Bank of Mexico warns fragmented global rules expose stablecoins to stress, arbitrage

cointelegraphPublicado a 2025-12-11Actualizado a 2025-12-11

Resumen

The Bank of Mexico (Banxico) has warned that stablecoins present significant risks to financial stability, citing rapid growth, exposure to traditional finance, and fragmented global regulations that could lead to arbitrage and market stress. The central bank highlighted vulnerabilities such as heavy reliance on short-term U.S. Treasuries, market concentration among a few issuers, and past depegging events. Without coordinated international safeguards, mass redemptions or issuer failures could spill over into broader financial markets. Banxico also noted that divergent regulatory approaches—such as the EU’s MiCA and the U.S. GENIUS Act—create gaps that may encourage regulatory arbitrage. While acknowledging potential benefits like improved settlement efficiency and lower remittance costs, Mexico maintains a cautious stance toward crypto integration with traditional finance. In contrast, Brazil and Argentina lead Latin America in crypto adoption, with Brazil implementing banking-style supervision for crypto firms and Argentina reconsidering its ban on bank crypto trading.

Mexico’s central bank warned in a new financial stability report that “stablecoins pose significant potential risks to financial stability,” citing their rapid growth, links to traditional finance and global regulatory gaps that could fuel arbitrage and magnify market stress.

Stablecoins’ heavy reliance on short-term US Treasurys, market concentration with two issuers controlling 86% of the supply and past depegging episodes with stablecoins underscore how vulnerable the sector remains to stress, according to the Banxico report.

Without coordinated international safeguards, mass redemptions or issuer failures could spill into broader funding markets, the central bank warned.

Banxico also highlighted diverging regulatory approaches as a growing source of risk, noting that frameworks like the EU’s MiCA and the US GENIUS Act impose different reserve, redemption and depositor-protection requirements, creating regulatory gaps that could incentivize arbitrage across jurisdictions.

*Stablecoin market cap. Source:* *DefiLlama*

Banxico acknowledged that stablecoins can improve settlement efficiency, reduce transfer costs and support remittances and liquidity in decentralized finance. However, it plans to keep a cautious distance between the traditional financial system and virtual assets, citing their potential to cause stress in broader markets.

Crypto adoption in Mexico is relatively low. According to Chainalysis’ Global Crypto Adoption Index, the country fell to 23rd place in 2025 from 14th place in 2024 in the adoption ranking.

The central bank’s warning reflects Mexico’s broader cautious stance on crypto. Despite the rise of exchanges like Bitso, the country has not introduced significant new digital-asset legislation and still relies on its 2018 Fintech Law as the primary regulatory framework.

Related: As inflation bites, Latin America banks on stablecoins instead of bankers

Brazil and Argentina lead Latin America in crypto adoption

While Mexico’s central bank maintains a cautious stance on digital assets, other Latin American countries have embraced adoption.

Chainalysis’ 2025 Geography of Crypto Report shows that Latin America generated nearly $1.5 trillion in crypto transaction volume from July 2022 to June 2025, with monthly activity increasing to almost $88 billion by December 2024 from $20.8 billion in mid-2022. Several months in late 2024 and early 2025 consistently exceeded $60 billion.

According to the report, Brazil led Latin America by a wide margin, receiving $318.8 billion in crypto value from July 2022 to June 2025, nearly one-third of all activity in the region, while Argentina ranked second with $93.9 billion in transaction volume.

*Source:* *Chainalysis 2025 Geography of Crypto Report*

The central banks of the two leading countries are also taking a more proactive stance in regulating digital assets.

In November, Brazil’s central bank finalized rules that place crypto companies under banking-style supervision, including treating stablecoin transactions and certain self-custody wallet transfers as foreign exchange operations.

In Argentina, a country that has suffered from soaring inflation, the central bank is reportedly weighing whether to allow traditional financial institutions to trade cryptocurrencies in a potential reversal of its 2022 ban, according to a report from La Nación on Friday.

Magazine: Meet the onchain crypto detectives fighting crime better than the cops

Criptos en tendencia

CitreaCTR

wrapped stUSDTWSTUSDT

Lecturas Relacionadas

Two Funding Rounds in Three Months: The Chinese Version of Palantir is on Fire

Investment Community AI has learned that Beijing Zhongshu Ruizhi Technology Co., Ltd., a domestic industrial-grade causal intelligence and high-reliability decision-making AI company, has recently completed a strategic financing round worth hundreds of millions of RMB. This round saw participation from China Internet Investment Fund, Suzhou Chuangtou National Social Security Fund, Financial Street Capital, ICBC Capital, Kunlun Capital, among others, with existing shareholders also increasing their investment. This follows a Series B funding round in the hundreds of millions completed just three months prior. The rapid succession of two major funding rounds signifies strong market recognition of the company's underlying original technology and scaled commercial implementation. Often referred to as the "Chinese version of Palantir," Zhongshu Ruizhi is entering a new phase of accelerated technological iteration, widespread scenario replication, and scaled performance release, mirroring the explosive growth of China's AI market. Founded in April 2020 by Dr. Han Han, a Tsinghua University Ph.D. and former core drafter of national AI policies, the company is mission-driven to "move AI from the digital world to the physical world." It focuses on the high-reliability, strong-decision industrial AI track and enterprise-grade AI Agent full-stack infrastructure. The team tackles the challenge of applying AI to China's vast and complex industrial and energy systems by developing a new intelligent operating system from scratch. Its core technological breakthrough lies in three proprietary底层 technologies: meta-causal cognitive theory, causal models, and a dynamic ontology engine. These address critical pain points of generative large models in industrial settings—such as AI hallucinations, insufficient reasoning, lack of temporal logic, unverifiable decisions, and multi-source rule conflicts—thereby providing trustworthy, explainable, and executable智能决策 capabilities. Commercially, Zhongshu Ruizhi has achieved scaled deployment, serving over 50 central state-owned enterprises and industrial groups in sectors like power, petroleum, and aerospace, with implementations in more than 800 highly complex production scenarios. The company reported doubled revenue in 2025, demonstrating strong self-sufficiency and a viable business model—a rarity among new-generation AI firms. The latest funds will be allocated towards advancing foundational theoretical research, replicating successful application models to expand market presence (including overseas), and attracting top-tier talent. Lead investor China Internet Investment Fund highlighted that in the current shift from general AI capability contests to deep industrial empowerment, industrial-grade causal intelligence is crucial for building China's modern digital foundation and fostering new quality productive forces. They expressed support for the company's efforts to define decision-making paradigms and trustworthy standards for industrial intelligence, aiming to secure a rule-making voice in the global physical AI arena.

marsbitHace 9 min(s)

Two Funding Rounds in Three Months: The Chinese Version of Palantir is on Fire

marsbitHace 9 min(s)

The Biggest Political Economy Question in the AI Era: As Robots Become More Capable, How Do Humans Share the Value?

In the AI era, the most pressing political economy question is: as machines become increasingly capable, how can humanity share in the value they create? An article originally critiquing China's tech focus has sparked a deeper debate on this global challenge. Historically, industrial progress improved efficiency but still relied on human labor for wealth creation and distribution. AI is fundamentally different—it is now replacing cognitive and knowledge work. As AI and robots take over more tasks, economic growth may continue while direct human participation in value creation shrinks, creating a core tension between productivity gains and widespread income generation. The issue is not unique to China. While leading tech companies amass enormous wealth, labor's share of income is declining globally. The core problem is a broken link: technological innovation and corporate profits are not translating into sufficient consumer income and demand. Three potential paths forward are outlined: a traditional capitalist model where profits primarily go to capital owners; a state-capitalist approach with public investment in AI; and more innovative models like digital sovereign wealth funds, universal shareholding, or AI-era basic income schemes to directly distribute AI-generated value. The future competitive advantage may lie not just in technological supremacy, but in which society can build a new, inclusive distribution system for the intelligent economy. The ultimate challenge is ensuring that as AI creates value, humans have a means to obtain income and share in the resulting widespread social benefits.

marsbitHace 19 min(s)

The Biggest Political Economy Question in the AI Era: As Robots Become More Capable, How Do Humans Share the Value?

marsbitHace 19 min(s)

Generating Profits for Seven Consecutive Quarters, Emerging Markets Carry Trade Outperforms Everything

For the seventh consecutive quarter, dollar-funded emerging market carry trades have delivered positive returns, marking the longest winning streak since 2008. According to Bloomberg's index, this strategy has gained approximately 22% since late 2024, outperforming U.S. Treasuries, emerging market sovereign, and corporate dollar debt. The core of the trade involves borrowing low-interest currencies like the U.S. dollar, euro, or yen to invest in high-yielding emerging market assets, such as Turkish lira bonds offering over 40% returns. Returns were amplified by favorable currency moves, with the dollar weakening against most emerging market currencies and other traditional funding currencies. For instance, the trade gained 48% on the Colombian peso in the past year. A key test came in August 2024 with a historic joint U.S.-Japan currency intervention, which caused only a modest 1% dip in the carry trade risk premium as investors shifted funding from the yen to the euro and Swiss franc. Looking ahead, the primary risk is the timing of Federal Reserve policy changes. While persistent inflation allows the Fed to hold rates, a rapid rise in long-term U.S. yields could threaten the trade. Another concern is crowding, as massive inflows increase vulnerability to a sudden reversal. High interest rates in regions like Latin America and Eastern Europe, supported by external factors like Middle East tensions and energy prices, continue to sustain the opportunity. Major investors remain engaged, favoring currencies like the Mexican peso, South African rand, and Turkish lira.

marsbitHace 34 min(s)

Generating Profits for Seven Consecutive Quarters, Emerging Markets Carry Trade Outperforms Everything

marsbitHace 34 min(s)

Tsinghua and Whiton Mentor-Disciple Team Cracks 40-Year-Old Mystery, Core Math All Written by GPT, Anyone Could Do It

Tsinghua and Wharton researchers have used GPT-5.6 Sol Pro to solve a 40-year-old open problem in optimization theory, proving a fundamental limit for gradient descent. They demonstrated that by adjusting only the step size sequence—without adding momentum or changing the algorithm's structure—the convergence rate of gradient descent cannot exceed Ω(T^{-1.9319}). This establishes a mathematical ceiling, showing that structural modifications are necessary to achieve the fastest possible convergence rates like O(1/T²). The core proof, involving a geometric construction based on "resisting oracles," was entirely generated through iterative interaction with the AI. The proof was then formally verified using the Lean 4 theorem prover, achieving zero "sorry" or "admit" statements, confirming its logical correctness without human intervention in the non-trivial mathematical steps. This result bridges the gap between the known O(T^{-1.2716}) rate achieved by the "silver stepsize" sequence and the newly proven impossibility bound. The work suggests a new paradigm where researchers can leverage general-purpose AI to explore and formally verify complex theoretical proofs.

marsbitHace 44 min(s)

Tsinghua and Whiton Mentor-Disciple Team Cracks 40-Year-Old Mystery, Core Math All Written by GPT, Anyone Could Do It

marsbitHace 44 min(s)

Anthropic Admits Fault, Evidence of Secretly Downgrading Claude's Intelligence

Anthropic publicly apologized after users discovered a hidden A/B test in Claude Code (versions 2.1.237+) that seemingly "dumbed down" the model. Developer argofowl found that selecting the "high" reasoning effort was being mapped internally to a value of "10," which previously corresponded to the "low" setting. While Anthropic engineers clarified this was only a change to the numerical mapping scale and assured it did not affect performance based on internal evaluations, the lack of communication about the test sparked widespread user backlash and self-tests on social media. Separately, significant complaints emerged about the flagship Opus 5 model, with users reporting it became "unstable," prone to lazy outputs, factual errors, and repetitive corrections. An Anthropic engineer acknowledged Opus 5's performance instability, calling its resolution a top priority. The incident highlights a growing industry-wide rift between improving benchmark scores and declining user-perceived quality and reliability. It underscores the lack of transparency in continuous model updates, where server-side changes, A/B tests, and configuration adjustments occur without clear user notification, eroding trust as AI models become critical infrastructure.

marsbitHace 44 min(s)

Anthropic Admits Fault, Evidence of Secretly Downgrading Claude's Intelligence

marsbitHace 44 min(s)

Trading

Spot

Artículos destacados

Qué es $BANK

Banco AI: Un Paso Revolucionario en el Futuro de la Banca Introducción En una era marcada por avances rápidos en tecnología, Banco AI se sitúa en la intersección de la inteligencia artificial (IA) y los servicios bancarios. Este proyecto innovador busca redefinir el panorama financiero, mejorando la eficiencia operativa, las medidas de seguridad y las experiencias del cliente a través del poder de la IA. Al embarcarnos en esta exploración de Banco AI, profundizaremos en lo que implica el proyecto, sus dinámicas operativas, su contexto histórico y hitos significativos. ¿Qué es Banco AI? En su esencia, Banco AI representa una iniciativa transformadora destinada a integrar la inteligencia artificial en varias operaciones bancarias. Este proyecto aprovecha las capacidades de la IA para automatizar procesos, mejorar los protocolos de gestión de riesgos y mejorar la interacción con los clientes a través de servicios personalizados. Los objetivos principales de Banco AI incluyen: Automatización de Funciones Bancarias: Al aprovechar las tecnologías de IA, Banco AI tiene como objetivo automatizar tareas rutinarias, reduciendo la carga sobre los recursos humanos y mejorando la eficiencia. Mejora en la Gestión de Riesgos: El proyecto utiliza algoritmos de IA para predecir e identificar riesgos, fortaleciendo así las medidas de seguridad contra fraudes y otras amenazas. Personalización de Servicios Bancarios: Banco AI se centra en ofrecer productos y servicios financieros a medida al analizar datos y comportamientos de los clientes. Mejoramiento de la Experiencia del Cliente: La implementación de soluciones impulsadas por IA, como chatbots y asistentes virtuales, tiene como objetivo proporcionar a los usuarios interacciones más humanas, revolucionando la forma en que los clientes se relacionan con los bancos. Con estos objetivos, Banco AI se posiciona como un jugador crucial para hacer que la banca sea más eficiente, segura y centrada en el usuario. ¿Quién es el Creador de Banco AI? Los detalles sobre el creador de Banco AI siguen siendo desconocidos. Como tal, no se ha identificado a ninguna persona u organización específica en la información disponible. El anonimato que rodea el inicio del proyecto plantea preguntas, pero no resta valor a su ambiciosa visión y objetivos. ¿Quiénes Son los Inversores de Banco AI? Al igual que con el creador del proyecto, no se ha divulgado información específica sobre los inversores u organizaciones que apoyan a Banco AI. Sin esta información, es un desafío delinear el respaldo financiero y el apoyo institucional que podrían estar impulsando el proyecto hacia adelante. No obstante, la importancia de contar con una sólida base de inversión es fundamental para sostener el desarrollo en un campo tan innovador. ¿Cómo Funciona Banco AI? Banco AI opera en múltiples frentes innovadores, centrándose en factores únicos que lo diferencian de los marcos bancarios tradicionales. A continuación, se presentan las características operativas clave: Automatización: Al aplicar algoritmos de aprendizaje automático, Banco AI automatiza varios procesos manuales dentro de los bancos. Esto resulta en la reducción de costos operativos y permite que los trabajadores humanos redirijan sus esfuerzos hacia actividades más estratégicas. Gestión Avanzada de Riesgos: La integración de la IA en las prácticas de gestión de riesgos equipa a los bancos con herramientas para predecir con precisión amenazas potenciales como el fraude, garantizando que la información y los activos de los clientes permanezcan seguros. Recomendaciones Financieras Personalizadas: A través del aprendizaje continuo a partir de las interacciones con los clientes, los sistemas de IA desarrollan una comprensión matizada de las necesidades del usuario, lo que les permite ofrecer consejos adaptados sobre decisiones financieras. Interacciones Mejoradas con los Clientes: Al utilizar chatbots y asistentes virtuales impulsados por IA, Banco AI permite una experiencia más atractiva para el cliente, permitiendo a los usuarios resolver sus consultas rápidamente, reduciendo así los tiempos de espera y mejorando los niveles de satisfacción. En conjunto, estas características operativas posicionan a Banco AI como un pionero en el sector bancario, estableciendo nuevos parámetros para la entrega de servicios y la excelencia operativa. Línea de Tiempo de Banco AI Entender la trayectoria de Banco AI requiere mirar su contexto histórico. A continuación, se presenta una línea de tiempo que destaca hitos y desarrollos importantes: Inicios de 2010: La conceptualización de la integración de la IA en los servicios bancarios comenzó a ganar atención a medida que las instituciones bancarias reconocieron los posibles beneficios. 2018: Se produjo un aumento notable en la implementación de tecnologías de IA cuando los bancos comenzaron a utilizar herramientas de IA como chatbots para el servicio al cliente básico y sistemas de gestión de riesgos para mejorar la seguridad. 2023: La sofisticación de la IA continuó avanzando, con la introducción de IA generativa para tareas más complejas como el procesamiento de documentos y análisis de inversiones en tiempo real. Este año marcó un salto significativo en las capacidades que la tecnología de IA otorgó a los bancos. 2024-Estatus Actual: A partir de este año, Banco AI se encuentra en una trayectoria ascendente, con investigaciones y desarrollos en curso que pronto mejorarán las capacidades en las operaciones bancarias. La continua exploración de las aplicaciones de IA sugiere emocionantes desarrollos aún por venir. Puntos Clave Sobre Banco AI Integración de la IA en la Banca: Banco AI se centra en adoptar inteligencia artificial para optimizar los procesos bancarios y mejorar la experiencia del usuario. Enfoque en Automatización y Gestión de Riesgos: El proyecto enfatiza fuertemente estas áreas, con el objetivo de desplazar la carga de tareas rutinarias mientras mejora los marcos de seguridad a través de análisis predictivos. Soluciones Bancarias Personalizadas: Al aprovechar los datos de los clientes, Banco AI permite servicios bancarios adaptados a las necesidades individuales de los usuarios. Compromiso con el Desarrollo: Banco AI se mantiene comprometido con esfuerzos de investigación y desarrollo continuos, asegurando su adaptabilidad y relevancia continua a medida que la tecnología sigue evolucionando. Conclusión En resumen, Banco AI ejemplifica un paso crucial hacia adelante en la industria bancaria, aprovechando la inteligencia artificial para redefinir los paradigmas operativos, mejorar la seguridad y promover la satisfacción del cliente. A pesar de las lagunas en la información sobre el creador y los inversores, los objetivos claros y los mecanismos funcionales de Banco AI proporcionan una sólida base para su evolución continua. A medida que la tecnología de IA sigue avanzando y fusionándose con el sector bancario, Banco AI está bien posicionado para impactar significativamente el futuro de los servicios financieros, mejorando la forma en que entendemos e interactuamos con la banca.

315 Vistas totalesPublicado en 2024.04.06Actualizado en 2024.12.03

Cómo comprar BANK

¡Bienvenido a HTX.com! Hemos hecho que comprar Lorenzo Protocol (BANK) sea simple y conveniente. Sigue nuestra guía paso a paso para iniciar tu viaje de criptos.Paso 1: crea tu cuenta HTXUtiliza tu correo electrónico o número de teléfono para registrarte y obtener una cuenta gratuita en HTX. Experimenta un proceso de registro sin complicaciones y desbloquea todas las funciones.Obtener mi cuentaPaso 2: ve a Comprar cripto y elige tu método de pagoTarjeta de crédito/débito: usa tu Visa o Mastercard para comprar Lorenzo Protocol (BANK) al instante.Saldo: utiliza fondos del saldo de tu cuenta HTX para tradear sin problemas.Terceros: hemos agregado métodos de pago populares como Google Pay y Apple Pay para mejorar la comodidad.P2P: tradear directamente con otros usuarios en HTX.Over-the-Counter (OTC): ofrecemos servicios personalizados y tipos de cambio competitivos para los traders.Paso 3: guarda tu Lorenzo Protocol (BANK)Después de comprar tu Lorenzo Protocol (BANK), guárdalo en tu cuenta HTX. Alternativamente, puedes enviarlo a otro lugar mediante transferencia blockchain o utilizarlo para tradear otras criptomonedas.Paso 4: tradear Lorenzo Protocol (BANK)Tradear fácilmente con Lorenzo Protocol (BANK) en HTX's mercado spot. Simplemente accede a tu cuenta, selecciona tu par de trading, ejecuta tus trades y monitorea en tiempo real. Ofrecemos una experiencia fácil de usar tanto para principiantes como para traders experimentados.

1.0k Vistas totalesPublicado en 2025.05.09Actualizado en 2026.06.02

Discusiones

Bienvenido a la comunidad de HTX. Aquí puedes mantenerte informado sobre los últimos desarrollos de la plataforma y acceder a análisis profesionales del mercado. A continuación se presentan las opiniones de los usuarios sobre el precio de BANK (BANK).