Stablecoins are among the most transformative innovations of the era of blockchain, now representing more than 60% of on-chain transfer of value. Their use cases are rapidly expanding into real-world financial applications such as cross-border payments, store-of-value functions, and global settlement. With the GENIUS Act now signed into law in the United States, stablecoins are gaining formal recognition as a mainstream financial rail. The new legislation introduces the first federal framework for stablecoins, setting clear standards for reserves, audits, and transparency to strengthen trust and market stability. This move aligns with global regulatory momentum, including Europe's MiCA (Markets in Crypto-Assets Regulation) framework, which went into effect last year establishing similar oversight.

With mainstream adoption comes greater exposure to operational and compliance risk. Legacy risk systems, designed for slower and permissioned financial environments, are struggling to keep up with a real-time, AI-enabled, and borderless digital economy.

This tension was the focus of Oscilar’s recent webinar, Real-Time Detection and Response: Leveraging AI and Automation to Manage Stablecoin Risk — now available on demand. The discussion brought together experts across fintech, digital assets, and risk intelligence:

• Rodger Desai, CEO of Prove

• Vishal K. Gupta, CEO of True Markets (former Head of Exchange at Coinbase)

• Saurabh Bajaj, Chief Product Officer at Oscilar

• Moderated by Jason Mikula, Publisher of Fintech Business Weekly

The panel unpacked how stablecoins are shaking up global payments and why today's risk models can't keep up. Here are five reasons why adopting AI-native infrastructure is essential for managing stablecoin risk.

1. Stablecoins are entering their "real economy" moment

First introduced to capture the benefits of blockchain without volatility, stablecoins have helped bridge the gap between TradFi and cryptocurrency and are quickly becoming the backbone of a new global payment and settlement layer: one that is programmable, instant, and accessible across borders. While stablecoins initially powered liquidity in crypto markets, they are now enabling high-value use cases in economies around the world.

Some examples include:

• Félix, enabling remittances in Latin America via WhatsApp with USDC

• Remote, allowing U.S. employers to pay contractors with stablecoins in more than 60 countries

• Merchant payments in high-inflation economies such as Nigeria, Türkiye, and Argentina

• Programmable disbursements linked to escrow or milestone-based payouts

The panel also pointed to growing interest in using stablecoins to streamline B2B cross-border payments, particularly where traditional infrastructure is costly or unreliable.

2. Borderless infrastructure introduces new risk dynamics

The same characteristics that make stablecoins so powerful: Permissionless access, 24/7 uptime, and global reach also introduce new security and compliance challenges. Criminals are often the earliest adopters of new technologies and blockchain is no exception. The same tools that empower users also give bad actors the ability to operate seamlessly across borders.

Specific enablers include:

• Encrypted messaging apps enabling instant and discreet coordination

• Digital assets facilitating virtually instant fund transfers across jurisdictions

• AI and machine learning models allowing fraud networks to scale and adapt faster than human-led detection systems

Financial cybercrime has irrevocably industrialized. These aren't one-off scams: We’re seeing entire operations run by sophisticated criminal syndicates, some backed by nation-states. The growth of borderless, programmable payments has created a corresponding need for detection tools that can operate across identity, device, transaction, and blockchain layers simultaneously.

3. AI is fueling the industrialization of financial fraud

The game has changed. This isn't card fraud in a new wrapper, it's an entirely new category of crime powered by AI. The fraud landscape is becoming increasingly automated and scalable. Criminal networks are building fraud assembly lines, operating around the clock while most risk teams still work business hours

Common tactics include:

• Deepfakes used to bypass video-based KYC

• Credential-stuffing bots executing large-scale account takeovers

• Smart contracts and DeFi protocols exploited for laundering, fund fragmentation, and obfuscation

• Behavioral emulation tools that mimic legitimate users across web and mobile platforms

This shift toward machine-driven attacks has created a growing gap in speed and sophistication between adversaries and the financial institutions trying to stop them.

4. Legacy risk tools aren’t built for a blockchain and AI-activated world

Most current risk systems rely on static rules and human-in-the-loop workflows. These tools were not designed for environments where threats emerge and adapt in real time.

Common limitations include:

• Fragmented systems that separate identity, transaction, and blockchain data

• Rules-based monitoring that cannot adapt to new or obfuscated attack patterns

• One-time KYC checks that fail to detect synthetic identities or account takeover

• Delayed alerting, with multi-day lags between threat activity and response

• Manual case management processes that cannot scale with machine-speed crime

We're using tools built for a world of forms and fax machines to fight criminal operations powered by generative AI. In practice, this means that by the time a fraud signal is reviewed, the underlying funds have often already moved, leaving limited opportunity for recourse.

5. AI-native defense is no longer optional for modern finance

The speakers emphasized the need for agentic AI: Systems that not only detect but act and adapt autonomously in response to evolving threats. Agentic AI isn’t a luxury; it’s our best shot at keeping pace with the scale, speed, and complexity of stablecoin risk.

Important capabilities of platforms like Oscilar include:

• Real-time signal orchestration across identity, transaction, and blockchain data

• Use of behavioral biometrics and device fingerprints to establish resilient identity signals

• Graph analytics for detecting laundering networks and coordinated activity

• Autonomous rule generation and deployment

• AI co-pilots to assist in investigations and decision workflows

The conversation also emphasized the role of stablecoin issuers in maintaining trust across the ecosystem. Stablecoins offer a unique opportunity for ecosystem participants to lead on safety and integrity. Centralized issuers like Circle (USDC) and Tether (USDT) have the authority to freeze assets and monitor suspicious flows, but only if supported by the right infrastructure to detect risks before they escalate.

The future of stablecoins depends on trust

Stablecoins are poised to become a cornerstone of modern financial infrastructure, powering everything from real-time settlement to programmable money. But long-term adoption hinges on whether users, institutions, and regulators trust that these systems can be secured at scale.

"None of this matters without trust."—Saurabh Bajaj

As adoption grows, the risks will scale in parallel. You can't build 2025-level trust with tools from 2015. The new trust equation demands real-time AI defense. Platforms that build with AI-native risk infrastructure will be better equipped to detect and respond to evolving threats, while those that rely on traditional tools won't just fall behind, but may be a future casualty.

Not because they're not trying hard enough, but because they're fighting the wrong war.

Want to see how Oscilar’s AI Risk Decisioning™ platform powers safer, smarter stablecoin ops? Unify your data, stop fraud before it starts, and take control of risk and compliance with AI you can actually test, trust, and fine-tune. Request a demo.