Artificial intelligence is rapidly transforming financial technology, from fraud detection to onboarding flows. But a powerful evolution is quietly unfolding: agentic AI. These are advanced AI systems that can reason, act on goals and adapt autonomously without the need of constant human oversight.
Unlike traditional automation or narrow AI tools with limited scope, agentic AI excels at evaluating context, making complex multi-step decisions and adjusting to real-time changes. This shift from basic automation to true autonomy isn’t theoretical anymore, it’s already reshaping payments, risk management, and infrastructure orchestration within fintech.
What is agentic AI?
Agentic AI refers to advanced AI models that exhibit agency: the ability to plan, act, and learn with a significant degree of autonomy. Unlike traditional AI, agentic AI systems are proactive problem-solvers able to manage workflows, navigate ambiguity, and pursue evolving goals exactly the same way a human would do it.
In fintech, this translates to AI agents that don’t just analyze transactions or answer queries, they:
- Make real-time routing decisions for payments
- Ensure regulatory compliance
- Proactively manage liquidity
- Optimize operational costs across various layers of financial infrastructure
A perfect illustration of agentic AI in action is Google's innovative "Buy for me" feature. It basically allows you to track a product's price and, once it meets your desired pricing, the system autonomously executes the payment without any manual intervention from you. Agentic AI empowers systems to operate with unprecedented independence and decision-making abilities that go far beyond traditional AI tools.
How agentic AI is used in fintech today
From startups to global financial platforms, fintech players are rapidly integrating agentic AI into high-impact domains. Here's how:
1. Payment Processor Selection Agents
Agentic AI is being implemented to dynamically select the best payment processor for each transaction. These agents evaluate factors such as:
- Real-time processing fees
- Downtime or latency metrics
- Regional authorization rates
- Processor-specific rules or risk thresholds
They act as orchestration layers, ensuring every payment is routed through the most efficient and cost-effective partner with zero human intervention. If you want to go deep into this topic, here's an article you can further explore.
2. Next-gen personal financial assistants
AI-powered financial assistants driven by agentic AI go far beyond static chatbots. These intelligent agents are capable of:
- Analyzing cash flow trends, offering clear insights on the users’ finances
- Forecasting income and expenses
- Recommending savings and debt strategies
- Executing actions like automatically transferring funds or adjusting budgets in real-time
Visa recently announced AI shopping agents that can autonomously reorder recurring items, optimizing cost and timing through user behavior and preferences.
3. Risk and Fraud Management
Agentic AI agents are redefining fraud detection providing proactive and continuously evolving security by:
- Monitoring real-time user behavior and identifying anomalies
- Detecting emerging threat patterns
- Triggering account freezes or multi-factor verification when identifying a threat
Unlike fixed, rule-based systems, these agents evolve with each transaction, providing constant adaptive security.
4. Autonomous Underwriting and Portfolio Management
On lending and investment platforms, agentic AI is transforming decision-making by being able to:
- Aggregate data from credit bureaus, bank feeds, and public records
- Score risk and exposure dynamically
- Make or suggest approvals/adjustments in real-time
This not only streamlines decisions but also improves personalization and portfolio performance.
5. Agents that make purchases on behalf of users
A rapidly growing area of experimentation involves AI agents capable of autonomously completing purchases for users, moving beyond recommendations into actual transaction execution. These agents are designed to anticipate the optimal moment for a purchase and complete the entire checkout flows across various merchants.
This model, often referred to as agentic commerce, introduces a shift in how transactions are initiated: users define preferences and boundaries, while agents act within them to simplify decision-making and reduce friction.
Network tokens play a foundational role here, enabling persistent, secure credentials across payment networks. This allows AI agents to operate seamlessly across multiple platforms, maintaining security and continuity in the users' identity. As Jason Heister outlines, this approach could reshape the function of digital wallets from passive tools to active participants in commerce.
Short-term challenges to watch
As powerful as agentic AI may be, the short-term risks are significant. One critical issue is accountability: if an AI agent violates a regulation, such as misclassifying a financial transaction or failing to comply with a data privacy law, who is legally responsible? The developer? The financial institution? The end-user? The AI itself? These questions are still largely unresolved.
Additionally, there are operational risks and security threats: An agent's behavior changes over time in unintended ways,which can lead to:
- Model drifts: an agent's behavior could unintentionally change over time
- System interoperability failures: challenges arise when AI agents interact with legacy banking technology
- Edge-case exploitation: malicious actors could exploit unforeseen vulnerabilities
- Lack of standardization: inconsistent outcomes across platforms could lead to disruption
Organizations must prepare with strong monitoring, legal frameworks, and rapid-response mechanisms to mitigate these early-stage challenges.
Last but not least, a particularly pressing concern relates to KYC (Know Your Customer) and compliance processes. As AI agents become more involved in onboarding and identity verification flows, the risk of synthetic identity fraud rises sharply. These are AI-generated or manipulated identities that can bypass traditional checks. As Amy O’Grady points out, AI itself can be both the attacker and the defender in this space, which raises urgent questions about the robustness of compliance frameworks in this new agentic world.
Why it matters
As fintech continues to scale, intelligent systems that can adapt, decide and act independently are no longer a luxury, they’re becoming a commodity. Benefits include:
- Operational scalability with reduced manual intervention
- Dynamic cost optimization in processing and routing
- Risk mitigation via adaptive learning and real-time alerts
- Faster innovation through autonomous infrastructure management
According to The Paypers, agentic AI is the next competitive frontier in fintech, delivering hyper-efficiency while unlocking new forms of financial inclusion.
What’s next for agentic AI?
As fintech infrastructure evolves from API-first architectures to self-healing orchestration layers, these agents are moving from the edge to the heart of fintech platforms. But this degree of autonomy introduces new responsibilities:
- Governance: ensuring oversight with "human above the loop" models
- Transparency: making traceable and explainable decisions
- Ethics & Bias: preventing systemic discrimination through rigorous testing and responsible AI development
- Regulation: keeping pace with standards like the EU’s AI Act
In short, building AI agents isn’t just about what they can do, it’s about how responsibly they do it.
Final thoughts
Agentic AI represents a shift from reactive automation to proactive intelligence. It’s already reshaping how fintechs operate, from micro-loans in emerging markets to autonomous agents managing payments, compliance, and portfolios. With the right safeguards, these systems can drive not just efficiency, but real inclusion and innovation.
Still, some critics question whether this is truly "intelligent". As The Guardian recently posted, many AI models collapse under stress or novelty, suggesting we may be overestimating their autonomy. But perhaps intelligence isn't the point: utility, reliability, and accountability may prove to be more crucial metrics. So the question is: how much autonomy are we really comfortable giving these systems and what would you trust them to decide?
