Why the finance industry is looking to agentic AI

Why the finance industry is looking to agentic AI

Why the finance industry is looking – Imagine a scenario where an AI assistant manages your shopping tasks without needing constant input. This concept, known as agentic AI, represents software capable of executing intricate operations independently. At the Money 20/20 Europe conference in Amsterdam, three major players in the financial sector—Mastercard, ING, and Worldline—announced a groundbreaking achievement: the completion of “Europe’s first live end-to-end agentic payment.” The demonstration showcased an AI agent identifying concert tickets in a specified location and price range, then proceeding to make a purchase once the shopper selected an option, with final approval from a human. This example underscores how agentic AI is transforming financial workflows by enabling autonomous decision-making and execution.

A Paradigm Shift in Financial Tech

Agentic AI has emerged as a central theme at the Money 20/20 Europe event, widely regarded as the most significant annual gathering of fintech professionals. For years, the sector viewed traditional banks as competitors, but now many are collaborating to integrate these advanced technologies. Scarlett Sieber, the conference’s chief strategy and growth officer, emphasized that AI in finance has evolved from a marketing catchphrase to a tangible reality. “The shift has moved beyond startups,” she stated, noting that widespread adoption is occurring across the industry. This collaboration signals a broader trend where innovation is no longer confined to agile startups but is being embraced by established institutions.

“AI in finance used to be a buzzword, but now real adoption is happening across the board.”

The potential of agentic AI is now being harnessed in various ways, from automating routine transactions to enhancing customer interactions. A 2026 report by the University of Cambridge, which surveyed over 600 companies and regulatory bodies globally, predicts that AI agent deployment in the financial industry will surge from 24% to 81% by 2030. However, the report also highlights a critical challenge: the pace of technological advancement has outstripped the ability of current supervisory systems and technical expertise to keep up. This gap raises questions about how effectively the industry can manage the risks and complexities associated with these innovations.

AI Agents in Action: Real-World Applications

One standout example of agentic AI in action is the eToro platform, a well-known Israeli fintech company. Its app allows users to trade stocks and mirror the strategies of other investors. Recently, the company upgraded its AI assistant to act on behalf of users within predefined limits. A notable feature is POTU$, an app that monitors Donald Trump’s social media and news coverage. When the president shares content likely to influence markets, the AI can execute trades in a user’s account almost instantly. This demonstrates how agentic AI is being used to make real-time financial decisions based on external data.

“AI is useless without humans steering it.”

According to Yoni Assia, eToro’s CEO, the company has seen a dramatic increase in AI usage. In just six months, AI integration has expanded roughly tenfold, and 95% of new code is now written by AI systems. However, he stressed that human oversight remains essential. “The AI assistant is a tool, not a replacement for human judgment,” he explained. This balance between automation and human control is a recurring theme across the industry, as organizations seek to leverage AI’s capabilities while maintaining accountability.

Automation and Efficiency: The Klarna Case

Swedish fintech giant Klarna has also embraced agentic AI, integrating it into its customer service operations. In 2024, the company launched an AI assistant powered by OpenAI, which handles inquiries and transactions, effectively replacing the work of 700 full-time employees. Klarna’s CEO, Sebastian Siemiatkowski, noted that the technology has enabled the company to reduce its workforce from 6,000 to fewer than 3,000 in recent years while boosting revenue per employee. Yet, the shift hasn’t been without challenges. Siemiatkowski acknowledged that cost-cutting measures initially led to a decline in service quality, prompting the company to rehire human agents and invest in improving customer support.

“New technology allows us to do more with less,”

Siemiatkowski added, highlighting how automation has reshaped operational efficiency. This approach reflects a growing trend in the industry where AI is used to streamline processes and cut costs. However, the CEO also warned about the broader implications of such technology. “AI could lead to job losses across industries,” he said, noting that roles in customer-facing sectors—like sales and legal services—may remain resilient, while certain job categories could face short-term disruptions. The exact percentage of jobs replaced by AI at Klarna remains undisclosed, underscoring the need for transparency in AI’s impact on employment.

Traditional Banks Embrace Digital Transformation

Even traditional institutions are adapting to the agentic AI wave. ABN AMRO, the third-largest Dutch bank, has seen its physical branch network shrink from 500 in 2010 to just 26 today. The bank’s CEO, Marguerite Bérard, stated that 85% of her team now relies on AI in their daily tasks. For instance, the bank’s AI bot “Ana” facilitates millions of conversations with customers, while “Lenny” simplifies credit applications. These tools exemplify how traditional banks are merging legacy systems with cutting-edge automation to stay competitive in a rapidly evolving market.

Challenges and Concerns

Despite the enthusiasm for agentic AI, some experts caution about its rapid adoption. Research firm Gartner predicted that over 40% of agentic AI projects will be canceled by the end of 2027, citing reasons such as rising costs, unclear business benefits, and insufficient risk management protocols. A joint report from Accenture and Wharton business school further warned that increased automation requires leaders to define which decisions should be entrusted to AI and which must retain human oversight. The study emphasizes the importance of embedding governance, accountability, and trust into AI systems to prevent potential pitfalls.

Bérard of ABN AMRO echoed this sentiment, emphasizing the role of human guidance in AI implementation. “If you apply AI to an inefficient process, you’ll still end up with inefficiencies,” she said. This perspective highlights the need for careful integration of AI into existing workflows. As agentic AI becomes more prevalent, the industry must address concerns about scalability, reliability, and the long-term effects on labor markets. The collaboration between fintech innovators and traditional banks suggests that the future of finance will be shaped by a delicate balance between human expertise and machine efficiency.

While agentic AI offers unprecedented opportunities for automation and efficiency, its success depends on how well it aligns with human needs and values. The examples from Mastercard, ING, Worldline, eToro, and Klarna illustrate a sector-wide shift toward leveraging AI to enhance services and reduce costs. Yet, the challenges outlined by Gartner and Accenture remind stakeholders that the journey is far from complete. As the technology matures, the focus will remain on refining its applications, ensuring robust oversight, and navigating its impact on employment. For now, the finance industry is at the forefront of a digital revolution, with agentic AI driving both innovation and transformation.