This study examines the theoretical, structural, and empirical applications of Artificial Intelligence (AI) and Machine Learning (ML) architectures within the domain of regulatory compliance (RegTech) and supervisory technology (SupTech) for cross-border digital transactions. The exponential expansion of cross-border financial flows, real-time payment systems, and decentralized financial instruments has amplified regulatory fragmentation, multi-jurisdictional compliance friction, and sophisticated financial crime typologies. Utilizing institutional economics, information asymmetry theory, and computational compliance modeling, this paper analyzes how advanced algorithmic architectures—specifically Graph Neural Networks (GNNs), Natural Language Processing (NLP), and Federated Learning—optimize anti-money laundering (AML), counter-terrorist financing (CFT), and real-time sanctions screening. The findings demonstrate that shifting from legacy rule-based heuristics to adaptive, privacy-preserving AI frameworks significantly compresses false-positive rates, bridges cross-jurisdictional regulatory disparities, and establishes a dynamic, mathematically rigorous paradigm for global financial integrity.
The increasing reliance on digital banking solutions has significantly transformed financial services, with Automated1Teller1Machine (ATM) transactions playing a critical role in banking operations. This study examines the impact of ATM transactions on the1 financial performance of Deposit Money Banks (DMBs) in Nigeria, utilizing a Robust Least Squares (RLS) estimation technique to analyze quarterly data from 2009 to 2023. The study employs Return on Assets (ROA), Return on Equity (ROE), and Capital Adequacy Ratio1 (CAR) as proxies for financial performance. The findings reveal that while ATM transactions exhibit a statistically insignificant effect on ROA and ROE, they have a significant positive relationship with CAR, suggesting that ATM services contribute more to the financial stability of banks than to their profitability. The study also highlights key challenges associated with ATM usage, including network failures, fraud risks, and high maintenance costs, which may limit its full potential in enhancing bank performance. Given these findings, the study recommends that Nigerian banks strengthen ATM infrastructure, enhance cybersecurity measures, integrate emerging technologies such as blockchain, and implement customer education programs to optimize ATM efficiency and mitigate associated risks. These measures will enhance financial inclusion, improve customer satisfaction, and sustain the overall financial health of deposit money banks in Nigeria.
Pre-analysis commitment for a study of deposit rate sensitivity across U.S. bank size classes over the 2021 to 2024 tightening cycle, using FDIC Call Report data. The plan fixes the estimator, sample, comparison groups, controls, reported statistics, robustness variants, and the threshold for what counts as a finding. The file was written on August 25, 2026, before any data was retrieved. It was deposited here on August 27, 2026, after estimation had been carried out. This deposit therefore establishes the content and the deposit date. It does not independently verify that the file predates the estimation, and no claim to that effect is made. Departures from the plan are recorded in a deviation log accompanying the analysis. The work is funded by the Blockchain Association. The author retains the right to publish the findings regardless of what they show.
Omar A. Esqueda, Mohammad Sharif Karimi, Daniel P. Liston, Saleh Ghavidel Doostkouei
This paper investigates the dynamic relationship between macroeconomic factors—particularly Bitcoin pricing—and the equity returns of firms in the financial technology (FinTech) sector. Using a Structural Vector Autoregression (SVAR) framework with daily data from July 2013 to March 2025, the analysis examines how shocks in major financial variables affect FinTech equity performance. The results indicate that positive shocks to the S&P 500 index are associated with a significant increase in the FinTech sector indicator, underscoring the sector’s close linkage with overall equity market performance. Shocks to the 10-year U.S. Treasury bond yield also generate a positive but comparatively weaker and delayed response, suggesting a secondary influence of interest rate dynamics. In contrast, Bitcoin price shocks do not produce a statistically significant effect on FinTech returns, implying limited spillovers from cryptocurrency markets to traditional FinTech equities. Robustness checks using PARCH and TARCH models confirm the stability of these findings. Overall, the evidence suggests that FinTech firms remain more sensitive to developments in conventional financial markets than to movements in digital asset prices, highlighting the sector’s growing integration with institutional finance rather than speculative crypto-based activity.
This study examines whether major cryptocurrency returns respond systematically to scheduled Federal Reserve (Fed) interest rate announcements and whether FOMC-window movements are explained more by realised policy decisions or by broader risk-sentiment conditions. Using daily data for Bitcoin, Ethereum, XRP, Dogecoin, Solana, the U.S. Dollar Index, and VIX, the analysis covers 43 scheduled FOMC announcements between 2021 and 2026. Six cumulative event-window returns are evaluated through parametric mean tests, Wilcoxon signed-rank tests, and panel event-study regressions with crypto fixed effects and FOMC-event-clustered standard errors. Because the available surprise measure contains only two nonzero observations, the study focuses on realised rate changes, hike/cut/hold categories, asymmetric rate-change magnitudes, VIX changes, and DXY returns rather than formal monetary policy shocks. The results provide little evidence that cryptocurrency returns differ systematically from zero around FOMC announcements. Actual rate changes, policy-direction categories, and asymmetric hike/cut magnitudes do not robustly explain event-window returns, and crypto-specific interaction models provide no stable evidence of heterogeneous sensitivity across assets. By contrast, VIX changes are negatively and significantly associated with cryptocurrency returns in several windows, while DXY effects are weak and unstable. The study contributes by showing that FOMC-window cryptocurrency performance is better explained by risk-sentiment conditions than by the realised size or direction of Fed rate decisions.
The rapid development of cryptocurrencies, stablecoins, and central bank digital currencies (CBDCs) has transformed the global monetary landscape and accelerated the transition toward a cashless society. While critics argue that digital currencies threaten financial stability due to volatility, disintermediation, energy consumption, and regulatory concerns, this paper contends that the increasing competition among digital and fiat currencies can generate significant economic benefits. By examining the evolution of cryptocurrencies, the emergence of stablecoins, the global adoption of CBDCs, and the case of Zimbabwe's hyperinflation, this study argues that currency competition encourages governments to pursue more disciplined fiscal and monetary policies, strengthens policy credibility, and helps anchor inflation expectations. Greater monetary credibility also expands policymakers' ability to respond effectively to future economic downturns. Although digital currencies present important risks, many of these challenges can be mitigated through technological innovation, appropriate regulation, and institutional development. Overall, this paper concludes that a wellmanaged transition toward a cashless society can promote competition, innovation, and long-term economic resilience rather than undermine financial stability.
The Triadic Stress Index (TSI) takes a network index whose four factors were first observed in soil microbiome co-occurrence networks and applies it, without alteration, to the correlation network of financial assets. We test it on five markets spanning 2006-2026 (equities including banking crises and the AI sector, cryptocurrencies, commodities, foreign exchange and sovereign debt), against three independent definitions of a crisis episode, at a fixed alarm budget, out of sample, with block-bootstrap intervals and a Holm correction across the family of tests. The benchmarks are the Absorption Ratio, the industry standard used by MSCI and central banks; the effective rank and the Vendi score, the sharpest spectral measures available; Ollivier-Ricci curvature; and the global and local balance indices of signed correlation networks. Three comparisons favour the index. It carries a per-node decomposition, diag(A^3), naming which asset is carrying the concentration with no parameter to select, and scores 0.97-0.99 against 0.33-0.84 for the only published per-node alternative, whereas spectral attribution must first choose how many components to read and collapses under a standard but wrong choice. Its alarms are the cleanest of anything tested, 4.0% of them with no matching episode against 14.7% for the effective rank and roughly 59% for the Absorption Ratio. And it beats the Absorption Ratio on detection by 0.273 in F1 out of sample, p<0.0005. The remaining comparisons are ties. Against the effective rank and the Vendi score the index ties in every scheme and both samples, and the margin over the Absorption Ratio narrows under the strictest labelling. On real matrices the far simpler node degree reproduces the attribution. A lead-lag analysis puts the peak cross-correlation at zero lag: this is a coincident state index, not a forecast.
Decentralized Finance (DeFi) refers to an open financial ecosystem built on blockchain technology that does not require the participation of centralized institutions. The technology and operational mechanisms it employs represent a significant "paradigm mismatch" with the current financial regulatory framework. This paper examines the comprehensive impact of DeFi on existing financial regulation from multiple perspectives, including the blurring of regulatory authority and a lack of accountability; the difficulty in identifying regulatory targets and the ambiguity in determining their nature; the ineffectiveness of regulatory rules and the absence of relevant provisions; overlapping jurisdictions, and difficulties in enforcement. Through a comparative study of regulatory experiences in the United States, Europe, and other regions, this paper proposes solutions such as shifting the existing regulatory philosophy toward functional regulation, embedding compliance requirements into the underlying technology at the institutional level, and strengthening international cooperation at the operational level, while also discussing the specific context in China. This paper identifies a threefold paradigm mismatch between decentralized finance and traditional financial regulation, giving rise to multiple regulatory challenges such as difficulties in holding entities accountable, ambiguity in defining regulatory targets, ineffective regulatory rules, and obstacles to cross-border enforcement. A comparison of regulatory practices in the U.S. and Europe reveals that it is difficult for any single country to independently manage the risks associated with globalized DeFi.
This article presents a novel, first-of-its-kind predictive RegTech solution to address this challenge using machine learning methods. The rapid global adoption of Real‑Time Payment Systems (RTPS) has created a significant “velocity gap” in regulatory compliance. While expanding financial accessibility, these systems introduce new vulnerabilities into existing AML frameworks. Static, rule-based systems and batch processing architectures cannot effectively counter money laundering in sub-second transaction environments. This limitation enables sophisticated activities such as digital layering and smurfing that move illicit financial flows across networks faster than regulatory systems can react. The core of our solution involves the use of graph neural networks (GNNs). This approach enables real-time, pre-settlement risk assessment, preventing illicit transactions before execution. Unlike traditional AML systems that evaluate transactions in isolation, this framework analyzes the entire transaction network to detect coordinated illicit behavior in real time. GNNs capture complex structures such as loops, funnels, and bridges that indicate illicit activity. To support efficient implementation, the framework integrates Event-Driven Architecture (EDA). The proposed architecture introduces the concept of the Zero-Knowledge Proof (ZKP) protocol layer in order to make risk-sharing possible in a secure manner across multiple institutions. This allows the banks to cooperate with each other in order to combat financial crimes while maintaining their data sovereignty. With predictive graph analytics, event-driven integration, and private cooperation, the proposed architecture enables proactive compliance in real-time payment environments, including real-time payment systems such as FedNow, against high-speed financial crime.
We construct the Settlement Modernisation Index, a panel dataset of 809 reform events across 24 advanced economies between 1993 and 2024, decomposed into three economic channels and three adoption phases. We document an S-curve in inside money elasticity with two interior turning points at SMI = 0.27 and 0.93, separating a liberation phase, a post-global-financial-crisis compliance valley, and a mature-infrastructure recovery phase. We show that settlement modernisation generates network-conditional balance sheet efficiencies through a T2S event-study with year-by-year EMIR decomposition (saturation beta = +0.557, p < 0.01) and an out-of-sample synthetic control null on Switzerland's post-2021 SDX deployment. Applied along the BIS three-layer connectivity taxonomy, the framework forecasts +13.4 percent efficiency recovery from the ECB's Pontes initiative over 2027-2032. Conditional UK and US accession to the Appia composability layer (2028) raises the ceiling to +37.5 percent. Balance-sheet efficiencies from atomic settlement are a property of the bilateral pair, not the node.
The swift expansion of Decentralised Finance (DeFi) has garnered increased scrutiny from regulatory bodies due to its potential risks and the absence of a central entity that can be held accountable. While DeFi offers certain benefits for the trading of security tokens, its decentralized structure challenges current regulatory systems that depend on centralized oversight. Global standard-setting bodies have therefore intensified their calls for regulators to address DeFi-related vulnerabilities. This document thoroughly analyses the difficulties associated with DeFi and proposes possible regulatory strategies. These strategies could involve overseeing entities with particular degrees of influence, such as developers and validators, integrating regulation through dedicated supervisory nodes, and/or establishing a reliable regulatory protocol layer. Yet, policymakers might be even more inclined to guide the financial market towards more centralized financial systems (CeFi) in the case of security tokens, which can be achieved by promoting the creation of regulatory sandboxes having a single entity asking for an authorization. This initiative could encourage the development of innovations that comply with regulations while reducing risks.
The rapid expansion of decentralized finance has introduced unprecedented systemic risks, most notably the phenomenon of stablecoin runs. Traditional econometric models analyzing financial fragility rely heavily on retrospective data, which is insufficient for tracking high-velocity, algorithmic bank runs on blockchain networks. This paper proposes a cloud-native architectural solution utilizing distributed Amazon Web Services middleware to ingest, normalize, and analyze blockchain ledger data in real-time. By deploying an asynchronous Python orchestration pipeline integrated with eXtreme Gradient Boosting and K-Nearest Neighbors algorithms, the proposed system identifies transaction velocity anomalies indicative of panic-selling and de-pegging events. This methodology fundamentally shifts the analysis of stablecoin fragility from theoretical post-mortem to programmatic, real-time detection. Preliminary architectural evaluations demonstrate that decoupling the data ingestion layer from the predictive inference engine significantly reduces latency, providing financial regulators and researchers with a scalable, deterministic tool for monitoring digital asset stability.
This paper presents a unified back‑end settlement architecture designed to support multi‑rail, ledger‑agnostic financial transactions across modern digital asset systems. It defines a deterministic settlement model capable of coordinating fiat rails, tokenized assets, distributed ledgers, and messaging networks under a single canonical framework. The architecture introduces a universal settlement core that abstracts rail‑specific behaviors into standardized primitives, enabling consistent execution, reconciliation, and finality across heterogeneous systems. It incorporates a canonical identity layer, semantic tokenization model, and compliance‑aware routing logic to ensure interoperability between traditional financial infrastructure and emerging tokenized environments. Key contributions include: A multi‑rail settlement engine supporting synchronous and asynchronous flows Deterministic finality logic for cross‑rail and cross‑ledger operations A universal bridge framework for rail‑agnostic asset movement Canonical identity mapping for participants, assets, and transaction states Semantic tokenization rules enabling unified representation of digital and traditional instruments Compliance and audit primitives embedded directly into the settlement workflow This work provides a complete architectural foundation for institutions seeking to modernize settlement operations, integrate tokenized assets, and achieve interoperability across fragmented financial rails. It serves as a reference model for next‑generation clearing and settlement systems.
This working paper introduces selected findings from Flow Extraction Theory (FET), an independent research program studying economic-state representation in decentralized financial systems. The paper argues that event history is not equivalent to state, and that observed pressure is not equivalent to explained pressure. Using a bounded Aave V3 case study at Ethereum block 20,000,000, the paper distinguishes historical event evidence, frozen protocol state, token-level representation, account-level aggregate outputs, inference, and unknowns. The study shows that event-derived reconstruction can disagree with exact frozen state, and that health-factor distance can be observed with high confidence while the evidence required to explain that distance remains incomplete. The paper introduces representation risk as the risk created when different evidence classes are collapsed into one operational view of “state.” This public version summarizes selected findings only. It does not disclose implementation details, private tooling, execution logic, complete artifacts, or trading signals.
Purpose This study develops a pricing and contract design framework for cryptocurrency catastrophe (CAT) bonds to transfer extreme crypto-native risks, including protocol exploits, exchange breaches and decentralized finance (DeFi) failures, to capital markets. The paper aims to address arbitrage-free valuation, sponsor-optimal contract design and trustless settlement under the unique informational and operational features of blockchain systems. Design/methodology/approach We propose a multi-trigger crypto CAT bond structure that jointly captures short-term catastrophic shocks and long-term systemic deterioration through oracle-reported loss metrics. An arbitrage-free valuation framework is developed under an incomplete market setting using the minimal martingale measure, while sponsor-optimal contract design is formulated under a dual-measure framework. Empirically, crypto loss dynamics are modeled using generalized extreme value distributions and copula-based dependence structures, whereas financial risk factors are modeled through ARIMA–GARCH and vine copulas. A smart-contract-enabled on-chain settlement architecture is further introduced to automate trigger evaluation and cash-flow execution. Findings Empirical results based on REKT crypto incident data demonstrate strong dependence between monthly extreme and aggregate losses, with heterogeneous dependence structures across blockchain ecosystems. Simulation studies show that trigger and principal repayment designs substantially affect bond price distributions and tail risk exposures. Conservative trigger structures generate more stable bond valuations, whereas aggressive structures exhibit greater downside dispersion. The proposed framework supports economically viable risk transfer while enabling transparent and timely settlement through blockchain-based execution. Originality/value This study develops, to the best of our knowledge, the first integrated framework for crypto native catastrophe bonds that combines arbitrage-free pricing, sponsor optimal contract design and smart contract-based on-chain settlement. Unlike traditional CAT bonds or cyber insurance-linked securities the proposed framework explicitly incorporates oracle-based observability, crypto-specific dependence structures and automated settlement, providing a novel mechanism for transferring systemic digital asset risks to capital markets.
Purpose This paper aims to compare Ghana’s Virtual Asset Service Providers Act, 2025 (Act 1154), with the USA’s anti-money laundering (AML) framework for virtual assets. It asks whether a unified statute can give an emerging economy advantages over a fragmented, path-dependent regime. Design/methodology/approach The study uses functional and institutional comparative legal analysis. It reviews statutes, supervisory notices, sandbox materials and enforcement documents through a six-dimensional matrix mapped to Financial Action Task Force Recommendations 10, 12, 15, 16, 20, 26, 27 and 35. Findings Ghana’s Act offers statutory coherence, and early implementation steps show movement beyond a purely prospective regime. However, enforcement capacity for virtual asset service providers (VASPs) is still developing. The US framework is institutionally fragmented yet operationally mature. Ghana’s licensing model more closely resembles a banking charter than a money services business (MSB) registration, increasing demands on supervisory expertise, verification systems and technical infrastructure. Both frameworks also leave gaps around decentralized finance. Research limitations/implications Implementing regulations remain incomplete and Ghana does not yet have a mature enforcement record specific to VASPs. The analysis, therefore, combines legal design with early operational evidence rather than a full account of law in action. Practical implications Emerging-economy regulators need more than statutory clarity; they need credible supervisory capacity. VASPs in Ghana should expect operational requirements to evolve as implementation matures. Originality/value The paper offers an early comparative analysis of Ghana’s Act and contributes to debates on regulatory leapfrogging, implementation gaps and compliance capacity in the Global South.
Ms. Sanskruti Pawaskar, Mr. Harsh Shinde, Mr. Ruturaj Laad, Vaishali Gatty
Decentralized finance has disrupted the lending process by transferring the intermediary role from institutionally-led balance sheets into a public ledger framework of smart contracts, pooled liquidity, and tokenized governance. The relevance of such a change in the lending paradigm is more of a question of different trust mechanisms, where the solvency of actors can be maintained through the imposition of collateral and automated processing [1][3]. A qualitative comparison is made below through a literature-constrained synthesis of five sources on DeFi architecture, flash loan exploits, lending protocol structure, decentralized governance flaws, and extractable value [1]-[5]. The two protocols of Aave and Compound have been selected for being representative DeFi lending cases, as per the allowed literature that points them out to be the top loanable funds protocols, having liquidity pools and variable rates [1][3]. This comparison is made against CeFi as an institution-driven reference point rather than other DeFi lending protocols owing to the asymmetry of the evidence base. Three conclusions are drawn.Second, the risk architecture of DeFi lending is structurally different from other financial institutions in that flash loans, dependence on oracle feeds, smart contract weakness, composable nature, extractable value, and governance capture are not mere flaws in DeFi but inherent aspects of open and highly coupled financial systems [2][4][5]. Third, governance in DeFi is an additional security mechanism, as the governance of protocol control, parameters and responses to emergencies rests on the robustness of token-based decision-making mechanisms [4].
This PhD thesis examines risks, opportunities and socio-technical innovation in blockchain-based financial systems, combining network analysis, empirical market data, and institutional analysis. As the crypto ecosystem and decentralized financial infrastructures continue to expand and interact with traditional monetary systems, understanding how risk propagates across assets, platforms, and institutional designs has become increasingly important for market participants and policymakers. The first two chapters focus on systemic risk in crypto assets (cryptocurrencies and stablecoins) using a network-based approach. The first paper analyzes major crypto assets and constructs dynamic networks based on return co-movements to study the evolution of interconnectedness and contagion risk over time. Network centrality measures (degree, closeness, betweenness, and eigenvector) are used to identify systemically important nodes (cryptocurrencies and stablecoins) and to assess how these measures affect their systemic risk contributions, particularly during market stress episodes. Results showed that the systemic risk contribution of crypto assets decreases over time as their connectedness in the system increases. This impact is more pronounced for cryptocurrencies than for centrally issued, managed, and governed stablecoins. Our findings suggest that pure network interconnectedness plays a diminished role in tail risk propagation in the crypto market. The second paper extends this framework to token pairs traded on centralized and decentralized exchanges (CEXs and DEXs), allowing for a comparison of market structure and risk transmission across trading platforms. By incorporating data from CEXs and DEXs, this chapter highlights differences in network topology and the role of liquidity concentration in shaping systemic risk. Results showed that centrality values significantly impact systemic risk contribution of token pairs listed on centralized exchanges. Conversely, insignificant results were found for all token pairs traded on decentralized exchanges. The token pairs on centralized exchanges exhibited a negative association with centrality values, consistent with the findings reported in the first paper. These findings imply that systemic risk in cryptocurrency markets is not solely driven by interconnectedness, but by how that interconnectedness is structured. In particular, the negative relationship between centrality and systemic risk suggests that higher network integration, supported by transparency and decentralized architectures, may enhance risk sharing and reduce systemic vulnerability. These results highlight the potential of blockchain based financial systems to contribute to more resilient, efficient, and inclusive financial ecosystems, while also offering new insights for the design of risk management and regulatory frameworks. The third paper shifts the focus from market level risk to protocol level risk management in leading Decentralized Finance (DeFi) lending platforms. It examines the determinants of liquidation events and evaluates the effectiveness of protocol design features as risk management tools. Exploiting the transition from earlier to newer protocol versions across different blockchain layers, the empirical analysis employs panel fixed effect regression models to assess how changes in risk control measures 3 affect liquidation dynamics and protocol’s performance. The findings emphasize that protocol level design choices play a critical role in mitigating risk beyond asset price volatility alone. The architectural evolution from v2 to v3, characterized by granular risk parameters, isolation modes, and enhanced risk management mechanisms has systematically improved protocol resilience, with liquidations in v3 serving as positive signals of stability rather than distress. The fourth paper broadens the scope of the thesis by examining blockchain based complementary currencies in comparison with traditional complementary currency systems, with a particular focus on their potential role in universal basic income schemes. It investigates the socio-technical evolution of Complementary Currencies for Basic Income using a data-driven approach to different case studies (Fiat and Blockchain based models). It highlights how technological choices influence scalability, transparency, and risk exposure in social and monetary innovations by employing mix method approach. Finally, based on the trade-offs of each system, a hybrid model for UBI is proposed for financial inclusion and poverty elimination. Taken together, the four papers provide an integrated perspective on risks and opportunities in emerging financial ecosystems, spanning asset markets, trading infrastructure, decentralized protocols, and alternative monetary arrangements. Overall, the results suggest that the core features of blockchain based markets, e.g., decentralization, transparency, accessibility, low transaction costs and automated risk management, are not merely technological innovations but may serve as mechanisms for improving system resilience and inclusive financial architectures. This thesis also contributes to the literature by demonstrating how network structures and institutional design jointly shape systemic risk and resilience in DeFi, offering insights relevant for researchers, protocol designers, and policymakers navigating the evolving digital financial landscape.
Programmable money—digital currency whose behaviour is controlled by code—creates new design space for dynamic, data-driven monetary policy. This paper proposes a framework for AI-adjusted interest rates in programmable monetary systems, w here machine-learning models continuously calibrate interest-rate parameters in response to real-time economic and network conditions. We formally describe the architecture of such systems, illustrate how AI-driven mechanisms can extend existing algorithmic interest-rate models in decentralized finance (DeFi), and discuss their potential integration with central bank digital currencies (CBDCs). Using stylized simulation data calibrated to typical DeFi lending dynamics, we compare baseline algorithmic rate m odels with an AI-adjusted variant, showing reduced volatility and smoother utilization patterns. A case study on Compound and Aave interest-rate mechanisms demonstrates how AI- based forecasting and reinforcement learning could enhance stability and policy precision. We conclude by outlining governance, regulatory, and ethical considerations, and propose a research agenda for AI-driven algorithmic monetary policy.
This article considers the potential of decentralized finance (DeFi) to disrupt global financial stability, highlighting its evolving vulnerabilities and emerging systemic risks. While DeFi has yet to trigger a financial crisis, its rapid growth, increasing complexity, and expanding interconnections with traditional finance (TradFi) suggest that it could become a channel for financial instability under stress conditions. While DeFi inherits certain vulnerabilities of TradFi, its reliance on decentralized governance, algorithmic execution, and volatile collateral arrangements generates distinct risk dynamics. The article places a critical emphasis on stablecoins, whose structural fragilities and liquidity mismatches may amplify contagion effects in times of market stress. The article also examines the limitations of built-in risk mitigation mechanisms, such as overcollateralization and automated liquidation, which, in the absence of legal safeguards or supervisory oversight, may not be sufficient to prevent market-wide disruptions. To mitigate the threat that DeFi may pose to financial stability, this article identifies two regulatory priorities: enhancing monitoring and supervision of DeFi’s evolution and fostering international cooperation to mitigate transmission risks inherent in the DeFi ecosystem.
2026년 1월 전자증권법·자본시장법 개정안이 국회를 통과하여 이른바 ʻ토큰증권ʼ 제도화의 법적 기반이 마련되었다. 본 논문은 ʻ토큰증권ʼ이라는 단일 정책 브랜드 아래 추진된 이번 입법이 실제로는 법적으로 독립된 두 과제, 즉 증권 인프라 상 분산원장의 도입(전자증권법)과 비정형적 증권의 유통 허용(자본시장법)으로 나뉘어져 있으며, 후자가 전자를 전제하지 않음을 논증하고자 한다. 분산원장 도입에 관하여는 발행인계좌관리기관의 기술적 진입장벽이 존재하는 점, 분산원장만 한정적으로 허용하는 방안의 규범적 근거가 미약하다는 점, EU DLT Pilot Regime 및 일본 전자기록이전권리와의 비교법적 시사점을 검토하였을 때 그 실효성의 한계가 있다는 점을 지적한다. 비정형적 증권 유통에 관하여는 투자계약증권의 공동사업 요건과 보충성 원칙에서 비롯되는 증권성 판단의 불확실성, 가상자산 규제 체계와의 경계의 불분명성, 기초자산 확장의 제도적 전제 사항을 분석한다. 이러한 구별에 기초하여, 발행인계좌관리기관 등록 요건의 실질화, 분산원장의 기능 중심적 기술 요건 설계, 증권성 판단의 예측 가능성 확보, 디지털자산기본법과의 선제적 조화, 기초자산 확장을 위한 제도적 기반 마련 등을 하위법령 정비의 방향성으로 제언한다.