The era of passive chatbots is ending. We are now entering the age of agentic AI – autonomous systems capable of reasoning, planning, and executing complex workflows with minimal human intervention.
For fintech executives, this is not merely a technical upgrade; it is a fundamental operational shift. According to Gartner, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024.
The implications for payment processing, fraud detection, and customer experience are profound, promising a transition from simple automation to intelligent autonomy.
This article explores how industry leaders are navigating this shift, balancing the immense ROI potential with the critical need to govern agentic AI in fintech.
1. The shift: from reactive to autonomous
Traditionally, automation in fintech has been rules-based: “If X happens, do Y.” Agentic AI breaks this mould. It does not just follow a script; it perceives its environment and pursues goals.
As Riken Shah, Founder & CEO of OSP Labs, explains: “Agentic AI will transform fintech from reactive automation to autonomous decision execution. These agents work across systems, enabling platforms to handle complexity at scale while reducing operational costs.”
The market reflects this urgency. Recent data suggests the global agentic AI market is projected to reach US$57.42 billion by 2031, with financial services being a primary driver.
The race is no longer just about faster processing; it is about smarter execution.
2. Friend or foe: the traffic identity crisis
With the rise of autonomous agents, the nature of web traffic is changing. It is no longer just humans visiting your platform; it is AI agents acting on their behalf.
This innovation creates a critical challenge for security teams: distinguishing between helpful “commercial agents” and malicious “scraper bots”.
Fingerprint, a leader in fraud detection technology, warns that the traditional “block all bots” approach is obsolete. It risks breaking legitimate workflows and leaving revenue on the table.
“Fingerprint will be announcing the launch of a new ecosystem to identify authentic agentic AI traffic, allowing enterprises to distinguish machine friends from foes,” the company states.
By identifying the intent of web visitors rather than just their non-human status, businesses can safely enable legitimate AI visitors – such as shopping assistants or aggregators – while keeping fraudsters out.
3. The co-pilot reality: humans remain in the loop
Despite the “autonomous” label, the most successful implementations of agentic AI currently rely on a human-centric model. The goal is augmentation, not total replacement, particularly in high-stakes environments like Anti-Money Laundering (AML).
“In sensitive areas such as payments or AML, the most viable model remains human-in-the-loop: AI can suggest actions or prepare recommendations, but the final decision is always made by a person,” says Serge Kuznetsov, Co-Founder at INXY Payments.
Serge argues that bans cannot solve the “chaotic use” of AI by employees. “Bans simply do not work here. Instead, companies will increasingly move toward legitimising and standardising AI usage… defining which models and subscriptions are permitted.”
4. Case study: Mastercard’s decision intelligence
The power of this technology is already visible in fraud prevention. Mastercard recently integrated Generative AI with its Decision Intelligence platform to combat card fraud.
By simulating fraud scenarios and generating synthetic transaction data, the system allows Mastercard to detect compromised cards 2x faster than previous methods.
Mastercard’s proactive stance boosted its detection rate by up to 300% in pilot programmes, proving that agentic AI can secure the ecosystem without adding friction for legitimate customers.
5. Operational velocity: the “vibe-coded” agent
Inside the enterprise, agentic AI is democratising technical capabilities. Business teams no longer need to wait for IT to script every report or reconciliation.
Sibasis Padhi, Staff Software Engineer at Walmart Global Tech, highlights the rise of “Vibe-Coded” agents:
“Finance managers and business teams use natural-language agents to automate reports, approvals, reconciliations, and incentive calculations without writing code – turning manual workflows into governed, repeatable processes.”
These agents excel at Financial Operations & Audit Readiness, where they can reconcile data across systems and maintain audit-ready logs. This reduces the manual effort required during financial closes and regulatory reviews, freeing up talent for strategic analysis.
6. The productivity multiplier
The efficiency gains from these “digital workers” are quantifiable. Research by McKinsey indicates that in financial crime operations, a single compliance officer supervising a team of AI agents can achieve productivity gains of 200% to 2,000%.
By redesigning workflows around agents – who handle data retrieval, pattern recognition, and initial reporting – human experts can focus entirely on high-value decision-making. See McKinsey’s insights on agentic AI.
7. Agentic commerce: the new buyer
Perhaps the most disruptive change is occurring on the customer side. We are moving towards agentic commerce, where AI assistants handle purchases autonomously.
“By 2026, forward-thinking companies deploy AI agents that can execute multi-step financial tasks with minimal human oversight,” predicts Ximena Alemán, Co-Founder and Co-CEO of Prometeo.
She describes a landscape where agents reorder supplies, select optimal suppliers, and complete payments. “Businesses that make their pricing transparent and their systems AI-accessible will have a competitive advantage in this efficiency-driven landscape.”
8. Governance as a growth lever
With great power comes great regulatory scrutiny. The autonomy of these agents introduces risks regarding explainability and bias.
If an AI agent denies a loan or flags a legitimate transaction as fraud, the institution must be able to explain why.
“The biggest opportunity and challenge lies in governance,” notes Riken Shah. “Companies that invest in explainability, auditability, and human control will harness speed and agility.”
Regulatory bodies are paying attention. As Hogan Lovells points out, the “black box” nature of autonomous agents can conflict with existing frameworks that demand clear accountability.
Investing in “audit-ready” AI infrastructure is no longer optional; it is a license to operate.
9. Future outlook: the autonomous enterprise
The trajectory is clear. We are moving from tools that help us work to agents that do the work. However, as Serge Kuznetsov wisely reminds us, “Automating poorly designed processes almost always leads to automated chaos.”
Success in the agentic era requires a three-pronged strategy:
- Standardise Data: Ensure your agents have reliable ground truth to work from.
- Embed Governance: Build “human-in-the-loop” checkpoints into every workflow.
- Identify Intent: Distinguish between AI agents that build your business and those that try to exploit it.
The fintechs that master this balance will not just survive the AI revolution; they will define it.