Cryptography in Financial Markets: Potential Channels for Future Financial Stability

This study aims to explore the impact of blockchain technology on enhancing transparency and security in financial transactions within the entrepreneurial ecosystem. A qualitative research approach was utilized, employing semi-structured interviews with experts and practitioners in the field of blockchain and finance. The data were analyzed through thematic analysis to identify key themes and categories. The study identified five main themes: Transparency in Transactions, Security of Transactions, Impact on Entrepreneurship, Adoption and Challenges, and Technological Infrastructure. Each theme encompasses various sub-categories and concepts highlighting the potential benefits and challenges of implementing blockchain in financial transactions. Blockchain technology offers significant opportunities to enhance transparency and security in financial transactions. However, widespread adoption is contingent upon overcoming technical and regulatory challenges. Future research should focus on developing practical strategies for blockchain integration into various sectors.

The increasing sophistication of financial fraud, combined with the rapid expansion of digital financial services, poses significant challenges for conventional fraud detection systems. Traditional machine learning models, which rely on transaction-level features, often fail to capture the intricate relational structures underlying fraudulent behaviors in digital finance ecosystems. This study proposes a Graph Neural Network (GNN)-based fraud detection framework, leveraging network science, deep learning, and Explainable AI (XAI) to improve fraud prevention strategies in fintech platforms, payment systems, and digital lending markets. By modeling financial transactions as structured graphs, GNNs uncover hidden dependencies, collusive fraud rings, and synthetic identity fraud patterns more effectively than conventional fraud detection approaches. A comparative analysis against industry-standard models, including Random Forest and XGBoost, demonstrates that GNNs achieve superior recall and predictive accuracy, particularly in detecting fraudulent behaviors embedded within decentralized financial transactions and peer-to-peer lending networks. Additionally, we assess the computational efficiency and real-time feasibility of GNNs, addressing scalability concerns for high-frequency financial environments such as real-time payment fraud detection and blockchain-based financial transactions. Our findings highlight the potential of integrating GNN-based fraud detection into digital banking, fintech applications, and financial risk management frameworks, enhancing fraud prevention, risk assessment, and regulatory compliance in an increasingly interconnected financial landscape. This research provides actionable insights for financial institutions, fintech innovators, and policymakers, positioning graph-based AI as a foundational technology for secure, scalable, and transparent financial crime prevention in the digital economy.

Resource curse in OPEC with varied levels of financial regulations and constraints: The role of oil price shocks and digital finance

This study examined the impact of Financial Technology (FinTech) on the financial ecosystem of Nigeria’s. energy capital city of Port Harcourt. The study focused on how two critical dimensions of FinTech - Digital Payment Systems and Blockchain and Cryptocurrency Technologies - affect the effectiveness of traditional banking and allied financial services, operationalized here as Customer Satisfaction and Financial Accessibility, with Regulatory Framework serving as a moderating variable. The research adopted a descriptive survey design, targeting staff and customers of selected commercial banks in Port Harcourt. A structured questionnaire was used to collect data, and the responses were analyzed using Pearson Product-Moment Correlation Coefficient (PPMCC) and Multiple Regression Analysis. The findings revealed a significant positive relationship between Digital Payment Systems and both Customer Satisfaction and Financial Accessibility, the two proxies of Effective Financial Services – the criterion variable. Similarly, Blockchain and Cryptocurrency Technologies were found to significantly enhance transparency, efficiency, and trust in financial transactions as evidenced in Customer Satisfaction and Financial Accessibility. Furthermore, the study established that Regulatory Framework plays a crucial moderating role, ensuring that the adoption of FinTech strengthens the stability and performance of traditional banking systems rather than displacing them. The study concluded that FinTech integration has redefined customer experience, improved service delivery, and increased financial accessibility in Port Harcourt. In the light of these findings, it is recommended that banks upscale their investment in digital infrastructures and collaborate with FinTech firms. Also, regulators should establish adaptive frameworks to balance innovation with consumer protection.

Finance has long been a subject of security efforts. As financial transactions become increasingly digitalized, financial institutions and infrastructures play a crucial role in shaping security regimes by controlling financial flows, enforcing sanctions, and monitoring global transactions. This chapter explores the evolving landscape of financial security in the context of digital transformation, emphasizing the role of financial infrastructures in both enabling connectivity and facilitating surveillance. The discussion begins by examining financial connectivity, focusing on correspondent banking and the SWIFT network as central infrastructures in international finance. Correspondent banks enable cross-border transactions but also serve as strategic tools in economic governance and sanctions enforcement. The SWIFT network, often viewed as a communication system rather than a payment processor, has played a key role in international financial surveillance, particularly in geopolitical conflicts. The exclusion of countries like Iran from SWIFT demonstrates the significant economic and political consequences of financial disconnection. The chapter then transitions to financial surveillance, highlighting how financial transactions generate data that can be leveraged by banks, intelligence agencies, and law enforcement to track (illicit) financial activities. The rise of digital platforms has further enhanced surveillance capabilities by embedding financial transactions within data-driven business models. Finally, the chapter examines emerging alternatives to Western-dominated financial infrastructures. Such alternatives, which are already present with the Chinese CIPS system, are continuing to emerge with digital finance. The rise of digital currencies, including central bank digital currencies (CBDCs) and so-called stable coins, also poses potential shifts in global financial governance. While digital currencies promise greater financial inclusion and efficiency, they also raise concerns regarding privacy and surveillance. The future of digital finance is currently shaped by the tension between financial fragmentation and efforts toward new forms of international cooperation.

Anwar Nasution: It is my pleasure to comment on this excellent paper on the important topic of analyzing the People’s Republic of China’s (PRC) reforms over the past decade to move toward internationalizing the Renminbi (RMB). The RMB is beginning to be used as an international medium of exchange in dominating and setting cross-border trade and financial transactions. The paper summarizes the benefits and costs of RMB internationalization from a theoretical point of view. There are also econometric studies on the impacts of RMB internationalization on financial development in the PRC and the estimate of seigniorage revenue of RMB, but there is no estimate on the inflationary tax enjoyed by the PRC from foreign holdings of its currency. In addition, internationalization of the RMB allows the PRC to borrow from the international community with liabilities denominated in that currency without exchange rate risk. Unfortunately, there are no brief summaries of PRC government policies on how to make the RMB become an international unit of account, medium of exchange, and store of value. Also there is no information provided in the paper on the size of capital markets in the PRC: the structural financial system, the size of public sector debt and security market, and their liquidity.

The integration of Artificial Intelligence (AI) and Blockchain is revolutionizing the financial sector, targeting crucial challenges like security and transparency. This paper explores the synergistic effects of AI and Blockchain on enhancing the security of financial transactions through advanced real-time fraud detection, anomaly identification, and decentralized transaction verification. Employing a comprehensive review of existing literature and case studies, the research investigates how AI’s capabilities in processing vast data volumes can be leveraged alongside Blockchain’s robust, immutable ledger system to mitigate risks in financial operations effectively. The findings reveal that integrating AI with Blockchain not only significantly improves the security by enabling the real-time detection of anomalies but also upholds the integrity and transparency of transactions across distributed ledgers. The results underscore the potential of AI-Blockchain technology to enhance financial transaction frameworks and highlight its capacity to support the achievement of the United Nations Sustainable Development Goals (SDGs), particularly SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 16 (Peace, Justice, and Strong Institutions) by fostering more transparent and secure economic environments. The conclusion of the study suggests further research on the scalability of AI-Blockchain integrations and their broader application across various industries, pointing towards a transformative impact on global financial practices.

Введение. Цифровизация всех форм корпоративных отношений и общественной жизни, обусловленная нарастанием влияния четвертой промышленной революции, актуализирует широкое применение цифровых технологий предприятиями реального сектора экономики, которые рассматривают их в качестве инструментов упрощения процессов заключения сделок и обеспечения их безопасности, а также в качестве нового источника привлечения инвестиционного капитала. Уже сейчас пилотные проекты по применению цифровых финансовых активов (ЦФА) как источников наращения объемов финансового обеспечения показали свою высокую результативность, а сам механизм их внедрения оказался достаточно прост и удобен для компаний-эмитентов. В связи с этим можно обозначить достаточно высокий потенциал применения в реальном секторе экономики цифровых финансовых активов и их перспективность для рынков инвестиционного капитала, однако в дельнейшем требуется достаточно серьезная работа по правовому регулированию ЦФА, анализу рисков его использования для предприятий реального сектора, а также создание определенных унифицированных алгоритмов, которые позволят превратить цифровые активы в полноценный инструмент финансирования. Материалы и методы. Проведение анализа возможностей применения цифровых финансовых активов как перспективных финансовых инструментов для реального сектора экономики основано на использовании основных положений Стратегии развития национальной платежной системы, что подразумевает рассмотрение термина «ЦФА» с точки зрения инновационного подхода к осуществлению инвестиционных операций и расширению сферы применения данных активов, в том числе и как самых современных средств платежа. Основу методического аппарата данного исследования составляет концептуально-теоретическое обобщение основных аспектов управления ЦФА, а также анализ преимуществ и недостатков внедрения цифровых активов для осуществления инвестирования и привлечения капитала на основе технологий блокчейна. Результаты исследования. Полученные в ходе исследования результаты свидетельствуют о том, что на сегодняшний день ЦФА, несмотря на то, что являются новым явлением на финансовом рынке, обладают большим потенциалом для развития за счет возможности повышения безопасности и скорости заключения финансовых сделок, что открывает для предприятий реального сектора экономики новые технологические горизонты в области совершенствования методов финансового обеспечения своей деятельности. Обосновано, что в этом случае ЦФА позволяют снизить средневзвешенную стоимость капитала за счет уменьшения транзакционных издержек на привлечение финансирования, а также обеспечить максимальную прозрачность финансовых операций, что повышает инвестиционную привлекательность компании и укрепляет доверие к ней со стороны инвесторов. Обсуждение и заключение. Выводы и обобщения, сделанные в рамках данной статьи, могут быть использованы в практике работы предприятий реального сектора экономики, рассматривающих возможность применения ЦФА для инвестирования и финансирования, облегчения доступа на рынок заимствований (что особенно важно для субъектов среднего и малого бизнеса), оптимизации торгового финансирования, доступа к новому рынку ликвидности, токенизации активов с целью ускорения привлечения финансирования под них. Научная значимость представленного в статье исследования заключается в идентификации основных преимуществ и ограничений выпуска цифровых финансовых активов для эмитентов предпрятий реального сектора экономики. Introduction. The digitalization of all forms of corporate relations and public life, due to the increasing influence of the fourth industrial revolution, leads to the widespread use of digital technologies by enterprises in the real sector of the economy, which consider them as tools to simplify the processes of concluding transactions and ensuring their security, as well as as a new source of attracting external investment capital. Pilot projects on the use of digital financial assets (CFA) are already underway as sources of increasing the volume of financial support, they have shown their high effectiveness, and the mechanism of their implementation has turned out to be quite simple and convenient for issuing companies. In this regard, it is possible to identify a fairly high potential for the use of digital financial assets in the real sector of the economy and their prospects for investment capital markets, however, serious work is required on the legal regulation of the CFA, an analysis of the risks of its use for real sector enterprises, as well as the creation of certain unified algorithms that will turn digital assets into a full-fledged a financing tool. Materials and Methods. The analysis of the possibilities of using digital financial assets as promising financial instruments for the real sector of the economy is based on the use of the main provisions of the Strategy for the Development of the national payment system, which implies considering the term CFA from the point of view of an innovative approach to investment transactions and expanding the scope of these assets, including the most modern means of payment. The basis of the methodological apparatus of this study is a conceptual and theoretical generalization of the main aspects of CFA management, as well as an analysis of the advantages and disadvantages of introducing digital assets for investing and raising capital based on blockchain technologies. Results. The results obtained during the study indicate that today, despite the fact that CFAs are a new phenomenon in the financial market, they have great potential for development due to the possibility of increasing the security and speed of concluding financial transactions, which opens up new technological horizons for enterprises in the real sector of the economy in the field of improving methods of financial support for their activities. It is proved that in this case, CFAs can reduce the weighted average cost of capital by reducing transaction costs for attracting financing, as well as ensure maximum transparency of financial transactions, which increases the investment attractiveness of the company and strengthens investor confidence in it. Discussion and Conclusion. The conclusions and generalizations made within the framework of this article can be used in the practice of enterprises in the real sector of the economy considering the possibility of using CFA for investment and financing – facilitating access to the borrowing market (which is especially important for medium and small businesses), optimizing trade finance, access to a new liquidity market, tokenization of assets to accelerate the attraction of financing for them. The scientific significance of the research presented in the article is to identify the main advantages and limitations of issuing digital financial assets for issuers of enterprises in the real sector of the economy.

Digitalization is reshaping finance, opening new market and development opportunities, and bringing with it new risks. Digital finance, or ‘fintech’, makes a difference by providing access to more, better and cheaper data, removing unnecessary intermediation, enhancing efficiency to reduce barriers and catalyzing innovation. The current crisis has transformed digital finance from a convenience into an existential lifeline. The UN Secretary General’s Task Force on Digital Financing for the Sustainable Development Goals (SDGs) highlights the potential of fintech, for example, to crowd in SDG-related risk and impact data into financing decisions, reduce financing costs, open opportunities for business innovations particularly those targeting the poor, and enable citizens to have a greater say over their money, as savers, lenders, investors, and tax payers. It also has identified risks that need to be mitigated if it is to be an enabler of financing aligned to the SDGs. One feature of digitalization, and a source of risk as well as opportunity, is that it enables ever-increasing returns to scale, increasing market concentration: ‘network effects’. A small number of digital finance platforms, often arising from ‘BigTech’ particularly e-commerce, social media, and indeed governments, have grown rapidly, a direction of travel likely to accelerate as a result of the crisis. These ‘global digital finance’ platforms, of ‘BigFintech’, will be increasingly impactful across the world, particularly in developing countries with smaller, weaker or under-developed financial systems, economies, and policy frameworks but where also the opportunities for transformation are greatest. With such extensive footprints, there is need, and yet challenges to securing the most appropriate policies, regulations, and broadly governance. Governance considerations of BigFintech are often narrowly focused, setting aside many SDG aspects, risks and opportunities. Moreover, they are likewise often not inclusive, notably of the voices of countries most likely to be directly impacted, particularly outside of the major economies. The Task Force’s ‘Dialogue on Global Digital Finance’ has been established as a complementary and supportive initiative aiming to enhance and rebalance governance debate, innovation and developments with these two factors in mind.

Research background: Big data-driven artificial Internet of Things (IoT) fintech algorithms can provide real-time personalized financial service access, strengthen risk management, and manage, monitor, and mitigate transaction operational risks by operational credit risk management, suspicious financial transaction abnormal pattern detection, and synthetic financial data-based fraud simulation. Blockchain technologies, automated financial planning and investment advice services, and risk scoring and fraud detection tools can be leveraged in financial trading forecasting and planning, cryptocurrency transactions, and financial workflow automation and fraud detection. Algorithmic trading and fraud detection tools, distributed ledger and cryptocurrency technologies, and ensemble learning and support vector machine algorithms are pivotal in predictive analytics-based risk mitigation, customer behavior and preference-based financial product and service personalization, and financial transaction and fraud detection automation. Credit scoring and risk management tools can offer financial personalized recommendations based on customer data, behavior, and preferences, in addition to transaction history, by generative adversarial and deep learning recurrent neural networks. Purpose of the article: We show that blockchain and edge computing technologies, generative artificial IoT-based fintech algorithms, and transaction monitoring and credit scoring tools can be harnessed in financial decision-making processes and loan default rate mitigation for transaction, payment, and credit process efficiency. Generative and predictive artificial intelligence (AI) algorithmic trading systems can drive coherent customer service operations, provide tailored financial and investment advice, and influence financial decision processing, while performing real-time risk assessment and financial and trading risk scenario simulation across fluctuating market conditions. Fraud and money laundering prevention tools, blockchain and financial transaction technologies, and federated and decentralized machine learning algorithms can articulate algorithmic profiling-based transaction data patterns and structures, credit assessment, loan repaying likelihood prediction, and interest rate and credit lending risk management by real-time financial pattern and economic forecast-based credit analysis across investment payment and transaction record infrastructures. Methods: Research published between 2023 and 2024 was identified and analyzed across ProQuest, Scopus, and the Web of Science databases by use of screening and quality assessment software systems such as Abstrackr, AMSTAR, AXIS, CADIMA, CASP, Catchii, DistillerSR, Eppi-Reviewer, MMAT, Nested Knowledge, PICO Portal, Rayyan, ROBIS, and SRDR+. Findings & value added: The main value added derived from the systematic literature review is that generative AI-based operational risk management, fraud detection, and transaction monitoring tools can provide personalized financial support and services and clarify financial and credit decisions and operations by financial decision-making process automation in dynamic business environments based on fraud detection capabilities and transaction data analysis and assessment. The benefits for theory and current state of the art are that credit risk and financial forecasting tools, artificial IoT-based fintech and generative AI algorithms, and algorithmic trading and distributed ledger technologies can be deployed in financial decision-making and customer behavior pattern optimization, credit score assessment, and money laundering and fraudulent payment detection. Policy implications reveal that investment management and algorithmic credit scoring tools can streamline financial activity operational efficiency, design financial planning analysis and forecasting, and carry out financial service and transaction data analysis for informed transaction decision-making and fraudulent behavior pattern and incident detection, taking into account credit history and risk evaluation and improving personalized experiences.

Financial transaction transparency has gradually become one of the main directions for the development and construction of the financial transaction market. This paper integrates blockchain and smart contracts and proposes a strategy to improve financial transaction transparency in order to protect transaction data privacy and identify and trace transaction anomalies. The proposed DM-IBBE scheme for smart contract transaction privacy involves choosing different interpolation points based on Lagrange interpolation curves and creating encryption modes that meet the requirements for financial transaction privacy. Based on a graph neural network, the propagation probability of abnormal transactions is calculated from the blockchain network topology using the TAGCN model, and the influence of irrelevant noise pairs is eliminated to realize the identification and traceability of abnormal transactions. Taking the financial transaction platform of City A as the research object and carrying out the practice of financial transaction optimization, the evaluation scores of the first-level indexes of comprehensive government transparency, transaction process transparency, operation result transparency, process service transparency, and operation transparency and guarantee are 76.58, 88.93, 95.42, 89.51, and 88.43, and except for the indexes of comprehensive government transparency, the other indexes are all greater than 80 points. The second-level indicators’ evaluation value increases from the 50–70 score range before financial transactions optimization to the 80–100 score range. The financial transaction platform in City A has significantly improved the transparency of financial transactions.

This chapter, contributed by Giada Valsangiacomo, presents the salient impacts of climate change on financial stability and discusses the role that central banks can play in addressing the resulting issues. After an overview of the commonly acknowledged features of the climate emergency, a brief illustration of climate-related risks together with transmission channels linking those risks to the financial system is outlined. The chapter will also show how the ensuing menace to financial stability, of which a new operational definition is given, provides scope for central banks to act without jeopardizing their existing mandates. To be read as a critique of mainstream analysis, the analysis also points out how radical uncertainty, as defined by Frank H. Knight a century ago, is a factor that utterly reshapes the analytical framework and thus the range of potential central bank responses. The latter should focus not only on merely upgrading macroprudential policies, but more importantly on implementing precautionary measures, which would ultimately improve financial market resilience to climate-related risks. Most notably, this new financial stability framework would impart clear guidance to the greening of financial markets and momentum for an orderly transition to a carbon-neutral economy. Despite the valuable promise of these measures, strong evidence persists that central bank action would need to be supported by a more comprehensive set of economic policies, in order to effectively overcome both climate and financial instability.

Human behavior as they engaged in financial activities is intimately connected to the observed market dynamics. Despite many existing theories and studies on the fundamental motivations of the behavior of humans in financial systems, there is still limited empirical deduction of the behavioral compositions of the financial agents from a detailed market analysis. Blockchain technology has provided an avenue for the latter investigation with its voluminous data and its transparency of financial transactions. It has enabled us to perform empirical inference on the behavioral patterns of users in the market, which we explore in the bitcoin and ethereum cryptocurrency markets. In our study, we first determine various properties of the bitcoin and ethereum users by a temporal complex network analysis. After which, we develop methodology by combining k-means clustering and Support Vector Machines to derive behavioral types of users in the two cryptocurrency markets. Interestingly, we found four distinct strategies that are common in both markets: optimists, pessimists, positive traders and negative traders. The composition of user behavior is remarkably different between the bitcoin and ethereum market during periods of local price fluctuations and large systemic events. We observe that bitcoin (ethereum) users tend to take a short-term (long-term) view of the market during the local events. For the large systemic events, ethereum (bitcoin) users are found to consistently display a greater sense of pessimism (optimism) towards the future of the market.

The exponential growth of digital finance—encompassing online banking, digital assets, decentralized finance (DeFi), and algorithmic trading—has intensified the need for robust cybersecurity frameworks. However, the rise of quantum computing presents a dual challenge: while it enables revolutionary advances in data analytics and optimization, it simultaneously threatens the cryptographic foundations of contemporary financial systems. This research explores the emerging field of quantum cybersecurity and its implications for safeguarding financial infrastructures in the post-quantum era. Traditional encryption methods such as RSA, ECC, and Diffie–Hellman key exchange are vulnerable to quantum attacks, particularly through Shor’s algorithm and Grover’s search algorithm, which can efficiently break asymmetric and symmetric cryptographic schemes. The study evaluates quantum-resistant cryptographic protocols—including lattice-based, hash-based, and multivariate polynomial encryption—as viable solutions for ensuring financial data integrity, transaction confidentiality, and regulatory compliance in quantum-vulnerable environments. Furthermore, it investigates Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as hardware-assisted techniques for achieving unconditional security in financial communications and transaction authentication. By integrating quantum cryptography, hybrid encryption, and AI-driven threat modeling, this work outlines a roadmap for financial institutions transitioning toward quantum-secure infrastructures. The findings demonstrate that quantum cybersecurity is not merely a defensive measure but a transformative enabler for resilient digital finance, aligning with global efforts to achieve technological sovereignty, financial stability, and sustainable innovation in the era of quantum computing.

Financial Cryptography is substantially complex, requiring skills drawn from diverse and incompatible, or at least, unfriendly, disciplines. Caught between Central Banking and Cryptography, or between accountants and programmers, there is a grave danger that efforts to construct Financial Cryptography systems will simplify or omit critical disciplines.This paper presents a model that seeks to encompass the breadth of Financial Cryptography (at the clear expense of the depth of each area). By placing each discipline into a seven layer model of introductory nature, where the relationship between each adjacent layer is clear, this model should assist project, managerial and requirements people.Whilst this model is presented as efficacious, there are limits to any model. This one does not propose a methodology for design, nor a checklist for protocols. Further, given the young heritage of the model, and of the field itself, it should be taken as a hint of complexity rather than a defining guide.