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From Autonomous Finance to Agentic AI: Lessons from Three Transformative Events

AI is reshaping finance, building on LLMs to enable agentic systems that deliver autonomy, scale, and hyper-personalization.

Simon Axon
Simon Axon
2025년 12월 12일 3 최소 읽기

This September, I attended three standout events that confirmed the financial services industry is undergoing a seismic shift: a community event hosted by Google Cloud, Arena International's Artificial Intelligence in Financial Services Conference in London, and The Data Lab’s session on scalable AI in Edinburgh. The message was clear: the transformation from conceptual AI to strategic, autonomous systems is happening now.

The autonomous financial institution: a vision in motion 

At the Google event, Tom Mason, Technical Director at Google Cloud's Office of the CTO, declared that “the pace is prodigious.” He mapped the rapid evolution from large language models in late 2023 to the era of agentic AI, which by April 2025 had already begun to take shape. This isn’t a distant future; it’s a strategic imperative. The vision is of an autonomous institution where AI agents can reason, plan, and act independently. Practical applications are already moving from theory to production—from generative AI copilots and autonomous portfolio agents to multimodal AI that can analyze text, voice, and transaction data for real-time fraud detection.

Agentic AI: the industry's next frontier 

This theme dominated the Arena International conference, where AI agents were framed as the next evolutionary step. These agents autonomously execute complex tasks, making them ideal for financial operations. The industry’s deep focus was clear, with sessions from EY on “Agents of Change,” Celent on “The New World of Agentic AI,” and a fireside chat with JoAnn Stonier of Mastercard exploring the future of AI agents. Firms such as Weave.AI highlighted continuous risk monitoring, underscoring a major pivot towards building more intelligent, autonomous systems.

Real-world impact and lessons from the field 

The tangible benefits of AI were consistently highlighted, with firms achieving production-level outcomes. In one example shared at the Google Cloud event, a challenger bank in the UK realized impressive results, including a 40% reduction in onboarding time and a 30% drop in false positives in anti-money laundering.

The panel in Edinburgh, however, added a dose of realism. Vinay Jha, Data Director at Lloyds Banking Group, argued that true AI at scale is achieved only when it’s “embedded into the core processes so that it quietly improves those millions of everyday moments for our customers.” He stressed that without a trusted data foundation, firms can run pilots but will never truly scale.

Martin Willcox, VP at Teradata, reinforced this, warning against the “garbage in garbage out problem” and the need for robust governance to explain decisions to regulators. He shared a powerful example of success: a large Asian bank analyzed 50,000 customer chats weekly with LLMs to understand its Net Promoter Score (NPS) drivers in near real time—unlocking immense value from previously ignored unstructured data.

The future: a consolidated vision for 2030 

Looking ahead, a consolidated vision for an AI-enabled future emerged across the events. The Google Cloud event outlined a 2030 roadmap where AI automates 80% of repetitive workflows, AI advisors are available 24/7, and products are hyper-personalized in real time.

Panelists in Edinburgh offered “moonshots” that echoed this vision. Vinay Jha described a future of true financial inclusion, where a bank could create personalized protection products on the spot for a customer with a £50 monthly budget. Others extended this vision, such as Dave McCallum, Senior Manager in Data & AI at Accenture, who foresees hyper-personalized financial agents managing an individual’s entire financial life—from tax optimization to debt consolidation. This focus extends beyond technology, as the AI in financial services conference closed with a keynote on building an AI-ready workforce through mentorship and upskilling programs for the younger generation.

Final thoughts 

The consistent theme across all three experiences was that AI is not just a tool—it’s a transformation. Success will require more than technology; it demands leadership, trust, and a willingness to rethink how financial services create value.

The future is here—and it’s agentic.

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약 Simon Axon

Simon’s primary focus is to help Teradata customers drive more business value from their data by understanding the impact of integrated data, advanced analytics and AI. With a background that includes leadership roles in Data Science, Business Analysis and Industry Consultancy across Europe, Middle East & Asia-Pacific, Simon applies his diverse experience to understand customers’ needs and identify opportunities to put data and analytics to work – achieving high-impact business outcomes.

Having worked for the Sainsbury’s Group and CACI Limited prior to joining Teradata in 2015, Simon is now the Global Financial Services Industry Strategist for Teradata.

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