This study explores the role of innovative debt financing mechanisms in Saudi Arabia’s transition towards a sustainable and diversified economy. Specifically, it examines how green bonds and carbon credits are utilized to fund large-scale development projects, such as Neom and the Red Sea development, which focus on renewable energy and environmental sustainability. This research aims to assess the extent to which these financial instruments contribute to the achievement of both economic growth and compliance with the Kingdom’s climate commitments, as outlined in Vision 2030.

This research adopts a case study approach, analyzing the impact of green finance on sustainable development initiatives in Saudi Arabia. It further incorporates artificial intelligence tools to empirically assess the relationship between innovative financing and economic growth, providing a data-driven analysis of the effectiveness of these mechanisms in fostering long-term economic and environmental benefits. This study also considers the integration of Islamic finance instruments, such as Islamic bonds, within the framework of dynamic asset pricing models and financial econometrics. This theoretical alignment helps to highlight the potential of Islamic finance to support global sustainability goals, alongside conventional financing methods.

The findings of this research indicate that green finance mechanisms, including carbon credits and green bonds, are essential for financing renewable energy projects and driving economic diversification in Saudi Arabia. Additionally, the integration of Islamic finance tools strengthens the financial infrastructure and enhances the alignment of Saudi Arabia’s financing strategies with international sustainability initiatives.

In conclusion, this study demonstrates that innovative debt financing can effectively address Saudi Arabia’s environmental and economic challenges. This research underscores the importance of incorporating Islamic finance tools into financial frameworks, offering practical insights for policymakers and financial institutions on how to leverage innovative financing mechanisms to support sustainable development in this region.

This study presents a bibliometric analysis of emerging trends in applying Artificial Intelligence (AI) and Machine Learning (ML) for financial fraud discovery and deterrence and provides insights for future research. Bibliometric analysis on fraud data analytics is helpful to researchers in getting insights on research trends, research impact and classification. However, research on fraud data analytics using machine learning is limited. The main objective of this quantitative analysis is to explore emerging trends in fraud data analytics and machine learning (ML) for financial crime detection and prevention. Bibliometric data has been collected from the Scopus database. One thousand four hundred eighty-three documents from the SCOPUS database have been analysed using VOSviewer. The data analysis divulges a growing interest in leveraging these technologies to strengthen financial crime detection. Fraud data analytics, Artificial Intelligence and Machine Learning are vital in identifying complex criminal patterns, strengthening companies in preventive vigilance, and ensuring fraud elimination. The study portrays the need for vigorous frameworks for the legislature, real-time analytics systems and more powerful tools and calls for integrating governments, financial institutions, and technology providers to strengthen prevention strategies and tackle financial crimes more effectively. It is recommended that companies should invest on AI & ML for the detection of fraud at the early stages.

Beyond the disruptions from the 2007–2008 financial crisis, the collapse of the Silicon Valley Bank, and acquisition of Credit Suisse by UBS Group AG in 2023, global banks continue to show weakness in absorbing major on- and off-balance sheet risk exposures. This study focuses on the joint and separate effects of Environmental (E), Social (S), and Governance (G) scores on bank risk captured both by a series of market related risk indices including cost of capital, and both levered and unlevered CAPM betas. Using a sample of U.S. banks over the period 2016 through 2023, banks’ financial and market data are triangulated with the total and composite ESG scores, all obtained from the London Stock Exchange Group (LSEG) database, formerly known as Refinitiv. The main hypotheses predict that (i) investors demand a lower cost of capital from banks with higher ESG scores, and (ii) banks with higher ESG scores are exposed to lower systematic risks captured both by levered and unlevered betas. Overall, we contend that banks with higher ESG scores establish better operational alignment with employees, shareholders, customers, consumers, and communities while exercising greater due diligence by focusing on a pool of environmentally conscientious borrowers .

The paper’s contributions to the literature is twofold. First, unlike previous studies, the focus on cost of capital and systematic risk indices captures the crucial interactions between ESG investment and firm decisions resulting from market imperfections and regulations. Second, empirical adjustments are made to address potential endogeneity problems in the model caused by factors such as reverse causality between ESG investment and risk, omitted variables, and measurement errors using the instrumental variables technique and application of simultaneous equation systems including the Seemingly Unrelated Regression (SUR) and Two Stage Least Square (2SLS) approaches.

This study examines the inherent complexity of artificial intelligence (AI), its influence on economics and finance, and how it has triggered tremendous shifts in the global economic landscape. Thus, it reveals new patterns that have never been seen due to deep learning, artificial intelligence, machine learning, and natural language processing, which are revolutionizing data analysis. In keeping with all of them, it offers numerous advantages, such as the ability to detect delinquent behavior, reduce the risk of adverse effects, perform algorithmic trading, and improve the prediction of the macroeconomic scenario. As an optical illusion of substance in economics, artificial intelligence is a multi-faceted door to subversive studies and analysis using unusual methodologies that question and revise established statistical and economic principles. This is increasingly true when applying artificial intelligence to many problems, from behavioral finance to new forecasting systems, to improve our understanding of global markets. This allows college students to tailor their experiences, conduct real-time data analysis, and apply artificial intelligence to bring theory to the street. This might be useful for scholars too; therefore, all types of future work should cover data privacy concerns, algorithmic bias, and ethical issues. The practical application of artificial intelligence technology in the financial and economic fields is not an isolated technological pursuit but requires interdisciplinary collaboration.

Generative AI, which ushers a new age of AI, comes with huge economic potential. AI startups, like OpenAI, Anthropic, and DeepSeek, and technology giants, like Google, Amazon, Microsoft, and Meta, compete in the Generative AI market. To not fall behind in the AI race, they have ramped up AI investment with massive capital spending. For example, on January 3, 2025, Microsoft announced that the company is on track to invest approximately USD 80 billion to build AI-enabled data centers to train AI models and deploy AI- and cloud-based applications around the world in fiscal 2025. Also, on January 21, 2025, an AI joint venture called the Stargate Project was created by OpenAI, SoftBank, Oracle, and MGX. The venture plans to invest USD 500 billion in AI infrastructure by 2029. On January 24, 2025, Meta announced that the company plans to build a massive data center in Louisiana to power its newest AI model. It would invest USD 60-65 billion into AI including the data center, which would be “so large it would cover a significant part of Manhattan”. To capitalize on the AI boom, investors are interested in trading AI stocks. AI chatbots, which can identify and classify sentiments from financial news, can be leveraged for investment. So, this paper proposes a GPT-based sentiment analysis approach for trading AI stocks. Also, natural experiments are conducted to evaluate its effectiveness. The initial results show that the approach achieves a good rate of return.

The accuracy and influence of financial analyst forecasts have drawn significant attention due to their essential role in guiding investment decisions and shaping market sentiment. Earnings per share (EPS) forecasts, in particular, provide critical insights into corporate performance, influencing equity valuations and market trends. While EPS is a widely used measure of profitability and financial health, research highlights persistent biases that undermine its reliability. These biases often arise from information asymmetry, behavioral tendencies such as herding and overconfidence, and potential conflicts of interest, leading to systematic forecast errors. The I/B/E/S database aggregates detailed analyst data, including earning forecasts for publicly traded companies. This study evaluates analyst performance through a dynamic ranking system that measures EPS forecast accuracy over time. By periodically ranking analysts, we identify high- and low-performing forecasters while assessing the stability of their predictions. To improve forecast accuracy, we introduce an enhanced consensus method that surpasses individual estimates by applying rank-based weighting. Our approach leverages iterative filtering algorithms to refine consensus estimates by computing key parameters such as individual and market variances and a reliability index. These metrics are integrated into a composite score, allowing for adjustments to prediction discrepancies and the identification of long-term reliability patterns, ultimately improving the accuracy and robustness of EPS consensus forecasts.

Introduction: The tendency to obtain private information results from the lack of transparency and reliability of public information, so that capital market participants, in order to obtain reliable information, take into account the auditor's ability to detect fraud in their investment decisions. The purpose of this study is to investigate the role of audit quality in detecting fraud from the perspective of clients and auditors.

Method: The data required for this study were collected and analyzed using a questionnaire completed by 159 employees of auditing firms and financial managers of companies.

Result: The results show that auditors and clients have different views on the individual ability and responsibility of auditors to detect fraud. But independent auditors have the same view of the quality of fraud detection. In other words, the expectations of capital market participants to detect auditors' fraud are different in different dimensions and are a function of individuals' knowledge and understanding of the auditors' risks and responsibilities for detecting fraud.

Conclusion: Auditors are not reluctant to accept additional responsibility for fraud. However, the auditing profession is committed to improving audit methods to detect fraud and to increase efficiency. In other words, by increasing the quality of an organization's audit, auditors are adequately trained and work is planned based on auditors specializing in each industry, which increases the likelihood of fraud detection.

In recent years, sustainable finance has emerged as a central concept at the convergence of finance and the Sustainable Development Goals (SDGs). Likewise, blockchain technology (BT) and artificial intelligence (AI) are currently considered among the most well-known technologies, and combining these two technologies has uncapped potential, especially for sustainable finance. This study synthesizes and systematically reviews the existing literature on blockchain and artificial intelligence in sustainable finance. We gathered relevant studies from Google Scholar, Scopus, and Web of Science databases. We applied co-occurrence mapping and thematic analysis with the help of VOSviewer and NVivo software. In this study, based on a systematic literature review of 509 studies, the findings revealed five thematic areas (i.e., ESG measurement and disclosure; tracking and trading of carbon footprint; renewable energy and circular economy; transparency, security, governance, and compliance; and social and ethical aspects) attracting research interest. This study also identified several challenges faced by sustainable finance in the contemporary business environment. We outline, with a few remarks, general trends for the use of blockchain and artificial intelligence in sustainable finance developments in the financial markets. The findings of this study recommend more regulatory oversight on sustainable investments and actions from governments to promote sustainable investing.

Purpose: This study investigates the practical application of AI techniques in combating CCF within the accounting and finance sectors. It assesses the effectiveness of ML, blockchain, and fuzzy logic in detecting fraudulent transactions, providing insights for fraud examiners, auditors, accountants, bankers, and organizations.

Methodology
A cross-country survey was conducted, involving 403 respondents from various sectors. Data collection included interviews and structured questionnaires analyzed using SPSS.
Sample Composition:
Of the respondents, 40% were from Egypt, 20.6% were from Russia, and 17.1% were from the UK.In addition, 73% were PhD holders, 26.3% were researchers, and 13.6% were bankers.
Reliability and Validity:
Cronbach’s alpha coefficient (0.972) confirmed high reliability.

They key Findings.

1. ML's Role in CCFD:
Respondents confirmed that ML enhances fraud detection with an agreement mean of 4.56. ML's ability to process large datasets, detect anomalies, and prevent fraud supports its critical role in financial security .

2. Blockchain's Impact:
Blockchain technology was recognized for enhancing fraud detection with a mean rating of 4.46. Its decentralized nature, secure data exchange, and smart contracts improve fraud prevention mechanisms.

3. Fuzzy Logic in Fraud Detection:
Fuzzy logic was deemed valuable in fraud detection, scoring a mean of 4.37. It effectively processes ambiguous transaction data, reducing false alerts and improving fraud detection accuracy.

We propose a novel framework integrating AI, blockchain, fuzzy logic, and IoFS to create a secure, efficient system for detecting and preventing fraud.
1. Data Collection: Transaction data from IoFS, banks, and FinTech platforms are aggregated.
2. Behavioral Analysis: ML algorithms analyze spending patterns and detect anomalies.
3. Fraud Detection: AI compares transactions against historical data, flagging suspicious activities.
4. Fuzzy Logic Processing: Risk scores are assigned based on transaction uncertainty levels.
5. Blockchain Implementation: Smart contracts validate transactions and maintain a tamper-proof ledger.
6. Authentication and Approval: Stakeholders verify flagged transactions via blockchain consensus.
7IoFS-Based Data Sharing: Real-time fraud data exchange enhances system adaptability.

Advancements in Artificial Intelligence (AI), including Optical Character Recognition (OCR), have significantly transformed various industries. In developing countries like Indonesia, the relevance of AI-OCR remains debated due to low labor costs. This study aims to understand the motivations, challenges, and outcomes of AI-OCR adoption in the Procure-to-Pay (P2P) process of two Indonesian companies. Utilizing a qualitative approach grounded in Resource-Based View (RBV) theory, data were collected from 11 respondents, including management accountants, Chief Financial Officers (CFOs), IT personnel, and an AI expert provider. A semi-structured interview was conducted to gather deeper insights. Data analysis was conducted using thematic analysis to identify and interpret patterns and themes related to the adoption of AI-OCR in the P2P process. This study reveals that both companies are motivated by the overall efficiency enhancement of the P2P process despite low labor costs in Indonesia. However, the family-owned company is also driven by sustainability initiatives, aligning with RBV's emphasis on leveraging unique resources. Challenges faced by the companies differ due to business complexity, resource constraints, and resistance to change, which highlight differences in their resource capabilities. The multinational company experienced a smoother implementation of AI-OCR due to strategic alignment and active CFO involvement, illustrating RBV's principles of effective resource management. This study provides insights into motivations like sustainability and challenges like resource constraints. It addresses a gap in the literature by comparing AI adoption in resource-constrained environments, highlighting the role of strategic alignment and management support in successful technology implementation.