Author: Zhang Feng
Key Points: The reshaping of the global financial system by AI and tokenization is an inevitable trend, bringing both a revolution in efficiency and challenges in risk, driving the global financial governance system towards a more collaborative, intelligent, and inclusive direction.
The Bank for International Settlements (BIS), with its supranational coordination position, mature rule-making experience, cutting-edge research capabilities, and extensive international cooperation network, has become the core leader and key supporter of this financial governance transformation.
In the context of profound changes in the global financial system, central banks and regulatory agencies should further strengthen cooperation with the BIS to jointly improve the global governance framework for AI and tokenization.
Financial institutions, non-financial enterprises, and investors should actively integrate into the global financial governance system led by the BIS, with compliance as the baseline, innovation as the driving force, and risk prevention and control as the core, to jointly promote the healthy development of AI and tokenization technologies, strengthen the defense line for global financial stability, and build an efficient, stable, and resilient global financial system.
The deep integration of artificial intelligence (AI) and tokenization technology is fundamentally reconstructing the operational model of the global financial system, driving a revolution in the efficiency of financial transactions, asset pricing, and capital flow, while also bringing systemic risk challenges across markets and jurisdictions. The regional segmentation of traditional financial governance frameworks sharply contrasts with the global penetration of technological innovation, significantly increasing the difficulty of maintaining financial stability.
The Bank for International Settlements (BIS), as a cooperative platform for central banks worldwide, has become a key player in addressing the challenges of AI and tokenization, strengthening the global financial stability defense line, thanks to its supranational coordination capabilities, mature regulatory rule-making experience, and cutting-edge financial research system. It can be said that clarifying the multiple impacts of AI and tokenization on the financial system while balancing technological innovation and risk prevention, and leveraging the BIS's core supporting role to promote effective connections between various market entities and the BIS governance system, has become a core issue in upgrading the global financial governance system.
I. AI and Tokenization Reshape the Underlying Logic of Financial Operations, Opening a New Era of Efficiency Revolution
The intelligent decision-making capabilities of AI and the value transfer advantages of tokenization complement each other, fundamentally changing the operational methods of traditional finance from trade execution, asset management, financing models to cross-border settlement, driving the financial system towards digitalization, intelligence, and efficiency, becoming the core driving force of financial technology development.
Tokenization, based on distributed ledger technology (DLT), transforms physical and financial assets such as securities, deposits, and accounts receivable into programmable digital tokens, achieving integrated asset trading, settlement, and collateral management, fundamentally addressing the pain points of insufficient asset liquidity, cumbersome transaction processes, and excessive intermediaries in the traditional financial system.
In the field of cross-border payments and securities settlement, tokenization technology has shortened the settlement time from the traditional T+3 to seconds, significantly reducing transaction costs and operational risks, especially for the Asia-Pacific region, where trade and financial connections are close, becoming an important lever for enhancing cross-border financial efficiency.
In supply chain finance scenarios, data shows that the tokenization of accounts receivable has shortened the financing cycle for small and medium-sized enterprises from 3 months to 7 days, effectively solving the industry problem of difficult and expensive financing for SMEs; in the private equity sector, data indicates that tokenization of shares has reduced the exit cycle by more than 50% when addressing the traditional secondary circulation issues of private equity, activating liquidity in the private equity market.
AI, on the other hand, is moving from assisting decision-making to autonomous execution, becoming the core productivity of financial institutions and driving the intelligent upgrade of financial operations. AI agents can autonomously handle transaction execution, liquidity management, anomaly detection, and other financial operations, significantly enhancing response speed and achieving disruptive breakthroughs in scenarios such as credit risk control, wealth management, and trading markets.
In the field of credit risk control, AI technology has shifted from traditional "amount red line" management to dynamic "risk profiling" assessment, with data showing that risk control accuracy has exceeded 95%, effectively enhancing financial institutions' risk identification and management capabilities; in wealth management, AI financial management can provide customized asset allocation services to clients 24/7, with data indicating that it can reduce wealth management fees by 60%, achieving inclusive wealth management;
In trading markets, AI has restructured the entire trading decision-making process for financial products such as foreign exchange and stocks, increasing execution efficiency several times, becoming the core support for high-frequency trading and algorithmic trading. At the same time, AI's data synthesis technology can virtually generate compliant financial data, effectively addressing the issues of data silos and privacy protection in the financial sector, providing data support for the intelligent transformation of financial institutions.
The integration of AI and tokenization has given rise to a new paradigm of programmable finance, achieving a deep combination of financial assets and smart contracts. Smart contracts can automatically execute financial terms such as repayments, dividends, and settlements based on preset conditions without the need for third-party intermediaries, significantly reducing transaction costs and greatly minimizing human operational risks and moral hazards. AI technology provides intelligent decision support for programmable finance, dynamically adjusting the execution conditions of smart contracts through real-time analysis and prediction of market data, making the flow of financial assets more aligned with market demand.
The integration of the two promotes the unified and standardized development of global financial markets, while breakthroughs in cross-chain technology break down blockchain silos, laying the foundation for the formation of a globally unified digital asset market, making the optimal allocation of financial resources on a global scale possible.
II. The Dual Impact of AI and Tokenization Intensifies the Complexity and Challenges of Financial Stability
While AI and tokenization drive improvements in financial efficiency, they also bring new financial risks, with their technical characteristics determining that risks are characterized by rapid propagation, wide-ranging impacts, and strong concealment, intertwining with traditional financial risks and exacerbating the fragility of the financial system, posing multiple challenges to financial stability. The coexistence of technological innovation and financial fragility has become a core difficulty in maintaining global financial stability.
From the perspective of tokenization, its risks mainly focus on technical security, regulatory arbitrage, and asset pricing. On one hand, tokenized assets rely on distributed ledger technology, and issues such as technical vulnerabilities in blockchain systems and hacker attacks may lead to asset theft and transaction anomalies, triggering market panic; on the other hand, the cross-border circulation characteristics of tokenized assets make them susceptible to being tools for regulatory arbitrage, with some criminals exploiting regulatory differences across jurisdictions to engage in illegal financial activities such as virtual currency trading speculation and token issuance financing, disrupting economic and financial order and breeding money laundering, illegal fundraising, and fraud.
Moreover, some tokenized assets lack real asset backing, leading to issues such as price manipulation and excessive speculation, with numerous examples to illustrate this. At the same time, stablecoins, as an important form of tokenization, have an inherent fragility in their peg mechanism to fiat currencies; once there are insufficient reserves or a collapse of market confidence, it may lead to the decoupling of stablecoins, which could then transmit risks to the traditional financial system, triggering systemic risks.
From the perspective of artificial intelligence, issues such as algorithmic black boxes, model biases, and third-party dependencies have become new hidden dangers to financial stability. The complexity and opacity of AI algorithms lead to prominent "algorithmic black box" problems, making it difficult for financial institutions to effectively verify and trace the logic of AI decisions. Once an algorithm deviates or errors occur, it may trigger large-scale trading mistakes, leading to market volatility. Additionally, AI models rely on vast amounts of historical data for training; if the data is biased or distorted, it may result in erroneous decision outputs from the model, triggering a chain reaction of credit risk and market risk.
Furthermore, an increasing number of financial institutions rely on third-party AI technology service providers for core algorithms and technical support, creating new third-party dependency risks. If a third-party service provider experiences technical failures, operational crises, or data breaches, it may affect the normal operations of numerous financial institutions, leading to cross-institutional risk contagion. More concerning is the use of AI technology to manipulate financial markets, with some institutions using AI robots to manipulate social media sentiment and inflate asset valuations, engaging in market manipulation behaviors such as "pump and dump," severely undermining market fairness and integrity.
The integration of AI and tokenization further amplifies the propagation effects of financial risks, presenting systemic characteristics across markets, fields, and jurisdictions. On one hand, the high-speed computing capabilities of AI significantly enhance the transmission speed of risks between tokenized asset markets and traditional financial markets, allowing localized market risks to spread to the global financial system in a short time; on the other hand, the cross-border circulation characteristics of tokenized assets combined with AI's global service capabilities enable financial risks to transcend geographical limitations, forming risk contagion on a global scale.
Additionally, the fusion of AI and tokenization has also given rise to new financial formats and products, which often exist in regulatory gray areas and lack comprehensive risk prevention and control mechanisms, becoming potential hazards to financial stability. For example, AI-driven decentralized finance (DeFi) projects combine AI's intelligent decision-making with the decentralized characteristics of tokenization, but their reliance on unverified algorithms and opaque governance structures can easily lead to liquidity crises and market collapses.
III. AI and Tokenization Change the Evolutionary Patterns of Risks, Increasing the Difficulty of Risk Identification for Central Banks and Regulatory Agencies
The technical characteristics of AI and tokenization fundamentally change the mechanisms, transmission paths, and manifestations of financial risks, making traditional risk identification methods and regulatory tools inadequate to adapt to new risk features, posing significant challenges for central banks and regulatory agencies in risk identification, monitoring, and control, pushing financial regulation towards intelligent and collaborative transformation.
The issue of lag in risk identification is prominent, as traditional monitoring systems struggle to capture dynamic risks in real-time. Traditional financial risk monitoring mainly relies on retrospective financial data and transaction data statistical analysis, which is clearly lagging. In the context of AI and tokenization, the speed of financial transactions has significantly increased, and processes are highly automated, with the generation and transmission of risks occurring instantaneously, making traditional post-event monitoring models ineffective in identifying real-time risks.
For example, AI-driven high-frequency trading can complete a large number of transactions in milliseconds; if an algorithm errors, it may trigger large-scale trading anomalies in a short time, while traditional monitoring systems of regulatory agencies struggle to capture such dynamic risks in real-time. The second-level settlement characteristics of tokenized assets also significantly enhance the speed of risk transmission, making it difficult for regulatory agencies to timely detect and block risk propagation. At the same time, the trading data of tokenized assets is dispersed across different blockchain nodes, and the decision data of AI algorithms is concealed, making it challenging for regulatory agencies to comprehensively aggregate and analyze relevant data, leading to insufficient coverage in risk identification.
The concealment and complexity of risks have increased the technical difficulty of risk identification. The "black box" nature of AI algorithms makes it difficult to trace the causes of financial risk generation, preventing regulatory agencies from accurately determining whether risks are caused by market factors, human operations, or algorithm errors, making it challenging to take targeted control measures. The programmability of tokenized assets complicates the structure of asset transactions, with some criminals exploiting the complex design of smart contracts to hide illegal activities such as money laundering and illegal fundraising, increasing the difficulty of risk identification for regulatory agencies. Furthermore, the fusion of AI and tokenization has given rise to cross-field and cross-format financial products, intertwining traditional financial risks with technological risks, forming composite risks. Regulatory agencies need to possess expertise in financial regulation, artificial intelligence, blockchain, and other fields to effectively identify such risks, raising higher demands for the professional capabilities of regulatory agencies.
The contradiction between regulatory fragmentation and the cross-border transmission of risks makes it difficult for a single regulatory agency to achieve comprehensive risk identification. AI and tokenization technologies possess global service capabilities, and related financial businesses and asset transactions break through geographical limitations, exhibiting characteristics of cross-border operations, while financial regulation still operates within the confines of national jurisdictions, leading to significant regulatory fragmentation issues. The regulatory rules, technical standards, and monitoring capabilities of various national regulatory agencies differ, making it challenging to achieve coordinated monitoring and risk identification for cross-border AI financial businesses and tokenized asset transactions, easily creating regulatory vacuums.
For example, some AI-driven tokenization projects establish their operational entities in countries and regions with relaxed regulations, providing services to global investors, making it difficult for a single national regulatory agency to conduct comprehensive risk monitoring and control. At the same time, the information-sharing mechanisms between national regulatory agencies are inadequate, making real-time exchange of cross-border risk data difficult, resulting in regulatory agencies being unable to timely grasp the transmission paths and impact ranges of cross-border risks, significantly reducing the effectiveness of risk identification.
Traditional regulatory tools and methods struggle to meet the demands of technological innovation, limiting regulatory effectiveness. The traditional regulatory tools of central banks and regulatory agencies are primarily designed for conventional financial businesses, such as capital adequacy ratios and liquidity coverage ratios, making it difficult to effectively measure the technological risks and algorithmic risks brought by AI and tokenization; traditional methods such as on-site inspections and off-site supervision also struggle to adapt to the automation and intelligence characteristics of AI financial businesses, failing to effectively verify the rationality of AI algorithms and the compliance of smart contracts.
Moreover, there is a gap between the technical capabilities of regulatory agencies and financial institutions, with some financial institutions' AI and blockchain technologies being at the industry-leading level, while regulatory agencies lack corresponding technical means and talent reserves, making it difficult to effectively supervise the technological systems of financial institutions, exacerbating the issue of "regulation lagging behind innovation."
IV. The Characteristics of Cross-Border Governance Are Highlighted, and the BIS Becomes the Core Support for Global Financial Stability and Governance
The cross-border operational characteristics of AI and tokenization determine that their governance must have a global perspective; regulatory measures from a single country are insufficient to effectively address financial risks across jurisdictions, and the demand for global financial governance coordination has increased significantly. As the "bank for central banks," the Bank for International Settlements (BIS) has become the core support for addressing the challenges of AI and tokenization and maintaining global financial stability, playing an irreplaceable role in global financial governance due to its supranational coordination position, comprehensive regulatory rule system, cutting-edge financial research capabilities, and extensive international cooperation network.
The supranational nature of the BIS and its positioning as a global central bank cooperation platform provide a core vehicle for cross-border financial governance. The BIS is jointly owned by 63 central banks, does not serve ordinary market entities, and focuses on providing clearing, reserve management, and policy coordination platforms for central banks, possessing inherent supranational attributes and neutrality. Headquartered in Basel, Switzerland, it enjoys diplomatic immunity, allowing it to freely allocate funds across borders and engage in international cooperation, effectively breaking through geographical limitations and national sovereignty barriers, becoming an important platform for communication and policy coordination among central banks. The monthly meetings of central bank governors in Basel have become important "closed-door meetings" for global monetary policy coordination and financial risk prevention, where major central banks such as the Federal Reserve, European Central Bank, and Bank of Japan share policy trends, market observations, and risk information, forming a consensus on global financial governance.
In the context of AI and tokenization, the BIS's platform advantage is further highlighted, providing important guarantees for central banks to coordinate regulatory rules, share risk data, and jointly respond to cross-border risks, effectively addressing coordination challenges in global financial governance.
The BIS leads the formulation of global financial regulatory rules, providing standard support for the governance of AI and tokenization. Since the 1970s, the Basel Committee on Banking Supervision (BCBS), established under the leadership of the BIS, has successively introduced the Basel Accords I, II, and III, constructing the core rule system for global banking supervision, becoming the "bible" of global financial regulation. The Basel Accords have evolved from initial capital adequacy requirements to encompass minimum capital requirements, supervisory authority oversight, and market constraints as the "three pillars," introducing indicators such as liquidity coverage ratios, net stable funding ratios, leverage ratios, and countercyclical capital buffers, forming a comprehensive macro-prudential regulatory framework that effectively enhances the risk prevention capabilities of the global banking industry.
In the era of AI and tokenization, the BIS, leveraging its rule-making experience, collaborates with international organizations such as the Financial Stability Board (FSB) and the International Monetary Fund (IMF) to promote the establishment of a global governance framework for AI and digital finance, focusing on core issues such as data governance in the field of artificial intelligence, third-party dependencies, and the definition of operational responsibilities in the tokenization process. At the same time, the BIS is also promoting international recognition of regulatory rules, reducing discrepancies in national regulatory rules, preventing regulatory arbitrage, and providing unified regulatory standards for the cross-border development of AI and tokenization.
The BIS relies on cutting-edge research and innovative practices to provide technical and practical support for the governance of AI and tokenization. The BIS has established multiple innovation centers focusing on research and practice in cutting-edge technologies such as digital finance, artificial intelligence, and blockchain, conducting pilot projects in areas such as central bank digital currencies (CBDC), tokenized assets, and AI regulatory technology, providing technical references and practical experiences for central banks.
For example, the CBDC cross-border settlement pilot conducted by the BIS Innovation Hub in Hong Kong explored the interconnectivity model between central bank digital currencies and tokenized assets, providing practical pathways to address cross-border payment challenges and standardize tokenized asset transactions. Additionally, the BIS has a world-class financial research team capable of timely tracking the development trends of AI and tokenization, analyzing their impacts on the financial system, predicting potential financial risks, and providing cutting-edge research results and decision-making references for central banks' policy formulation and risk prevention. The BIS also disseminates financial risk information globally through the publication of global financial stability reports and financial risk assessments, guiding central banks and financial institutions in effective risk prevention.
The BIS promotes collaborative cooperation among diverse international organizations, constructing a comprehensive global financial governance network. Global financial governance involves multiple entities, including central banks, regulatory agencies, and international organizations, making it difficult for a single institution to achieve comprehensive governance. The BIS fully leverages its coordinating role to promote division of labor and cooperation with international organizations such as the FSB, IMF, and World Bank, constructing a comprehensive and multi-layered global financial governance network. Among them, the FSB Secretariat is located at the BIS, primarily responsible for promoting the implementation of global financial regulatory standards by various countries and assessing their regulatory compliance; the IMF focuses on macroeconomic stability and balance of payments, forming a "macro + micro" synergy for financial risk prevention with the BIS; the World Bank collaborates with the BIS in areas such as financial inclusion and the construction of financial systems in developing countries, promoting the inclusiveness of global financial governance.
In the governance of AI and tokenization, the BIS, in collaboration with these international organizations, has formed a full-chain governance system encompassing rule-making, standard execution, macro-control, and risk monitoring, effectively enhancing the efficiency and effectiveness of global financial governance. At the same time, the BIS actively strengthens cooperation with emerging markets and developing countries, helping them enhance their financial regulatory capabilities and technical levels, narrowing the North-South gap in global financial governance, and promoting fairness and democratization in the global financial governance system.
V. Recognizing the Core Value of BIS Governance, Various Market Entities Actively Integrate into the Global Financial Governance System
The BIS plays a core supporting role in global financial governance in the context of AI and tokenization, and the regulatory rules it formulates, the risk information it publishes, and the international cooperation it promotes have significant guiding implications for the operational decisions and risk prevention of financial institutions, other enterprises, and investors. Various market entities should proactively recognize the role of the BIS and actively integrate into the global financial governance system led by the BIS, achieving a win-win situation for their own development and global financial stability.
Financial institutions need to incorporate BIS regulatory rules and risk guidelines into their core management systems, enhancing compliance operations and risk prevention capabilities. As core participants in the financial market, banks, securities firms, insurance companies, and other financial institutions are the main application entities of AI and tokenization technologies and also bear the primary responsibility for financial risks, making it essential to adhere to BIS regulatory rules as fundamental compliance.
On one hand, financial institutions should strictly implement the macro-prudential regulatory requirements set by the BIS, such as capital adequacy ratios and liquidity coverage ratios, and, in conjunction with the characteristics of AI and tokenization businesses, supplement and improve capital provisions and risk preparations for technological risks, algorithmic risks, and cross-border risks, enhancing their risk resilience;
On the other hand, financial institutions should closely monitor the financial risk reports and risk warning information published by the BIS, timely adjust their operational strategies, and establish and improve internal risk prevention systems targeting risk points such as AI algorithm black boxes and cross-border circulation of tokenized assets, strengthening access management and ongoing monitoring of AI technology service providers to prevent third-party dependency risks.
At the same time, financial institutions should actively participate in the innovative pilot projects in digital finance and AI regulatory technology conducted by the BIS, strengthen exchanges and cooperation with global peers, learn advanced risk management experiences and technical methods, and enhance their levels of intelligent and digital transformation, achieving a balanced development of compliance and innovation. Additionally, financial institutions should strengthen cross-border business information disclosure in accordance with BIS requirements and actively cooperate with national regulatory agencies in cross-border regulatory collaboration to prevent regulatory arbitrage.
Non-financial enterprises should rely on the BIS governance framework to规范开展 AI and tokenization-related businesses, achieving stable development. With the proliferation of financial technology, an increasing number of non-financial enterprises are beginning to engage in AI and tokenization-related businesses, such as supply chain companies conducting accounts receivable tokenization, technology companies developing AI financial technologies, and e-commerce companies providing embedded financial services. The operational behaviors of these enterprises not only affect their own development but may also trigger financial risks.
Non-financial enterprises should proactively understand the digital finance governance framework and regulatory standards established by the BIS, clarify the compliance boundaries of their businesses, and avoid engaging in illegal financial activities such as unapproved token issuance and virtual currency trading. For enterprises conducting asset tokenization and AI financial services, they should establish and improve their business operation systems and risk prevention mechanisms in accordance with BIS requirements, strengthen the auditing and verification of smart contracts, ensure that tokenized assets have real asset backing, and prevent price manipulation and excessive speculation; at the same time, they should enhance cooperation with financial institutions, relying on the compliance systems and risk control capabilities of financial institutions to规范开展相关业务.
Additionally, non-financial enterprises should closely monitor the technical development guidelines and risk alerts published by the BIS, strengthen the research and application management of AI algorithms and blockchain technologies, prevent technical vulnerabilities and hacker attacks, and ensure stable business operations.
Investors should use the risk information and research results from the BIS as the basis for decision-making, establishing a rational investment philosophy to prevent investment risks. In the context of AI and tokenization, new investment targets such as digital assets and AI financial products are continuously emerging, significantly increasing investment risks, while ordinary investors often lack professional risk identification capabilities, making them vulnerable to illegal financial activities.
Investors should actively pay attention to the global financial stability reports, risk warning information, and research results published by the BIS, understand the potential risks in the fields of AI and tokenization, establish a rational investment philosophy, and steer clear of illegal investment activities such as virtual currency trading speculation and unapproved token issuance.
At the same time, investors should refer to the BIS's evaluation standards for digital financial products and conduct comprehensive due diligence on investment targets, focusing on core elements such as the authenticity of underlying assets, transparency of governance structures, and compliance, to prevent investment losses caused by algorithm errors, lack of transparency in governance, and regulatory arbitrage.
For institutional investors, they should incorporate the BIS's risk assessment methods into their investment decision-making systems, strengthen risk modeling and valuation analysis of AI and tokenization investment targets, and enhance the scientific and rational nature of investment decisions; at the same time, they should actively participate in international investor protection cooperation promoted by the BIS, strengthening information sharing with global peers to jointly prevent cross-border investment risks.
Various market entities should also actively participate in the global financial governance exchanges and cooperation led by the BIS, contributing to the improvement of the global financial governance system. Global financial governance in the context of AI and tokenization is a dynamic and evolving process that requires the joint participation of governments, international organizations, and market entities. Various market entities should fully leverage their practical advantages, actively participate in seminars, pilot projects, and rule-making consultations organized by the BIS, and provide feedback to the BIS on practical issues and governance needs encountered in the application process of AI and tokenization, offering practical evidence for the BIS to formulate more scientific, reasonable, and operable regulatory rules.
At the same time, financial institutions and non-financial enterprises should strengthen cooperation with global peers to jointly explore compliant application models for AI and tokenization, establish industry self-regulatory standards to fill the gaps in official regulation, and promote the formation of a dual governance system of "official regulation + industry self-regulation." Investors should reflect their investment demands to the BIS and regulatory agencies through industry associations and investor protection organizations, promoting the improvement of investor protection mechanisms and creating a fair, just, and transparent investment environment.
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