of insurance policies, tailored to the specific risk profiles of individual clients. Consequently, insurers can manage risks more effectively and offer more competitive rates to customers. Furthermore, AI systems continually learn and adapt from new data, improving their predictive accuracy over time. The integration of AI in the insurance industry not only enhances operational efficiency but also transforms customer interactions. Automated customer service systems, powered by natural language processing, provide instant support and streamline communication, further enhancing customer experience. Overall, AIdriven advancements in claims processing and risk assessment are setting new standards in the insurance industry, enabling companies to operate more efficiently, manage risks better, and deliver superior service to their customers. The continued evolution of these technologies promises to bring even greater benefits, driving innovation and growth in the sector.

5. Future Prospects and Ethical Considerations

As AI continues to evolve, its potential to enhance financial security will only grow. Future innovations may include more advanced behavioral analytics, deeper integration with blockchain technology for secure transactions, and enhanced AI-driven cybersecurity measures. These advancements promise to make financial systems more resilient against fraud, cyber threats, and other vulnerabilities. However, the increasing reliance on AI also raises significant ethical and regulatory considerations. Ensuring transparency and accountability in AI decision-making processes is crucial to maintain trust and credibility. Financial institutions must implement robust governance frameworks to monitor AI systems and ensure they operate within ethical and legal boundaries. This involves not only regular audits and compliance checks but also the development of clear policies and guidelines for AI use.

Data privacy is another critical concern. AI systems depend on extensive data, leading to concerns about data collection, storage, and usage. Financial institutions need to prioritize data protection and adhere to regulations like the General Data Protection Regulation (GDPR) to ensure customer information is secure. This involves implementing strong data encryption methods, ensuring secure data storage, and establishing transparent data usage policies. Additionally, institutions must be vigilant about the potential biases in AI algorithms, which can lead to unfair or discriminatory outcomes. Regular reviews and updates of these algorithms are necessary to mitigate such risks.

In conclusion, AI-driven financial security innovations are transforming the landscape of asset protection. From fraud detection and predictive analytics to enhanced cybersecurity and automated compliance, AI provides powerful tools to safeguard assets and manage risks effectively. As these technologies continue to advance, they will play an increasingly vital role in ensuring the stability and security of the financial sector. However, it is essential to address the ethical and regulatory challenges associated with AI to fully realize its potential and build trust with stakeholders. By focusing on transparency, accountability, and data privacy, financial institutions can harness the full benefits of AI while maintaining ethical standards and regulatory compliance.

References 

Ahmed, R. Artificial Intelligence (AI) in Financial Sectors: Blessings or Threats?

Al-Shabandar, R., Lightbody, G., Browne, F., Liu, J., Wang, H., & Zheng, H. (2019). The application of artificial intelligence in financial compliance management. Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing,

Brynjolfsson, E., & Mcafee, A. (2017). Artificial intelligence, for real. Harvard business review, 1, 1-31.

Cao, L. (2020). AI in finance: A review. Available at SSRN 3647625.

Carvalho, M. C., Gonçalves, R., da Costa, R. L., Pereira, L. F., & Dias, A. (2022). Contributions of Artificial Intelligence in Operational Risk Management. International Journal of Intelligent Information Technologies (IJIIT), 18(1), 1-16.

Daiya, H. (2024). AI-Driven Risk Management Strategies in Financial Technology. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 5(1), 194- 216.

Kaur, R., Gabrijelčič, D., & Klobučar, T. (2023). Artificial intelligence for cybersecurity: Literature review and future research directions. Information Fusion, 97, 101804.

Kotagiri, A. (2023). Mastering Fraudulent Schemes: A Unified Framework for AI-Driven US Banking Fraud Detection and Prevention. International Transactions in Artificial Intelligence, 7(7), 1-19.

Li, Y., Yi, J., Chen, H., & Peng, D. (2021). Theory and application of artificial intelligence in financial industry. Data Science in Finance and Economics, 1(2), 96-116.

Mishra, S. (2023). Exploring the impact of AI-based cyber security financial sector management. Applied Sciences, 13(10), 5875.

Odeyemi, O., Mhlongo, N. Z., Nwankwo, E. E., & Soyombo, O. T. (2024). Reviewing the role of AI in fraud detection and prevention in financial services. International Journal of Science and Research Archive, 11(1), 2101- 2110.

Rahmani, A. M., Rezazadeh, B., Haghparast, M., Chang, W.- C., & Ting, S. G. (2023). Applications of artificial intelligence in the economy, including applications in stock trading, market analysis, and risk management. IEEE Access.

Saxena, M. (2024). AI-Powered Credit Scoring: Transforming Lending Decisions in Fintech. International Journal of Research and Review Techniques, 3(1), 31-34.

Yazdi, M., Zarei, E., Adumene, S., & Beheshti, A. (2024). Navigating the Power of Artificial Intelligence in Risk Management: A Comparative Analysis. Safety, 10(2), 42.

Zanke, P. (2021). Enhancing Claims Processing Efficiency Through Data Analytics in Property & Casualty Insurance. Journal of Science & Technology, 2(3), 69-92.

Zhang, Z. (2023). Revolutionizing Investment Strategies: Optimizing Portfolios Through Large-Scale Language Models and Innovative Leasing Structures. 2023 3rd International Signal Processing, Communications and Engineering Management Conference (ISPCEM),