Financial institutions are merging artificial intelligence with stablecoins, a digital currency pegged to stable assets like the US dollar, to modernize payments and reduce transaction costs globally. This convergence is reshaping how money moves across borders and between machines, with major players like Visa and Mastercard already deploying live systems that let AI agents execute payments autonomously.

Why Are Banks Suddenly Combining AI and Stablecoins?

The overlap between AI and stablecoins creates natural synergies that amplify the benefits of each technology. According to a Forrester Consulting study commissioned by AWS Marketplace and involving 521 global technology and business strategy decision-makers, organizations are increasingly deriving measurable value from combining these two capabilities.

The study found that 70% of professionals at financial institutions identified stablecoins as a key focus for their organizations. The primary drivers are clear: 71% of respondents cited facilitating cross-border transactions, and 67% identified the need for alternative money transfer and payment mechanisms. Notably, 65% of decision-makers believe stablecoin offerings will soon become table stakes, or essential baseline services, for financial providers.

Visa demonstrated this convergence in June 2026 when it announced new AI, stablecoin, and token capabilities. The company partnered with OpenAI to enable Visa payments within agentic commerce, allowing seamless transactions initiated by AI agents. Visa also developed a "Large Transaction Model," an AI system trained on billions of transactions to improve fraud detection while reducing false declines that frustrate legitimate customers.

What Real-World Payments Are Already Happening?

The technology is moving beyond announcements into live operations. In March 2026, Santander and Mastercard completed Europe's first end-to-end payment executed by an AI agent in a controlled environment using Mastercard Agent Pay, a framework introduced in 2025 that allows AI agents to initiate and execute payments on behalf of customers within predefined limits and permissions.

Mastercard expanded this capability in July 2026 with Agent Pay for Machines, a specialized extension built for high-frequency, automated machine-to-machine micropayments. This system supports stablecoins alongside traditional card networks and bank accounts, enabling a multi-rail settlement approach that gives institutions flexibility in how they process transactions.

Cross-border transactions have emerged as the flagship use case for stablecoins. According to the Forrester study, 67% of respondents are already using stablecoins for cross-border business and peer-to-peer payments, making these the most prominent applications. Additionally, 56% of respondents use stablecoins for treasury and cash management, where businesses optimize their liquid reserves.

How Are Financial Institutions Scaling AI Alongside Stablecoins?

AI adoption in financial services is accelerating rapidly. The Forrester study revealed that 52% of respondents are either scaling generative AI (genAI) or have operationalized it across their enterprise, making genAI the most widely adopted technology in both the AI and cryptocurrency sectors. Additionally, 35% of respondents are either scaling agentic AI, which refers to AI systems that can take autonomous actions, or have operationalized it across their enterprise.

Across Europe, the Middle East, and Africa (EMEA), adoption is particularly pronounced. A 2025 Deloitte survey of 87 banks and 49 insurers found that two-thirds of these institutions utilized AI or machine learning (ML) techniques in their operations. The growth has been dramatic: in 2025, 94% of large banks and 62% of small banks used genAI, compared to much lower adoption rates just two years prior. For small banks, AI adoption jumped from 22% in 2023 to 52% in 2025, indicating that even resource-constrained institutions are embracing the technology.

What Are Banks Actually Using AI For?

The primary applications of AI in banking focus on two critical areas: fraud prevention and customer experience. In the Deloitte survey, 58% of banks and 30% of insurers used AI for fraud detection, including anti-money laundering (AML) and know-your-customer (KYC) processes. Simultaneously, 53% of banks and 37% of insurers deployed AI for customer experience improvements.

Real-world results demonstrate the impact. Yiren Digital, a fintech company specializing in AI innovation in China, announced that its AI-powered fraud detection systems intercepted 10,300 fraudulent borrowers across 14,500 cases in 2025, helping avoid RMB165 million (approximately US$23 million) in fraud-related losses. The company's Hawkeye fraud detection system and DiTing intelligent decision-making platform combine to identify suspicious activity and improve underwriting quality.

"Risk management is one of the clearest examples of how AI can create measurable value across highly regulated financial services," said Ning Tang, Chairman and Chief Executive Officer of Yiren Digital. "Our third-generation AI fraud detection technology represents a significant advancement in financial risk management, enabling more adaptive and precise detection while continuously responding to emerging fraud patterns."

Ning Tang, Chairman and Chief Executive Officer of Yiren Digital

Yiren Digital's fraud detection operates at scale. As of the end of 2025, the company's proprietary blacklist database contained approximately 800 million records. The DiTing platform supports daily screening of approximately 30,000 potentially risky credentials, while related document and identity-verification tools identify approximately 1,500 counterfeit documents and more than 1,000 video-fraud cases each day.

What Challenges Are Slowing Adoption?

Despite the opportunities, significant hurdles remain. More than half of the Deloitte survey participants named transparency and explainability as major obstacles to utilizing AI applications. This reflects the increasing use of vendor solutions rather than in-house built AI tools and the growing complexity of AI methodology. Additionally, 39% of respondents identified internal skills and capabilities as a challenge to AI implementation, suggesting that many institutions lack the expertise to deploy and manage these systems effectively.

Legacy systems present another barrier. 24% of respondents identified the rigidity of existing processes as a major obstacle to adopting AI. Beyond technical challenges, institutions face broader concerns: 46% of respondents cited general risks posed by AI, 34% named fairness concerns, 34% cited the regulatory landscape, and 32% identified safety and security as hurdles.

Stablecoins themselves carry distinct risks. Contagion risk from a rapid "run" on a major stablecoin could spill over into the traditional banking system. Additionally, stablecoins represent a form of credit disintermediation, potentially siphoning deposits away from traditional lenders. If a stablecoin gains significant traction, its scale could substantially impact bank funding models. Furthermore, widespread use of dollar-pegged stablecoins can hinder central banks' ability to effectively transmit monetary policy, as interest rate changes may have less predictable effects on credit conditions if significant economic activity shifts outside conventional banking channels.

How Should Regulators Approach AI in Finance?

Policymakers are grappling with how to oversee AI in financial services. The American Fintech Council (AFC), representing over 150 member companies and partners, submitted a comment letter to the House Committee on Financial Services in August 2026 urging a unified, risk-based approach to AI regulation.

"AI is a powerful tool that can expand access to financial services, strengthen fraud prevention, and improve products for consumers," said Phil Goldfeder, CEO of the American Fintech Council. "Congress has a crucial opportunity to establish a unified regulatory framework for AI use that builds on existing consumer protections while giving financial institutions the clarity they need to deploy AI responsibly."

Phil Goldfeder, CEO of the American Fintech Council

The AFC recommends that AI regulation be risk-based and context-specific, rather than creating new requirements based solely on the technology being used. The council suggests applying existing regulatory frameworks when AI enhances existing financial products and services, while encouraging distinct oversight only when AI creates wholly new products or services. The AFC also emphasizes the need for a unified federal approach to avoid a patchwork of state requirements that could create compliance challenges and barriers to entry for smaller institutions.

Steps Financial Institutions Should Take Now

• Assess AI Readiness: Evaluate your organization's current AI capabilities, internal expertise, and legacy system constraints to identify gaps before scaling AI deployments across fraud detection, customer service, and payment processing.
• Prioritize Explainability and Governance: Implement human oversight mechanisms and model monitoring frameworks to ensure AI decisions are transparent, reviewable, and compliant with emerging regulatory requirements around fairness and safety.
• Explore Stablecoin Use Cases: Pilot stablecoin applications for cross-border transactions and treasury management to understand operational benefits, regulatory implications, and integration requirements before full-scale deployment.
• Engage with Regulators: Participate in regulatory sandboxes and AI Innovation Labs that allow institutions to test emerging technologies in partnership with regulators, ensuring compliance while maintaining competitive advantage.

The convergence of AI and stablecoins is reshaping financial infrastructure at a pace that demands immediate attention from institutions of all sizes. While the opportunities for cost reduction, fraud prevention, and expanded access are substantial, the risks around explainability, regulatory uncertainty, and systemic stability require thoughtful implementation and ongoing collaboration between industry and policymakers.