A new kind of money gains momentum despite concerns
Money has always operated on a relatively simple premise. It moves between people and institutions as a medium of exchange, a store of value, and a unit of account. That premise is beginning to change.
As financial systems become increasingly digital and artificial intelligence reshapes how transactions are processed, verified, and monitored, money is evolving into something far more capable. It is increasingly carrying rules, identity checks, compliance requirements, and automation directly within transactions themselves. That shift is gaining serious momentum and the financial industry is only beginning to reckon with what it means.
What programmable money actually is
The concept of programmable money is moving into the mainstream of financial services. Financial institutions are exploring new ways to embed compliance rules, identity verification, and automation directly into payment and settlement systems. Central banks globally are researching frameworks for more programmable forms of currency. Payment firms are rebuilding infrastructure to carry logic alongside value.
Artificial intelligence is accelerating the shift. AI systems are already deployed to monitor fraud, automate compliance checks, optimize treasury management, and process large volumes of financial data in real time, according to McKinsey.
Over time, AI agents could manage payments, subscriptions, and financial interactions autonomously, creating a future where money itself becomes more intelligent and embedded with programmable logic.
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That creates both an opportunity and a pressure point. As financial systems collect and process more data than ever before, the question of who controls that data, and who can see it, is becoming one of the most consequential debates in modern finance.
Why the privacy and compliance trade-off is being challenged
For decades, financial compliance has operated on a straightforward assumption: institutions need broad access to user data to verify legitimacy and prevent illicit activity. Emerging technologies are now challenging whether that assumption was ever necessary, or simply a limitation of the infrastructure that existed at the time.
"The perceived trade-off between privacy and compliance is largely a byproduct of legacy financial infrastructure, where verification requires full data exposure," Wish Wu, CEO of Pharos, told TheStreet.
Pharos is building programmable financial infrastructure designed for real-world asset markets and institutional finance. Wu's argument is that the trade-off most people assume exists between privacy and compliance is an artifact of old system design, not an inherent feature of how compliance works.
In a programmable financial system, the logic changes. Instead of requiring institutions to collect and store sensitive personal information, systems can be designed to verify that specific conditions have been satisfied without ever exposing the underlying data. A user can prove they meet regulatory requirements without handing over a passport. An institution can confirm a counterparty's eligibility without seeing their full financial profile.
John Woods, CEO of Nillion, frames the distinction this way. "The trade-off is becoming outdated because compliance and surveillance are 2 very different things," he told TheStreet. Nillion builds privacy-preserving computation infrastructure that allows sensitive data to be processed and verified without being exposed.
"The current model often treats access to data as the source of trust," Woods said. "The better model is one where trust comes from verification."
The technology enabling a new model
Zero-knowledge proofs are central to making this shift practical. The technology allows one party to prove a statement is true without revealing the information behind it. A user can verify their age, identity status, or compliance standing without exposing personal documents or financial history. For regulated financial flows, that capability is significant.
But technology alone is not the full picture. Woods points to a gap between having a proof and being able to trust the system producing it. That is why Nillion launched Blacklight, a tool designed to allow private applications to be verified continuously, so users and institutions can confirm that a privacy-preserving computation is running correctly and has not been compromised, according to Nillion.
The practical effect is that selective disclosure becomes credible at scale. An institution gets the answer it needs. The user does not expose data beyond what is required. And the privacy of the system producing that verification can itself be confirmed, according to Nillion.
Why institutions need this to move forward
The stakes are not theoretical. Large financial institutions operate under strict confidentiality obligations. Balance sheet positions, client data, and transaction activity cannot be exposed to competitors or the public. Any digital financial infrastructure that makes that exposure a default condition will not attract serious institutional capital.
"No serious institution can operate in an environment where balance sheets, positions, or underlying RWA data are fully exposed, which makes over-transparency a bottleneck rather than an advantage," Wu told TheStreet.
The model gaining traction instead is selective disclosure: transactions remain verifiable and auditable to regulators and approved counterparties, but sensitive information stays protected by default. Wu calls this the "verifiable but not visible" paradigm, a design principle he argues is essential for modern financial systems to preserve the confidentiality standards that institutions already expect.
The stakes are real. Data breaches and financial fraud tied to overexposure of sensitive financial information cost institutions billions annually, according to IBM's Cost of a Data Breach Report. Overexposure is not just a privacy concern. It is a systemic risk.
Key context on programmable money and financial privacy:
- AI adoption in financial services compliance and fraud detection grew 45% year-over-year in 2025, according to McKinsey
- The average cost of a financial data breach reached $6.08 million per incident in 2024, according to IBM
- Over 130 central banks are actively researching or piloting programmable currency frameworks as of early 2026, according to the Atlantic Council
- Global payment system modernization investment is projected to exceed $87 billion by 2030 as institutions rebuild infrastructure for programmable finance, according to McKinsey
Where this leaves the future of financial trust
The direction of travel across programmable finance, privacy-preserving infrastructure, and AI-driven compliance is converging on a common conclusion. Financial trust does not have to depend on exposure. It can be built on verification instead.
That shift changes more than just technology architecture. It changes the relationship between users and institutions, between regulators and markets, and between financial privacy and accountability. Systems that can deliver verifiable compliance without requiring unnecessary data exposure offer a path through one of the central tensions in modern financial services.
"As more capital and real-world financial activity move into digital systems, privacy will become a baseline expectation," Woods told TheStreet. "The next generation of financial applications will need a private layer by default, where sensitive data can be used, decisions can be made, and verification can still happen without exposing the full picture."
The transformation of money from a passive medium of exchange into programmable infrastructure is already underway. Whether the financial industry can build that infrastructure with privacy as a foundational principle, rather than an afterthought, will determine how broadly and how quickly that transformation reaches mainstream adoption.
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This story was originally published May 8, 2026 at 9:03 AM.