AI-Stablecoin Convergence Set to Transform Payments and Banking

Financial institutions are embracing stablecoins as a utility to modernize payments, enhance liquidity, and reduce transaction costs across their global operations. Simultaneously, artificial intelligence (AI) initiatives are maturing as financial providers scale generative AI (genAI) efforts and introduce new agentic capabilities.

According to a recent study by Forrester Consulting, these two capabilities have the potential to maximize their natural overlaps and significantly boost payments and banking when combined together.

Forrester Consulting, commissioned by AWS Marketplace, conducted customer interviews and an online survey with 521 global technology and business strategy decision-makers at the manager level and above to assess the current state of financial services organizations and their technology initiatives, including stablecoins and AI.

The study revealed that organizations are increasingly deriving value from their investments in AI, with some capitalizing on the natural synergies of AI and stablecoins to enhance payments and banking.

Visa offers an example of this convergence. In June 2026, the payment firm announced new AI, stablecoin and token capabilities, describing how the two foundational shifts that are AI and stablecoins are poised to transform both the front end and back end of money movement.

Visa’s partnership with OpenAI aims to enable Visa payments within agentic commerce, enabling seamless and trusted payments across OpenAI. Its “Large Transaction Model” is an AI model trained on billions of transactions to improve fraud detection while increasing authorization performance and reducing false declines. Furthermore, Visa’s Agent Score allows merchants to evaluate their websites for agentic commerce readiness.

Visa has also made significant advancements to its tokens, focusing on bringing more data, context and assurance into the credentials used in digital commerce. These enhancements embed identity, permissions and behavioral signals more deeply into credentials, allowing trust to travel with the transaction across devices, channels and use cases, including those initiated autonomously by AI agents.

Another notable example is Santander and its partnership with Mastercard which saw the two firms complete in March 2026 Europe’s first live end-to-end payment executed by an AI agent. Santander carried out the transaction in a controlled environment using Mastercard Agent Pay.

Mastercard Agent Pay is a framework introduced in 2025 that allows AI agents to initiate and execute payments on behalf of customers within predefined limits and permissions. Its specialized extension, Agent Pay for Machines, launched in July 2026, is built for high-frequency, automated machine-to-machine micropayments, and supports stablecoins as part of its multi-rail settlement capabilities, alongside traditional card networks and bank accounts.

Stablecoins and their applications

The convergence of AI and stablecoins is gaining momentum as financial institutions accelerate their development and implementation of both technologies.

The Forrester study revealed that stablecoins are a focus for a growing set of providers. Notably, 70% of professionals at financial institutions identified stablecoins as a key focus for their organizations.

A key goal for these respondents is facilitating cross border transactions, cited by 71% respondents, and adopting alternative money transfer and payment mechanisms, cited by 67%. Furthermore, 65% of decision-makers added that stablecoin offerings will soon become table stakes for financial providers.

Cross-border transactions have emerged as a clear flagship use case for stablecoins. 67% of respondents are using stablecoin for cross-border business and peer-to-peer payments, making these the most prominent applications of stablecoins.

56% of respondents also use stablecoins for treasury and cash management.

The state of AI adoption

Though stablecoins are gaining momentum, AI remains a top priority for financial institutions. 52% of respondents are now either scaling generative AI (genAI) or have operationalized genAI across their enterprise. GenAI stands out as the most widely adopted technology across both the AI and cryptocurrency sectors. Similarly, 35% of respondents are either scaling agentic AI or have operationalized agentic AI across their enterprise.

Across Europe, the Middle East, and Africa (EMEA), a growing number of banks and insurers are incorporating AI models into their operations, with the most prevalent applications being in fraud detection and customer services.

A 2025 survey conducted by Deloitte involving 87 banks and 49 insurers across EMEA revealed that two-thirds of these institutions utilized models that incorporate AI or machine learning (ML) techniques. The proliferation in AI use over the past two years was largely driven by increased adoption of genAI. Notably, in 2025, 94% of large banks and 62% of small banks used genAI in 2025.

The study also found a significant increase in AI adoption among smaller institutions. Notably, the use of AI among small banks increased from 22% in 2023 to 52% in 2025. For small insurers, use increased from 27% to 46% during the same timeframe.

In both banks and insurers, the primary applications of AI techniques in models are for fraud detection, such as anti-money laundering (AML) and know-your-customer (KYC) where 58% of banks and 30% of insurers used AI in 2025, and for customer experience (CX), where 53% of banks and 37% of insurers used AI.

Challenges and risks of stablecoins and AI

Despite the opportunities promised by stablecoins and AI, these new technologies also come with significant risks.

More than half of the Deloitte survey participants named transparency and explainability as major hurdles to utilizing AI applications, a reflection of the increasing use of vendor solutions instead of in-house built AI tools and the increasing complexity of AI methodology. This increasing complexity is also reflected by 39% of respondents naming internal skills and capabilities as a challenge to AI implementation.

Legacy systems and existing processes present a significant challenge, with 24% of respondents identifying the rigidity of processes as a major obstacle to adopting AI.

Further hurdles cited are fairness concerns, named by 34% of respondents, safety and security, cited by 32%, the regulatory landscape, cited by 34%, and generally risks posed by AI, cited by 46%.

Stablecoins also carry several risks. First, there is a contagion risk from a rapid “run” on a major stablecoin. This could potentially spill over into the traditional banking system.

Second, stablecoins represent a form of credit disintermediation, which could potentially siphon deposits away from traditional lenders. If a stablecoin gains significant traction, its scale could have a substantial impact on bank funding models.

Also, the widespread use of dollar-pegged stablecoins can hinder central banks’ ability to effectively transmit monetary policy. If significant economic activity shifts outside the conventional banking channel, interest rate changes may have less predictable effects on credit conditions.

Featured image: Edited by Fintech News Switzerland, based on image by thanyakij-12 via Magnific

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