“Composable finance” is more than a trendy buzzword. Its application in daily practice marks a decisive shift in how modern financial systems are designed. Instead of rigid platforms, they take shape as loosely coupled, purpose-specific components that can be deployed, upgraded, or swapped without rearchitecting the entire stack. In this model, payment authorization, tokenized identity, fraud analysis, and other core functions aren’t embedded. Rather, they’re externalized through application programming interfaces (APIs) and integrated in real time.
The rise of autonomous, agent-driven finance makes this architecture urgent, not optional. Pushing composability from concept to necessity is the rise of agentic artificial intelligence (AI) and decentralized orchestration. As autonomous systems begin initiating transactions or managing workflows, they need access to services that are programmatically accessible and context-aware. Composable design provides that foundation, enabling real-time orchestration across systems without exposing raw credentials, duplicating logic, or centralizing risk. For developers and product leaders, the takeaway is clear: the age of static systems is closing fast.
Rahulkumar Chawda is a global product leader and recognized expert in digital payments, with more than two decades of experience building secure, scalable financial solutions. At the forefront of innovation in transaction processing, tokenization, and API ecosystems, he has led transformative initiatives that align technology with business strategy, enabling more inclusive and future-ready commerce worldwide.
In this Q&A, Chawda offers insights about key features of composable finance, overcoming the biggest challenges in implementing AI in composable finance systems, and how agentic AI and decentralized orchestration shape the financial infrastructure.
Q: What are the key features of composable finance, and what role does it play in modern financial systems?
Chawda: At its core, composable finance is about modularity. In practice, it relies on an API-first architecture, tokenization for secure data exchange, and standardized interfaces to simplify integration. It enables the independent evolution of every service. New fraud models can be installed without manipulating the underlying ledger, or a new identity provider may be replaced without modifying transaction handling.
Its role in modern finance is now more strategic than just “nice to have.” Composable finance lets banks, fintechs, and payment networks respond more rapidly to shifts in the market, integrate new technologies like agentic AI with minimal disruption, and reduce operational risk by eliminating single points of failure. It also enables context-sensitive, personalized finance.
Q: How do agentic AI and decentralized orchestration shape financial infrastructure?
Chawda: Decentralized orchestration and agentic AI move banking infrastructure away from traditional, reactive, request–response systems and toward proactive, decentralized ecosystems that can foresee, spawn, and complete complex workflows without frequent human intervention.
In practice, agentic AI processes financial information in real time, spots patterns, makes choices, and takes actions such as shifting a payment path, sending a payment into further fraud analysis, or fulfilling a multistep investment transaction. These agents don’t simply run a script and then stop. They refine their choices when context changes, such as variations in an exchange rate, network traffic, or a customer’s behavior.
Decentralized orchestration augments this process by allowing AI agents to connect multiple services, including payment authorization, identity verification, and liquidity management, without depending upon a single central hub. Instead, every service is offered through secure APIs, accessible only via tokenized credentials, and is built to interoperate. This architecture reduces bottlenecks, eliminates single points of failure, and ensures innovation in one module does not necessitate a full-system revamp.
In combination, they create a self-managing and context-sensitive financial infrastructure. A cross-border payment, for instance, might be automatically routed through the cheapest, quickest corridor available at a given time, considering risk scores, compliance rules, and even regional liquidity, all coordinated in real time by self-managing agents. This process is less about efficiency and more about designing an infrastructure that can scale and evolve as quickly as market dynamics shift.
Q: Is the industry ready for AI agents to handle financial operations, and how can it prepare for advancing technologies?
Chawda: In some ways, the industry is ready. But in others, it is still catching up. Most banks, fintechs, and payment processes are already built on API-first designs, which means that AI agents can technically plug into and run workflows. With tokenized security models and zero-trust frameworks, agents can access sensitive systems without ever exposing raw data or credentials.
There are proven use cases. AI is widely used in fraud detection, credit scoring, and payment routing at scale. The next logical step is to extend those applications into more autonomous, agent-driven models.
The industry is not ready on the governance side. Regulations still assume that a human decision maker is accountable for every action. When an agent initiates a transaction autonomously and flags a false positive, who carries the liability? That is still a grey area. Also, while AI can enforce rules and optimize processes, true context sensitivity, like understanding customer intent, market nuances, and regulatory subtleties, continues to evolve. The required guardrails and security layers are largely in place, but governance, contextual intelligence, and trust mechanisms need to mature before AI agents can fully handle end-to-end financial operations without constant human oversight.
Q: What are the biggest challenges when implementing AI in composable finance systems, and how can companies overcome them?
Chawda: Deploying AI in composable finance systems requires a careful balance of intelligence, security, governance, and interoperability. These challenges fit into three key areas. The first is data fragmentation and quality. In a composable architecture, data resides across multiple modular services. If these services lack consistent, high-quality datasets, AI models may generate skewed or incomplete outputs. Overcoming this requires establishing common data standards and real-time sync layers so each module supplies AI with uniform, context-enriched information.
Another key area is security in highly connected environments. More modules translate to more API endpoints, and more endpoints potentially increase the attack surface. It is crucial for AI services, especially agentic AI, to operate without revealing raw credentials or sensitive data. Overcoming this hurdle involves combining tokenization, zero-trust access controls, and continuous monitoring so AI agents can orchestrate workflows without ever holding the “keys” themselves.
A third challenge is regulatory and ethical compliance. AI decisions, particularly autonomous ones, need to comply with prevailing financial regulations and new AI governance frameworks. In most jurisdictions, explainability is mandatory. Clearing this hurdle entails constructing explainable AI models, integrating compliance checks into orchestration flows, and maintaining immutable logs of AI-driven action for audits.
Q: What data privacy and cybersecurity measures support tokenized, AI-powered financial services?
Chawda: It is imperative for teams to embed privacy and cybersecurity into the architecture from the outset, rather than as an afterthought. The goal is to allow AI and tokenization to collaborate, without involving raw, exploitable data, while still accommodating rapid, intelligent decision-making.
Tokenization is the principal defense layer. Replace sensitive identifiers like card numbers, account IDs, and personal details with domain-specific, context-bound tokens. These tokens are useless if intercepted and valid only within predefined boundaries, for example, a specific merchant, channel, or time window.
Privacy-preserving AI techniques are also essential. AI models need to use methods such as federated learning or secure multiparty computing to learn from distributed data without moving sensitive records to a central location. This approach improves models without raw data leaving its source system. In addition, deploy AI-powered security monitoring alongside financial AI agents. Doing so allows the system to detect abnormal patterns in milliseconds, whether they indicate fraud, API abuse, or adversarial manipulation of an AI model .
Shaping the future of composable finance
Several real-world examples illustrate the impact of tokenization, agentic infrastructure, and composable finance in financial services, including Adyen’s tokenization of online payments. Adyen reduced false declines and improved authorization rates in e-commerce applications by swapping primary account numbers with secure tokens. The result was a substantial increase in approval rate, lower fraud, fewer false declines, and improved customer retention. This use case demonstrates that tokenization allows key payment components, such as fraud detection, routing, and authentication, to operate independently, enhancing flexibility, conversion and security.
Another example is TerraPay, which uses AI frameworks to deliver automated compliance and support for cross-border payments by handling alerts, identifying issues, and facilitating faster resolution via human-AI collaboration. This improves operational efficiency and accelerates anomaly analysis, while primary decisions remain human-verified. Agentic AI brings scale and speed to complex cross-border workflows, but trust and human oversight are still critical in sensitive transaction paths.
These examples indicate the future of financial infrastructure will be defined by trust at scale, and where tokenization, self-managing orchestration, and composable architecture converge to create systems that are secure by design and adaptive by nature. For forward-looking organizations, the solution lies in investing in API standardization, tokenization gateways, and runtime governance. These pillars of agent-driven composability are safe and scalable. Those who delay will be stuck with inflexible systems and unable to compete in a world where speed, adaptability, and trust are closely intertwined.
The direction is clear: the era of static, closed financial systems is over. The next era will be governed, intelligent, and composable by default, rewarding organizations that build adaptability into their designs from day one.
About the Author:
Mark McGraw is a freelance writer with more than 20 years of experience covering business, technology, and workplace topics. He can be reached at markmc34@gmail.com.
Tag/s:Artificial IntelligenceBusiness TransformationCybersecurityFinance
