Unlocking the true potential for Agentic AI in Financial Services
Read this guest blog by Keith Little, President of Experian Software Solutions, for techUK’s Tech and Innovation Focus Week 2026.
Financial services are on the cusp of an Agentic AI evolution. We are now at the stage where the implementation of the technology goes far beyond the proof-of-concepts, pilots and limited use cases that have largely characterised its deployment over the last few years.
One factor that will accelerate its development, is genuine public interest in adopting AI agents. Experian's latest research highlights growing openness to AI-assisted purchasing in the UK, with 55% of consumers already using, or open to using, AI tools for online purchases or bookings. This openness is strongest among 25-39-year-olds, with 70% either already using such tools or open to doing so.
More profound however, is the strides being made to deal with the fundamental issue of integration, governance and scaling AI agents to reliably handle more complex, end-to-end workflows. Trusted intelligence will enable agentic AI to scale well beyond pilot deployments.
For the financial services sector, the business case for AI agents is compelling: faster fraud detection, smarter credit decisions, and much more intuitive customer service to name just a few use cases, as well as the scope for implementing AI agents to streamline internal processes as well.
They continue to accelerate investment in AI but face major barriers to adoption. Nearly half of global organisations (48%), say integrating data into AI workflows remains difficult, while a third cite poor data lineage and a further third say data is siloed across teams and systems
The stakes of getting it wrong are especially high given the combination of regulation, risk, and the direct impact on people's financial lives. Decisions around creditworthiness, fraud prevention and compliance require highly accurate, up-to-date and verified data. If the data underpinning an AI agent is incomplete or unreliable, the risks multiply and lead to poor decisions, regulatory breaches and loss of customer trust.
So what does it take to move from “proof-of-concept”? And how is this achieved with a keen focus on governance and compliance? The answer is a better operational framework around the model.
Operationalising AI agents
The biggest obstacle to scaling agentic AI in financial services isn’t necessarily the AI itself. What can often lag behind is everything around the AI – the governance, the explainability, the data quality. That’s where many organisations are hitting walls.
What is now starting to evolve is not simply the capability of the underlying models. It is the maturity of the infrastructure layer around them – the connective tissue that allows AI agents to operate reliably within the complex, regulated environment of financial services.
The emergence of dedicated infrastructure layers that manage how AI models, enterprise data, and business workflows interact are key. These platforms provide the orchestration, security controls, and audit capabilities that large-scale agentic deployment requires. They are the difference between an agent that works in a demonstration and one that can be deployed with confidence in a regulated environment.
Another use case we’re particularly interested in is fraud detection. One of the most immediate and critical applications for agentic AI is in the ongoing battle against financial crime. Criminals are already leveraging AI to launch attacks at much higher volumes and AI can now generate convincing phishing emails and forged documents rapidly and at high volume. Fraudsters are also using AI to create synthetic identities over years, building credible-looking profiles that are designed to disappear after securing significant credit.
With agentic AI, organisations are much better equipped. By continuously analysing vast networks of data in real-time, it can identify subtle, anomalous patterns indicative of bot activity or a coordinated synthetic identity attack. This allows it to move beyond simple rule-based detection to proactively identifying high-volume, automated threats before they result in a loss, making it a crucial component of the modern security stack.
Making trust the architecture, not the afterthought
As AI agents take on more consequential tasks like approving credit, settling claims, flagging fraud and managing portfolios, the question of trust – and trusted data - becomes fundamental.
At its core, it involves creating a secure, verifiable link between a consumer's identity and the AI agent acting in their name, one that travels with the agent through each step of a transaction and generates an auditable record of both identity and intent. In an ecosystem where agents from different platforms will increasingly interact with merchants, payment networks, and financial institutions, this kind of standardised trust layer is key.
The financial services organisations that will capture most value from agentic AI are not necessarily those moving fastest, but those building on foundations strong and secure enough to support genuine scale. That means treating governance, data integrity, and verifiable trust not as prerequisites to be cleared before the real work begins, but as the strategic layer that makes everything else possible.
The technology is advancing quickly, and the direction of travel is clear. The opportunity now is to meet it with the rigour and long-term thinking that the best decision-making in this industry has always demanded.
Experian’s role in the agentic AI ecosystem
Our global platforms are helping define what comes next in agentic AI. By introducing agentic workflows within our cloud-based platform, Ascend, that bring together AI, data, analytics and decisioning, we are enabling clients to automate complex processes in ways that are more intelligent, trusted and scalable.
Our new, multi-year partnership with ServiceNow is another example of how we are extending that impact across enterprise and taking a leading role in shaping the trust layer for agentic commerce through Experian Agent Trust, a new framework designed to verify identity, intent and authorisation in AI-driven transactions.
Author
Keith Little
Keith Little
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Kir Nuthi is the Head of AI and Data at techUK.
She holds over seven years of Government Affairs and Tech Policy experience in the US and UK. Kir previously headed up the regulatory portfolio at a UK advocacy group for tech startups and held various public affairs in US tech policy. All involved policy research and campaigns on competition, artificial intelligence, access to data, and pro-innovation regulation.
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Usman joined techUK in January 2024 as Programme Manager for Artificial Intelligence.
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Prior to joining techUK in January 2015, Sue was responsible for Symantec's Government Relations in the UK and Ireland. Before that, Sue was senior policy advisor at the Confederation of British Industry (CBI). Sue has an BA degree on History and American Studies from Leeds University and a Master’s Degree in International Relations and Diplomacy from the University of Birmingham. Sue is a keen sportswoman and in 2016 achieved a lifelong ambition to swim the English Channel.
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