Detecting Intent: The Future of Fraud Prevention in an Agentic World

For years, fraud systems have been built on one simple assumption…

There’s a human at the other end.

They track how we type, swipe, and hesitate. They build trust over time through familiar devices and patterns of behaviour, location and financial activity. But that assumption is starting to break, because increasingly, there isn’t a human at the other end at all.

Traditionally, that was the strongest indicator of fraud. For years, fraudsters scaled their operations by automating attacks; using bots, scripts, and more recently, AI. to mimic human behaviour at scale. Now, those same signals are appearing in legitimate customer journeys as consumers delegate everyday actions like shopping, booking travel, and making payments to AI agents that act on their behalf.

And that’s where things get messy.

The Rise of the Digital Delegate

Autonomous AI assistants are becoming part of daily digital life; tools that can browse, fill forms, and complete checkouts without the user ever touching a keyboard.

For financial institutions and merchants, that convenience introduces a new challenge.

Fraud systems built to recognise human input now must interpret activity generated by machines.

That shift blurs many of the signals that fraud detection depends on:

• Behavioural biometrics become less reliable, because AI doesn’t pause or hesitate.
• Device fingerprinting loses accuracy, as transactions originate from cloud infrastructure rather than a known customer device.
• Velocity and session checks begin to show the same smooth patterns whether the activity is legitimate or malicious.

And when the activity is delegated to an agent running outside the user’s environment, in-session authentication becomes impossible. There is no biometric prompt, no challenge screen, and no trusted device context to confirm who is truly acting. Traditional step-ups can’t be triggered because the human isn’t in the loop.

The result is a widening blind spot; not because systems stop working, but because they’re now observing the wrong kind of behaviour.

When Fraud Looks Legitimate

As digital interactions become more automated, the boundary between authorised and unauthorised actions is starting to blur.

Fraudsters are learning to exploit legitimate automation rather than break it, blending their activities into the same channels customers use. At the same time, genuine users are approving transactions that they never personally initiate.

That creates a new question for fraud teams: not “is this a human?”, but “is this my customer’s intent?”

Understanding intent, and whether a decision truly belongs to the user, will define the next era of fraud prevention.

From Behaviour to Intent

Where behaviour tells us how a person acts, intent reveals why.

Modern fraud strategies need to understand that distinction. A genuine AI-driven transaction may follow every technical rule of a secure session, but if the intent behind it has been hijacked, trust is still broken.

Future fraud detection must evolve to assess:

1. Identity and Provenance – verifying who or what is initiating the action.
2. Intent and Consent – determining whether the customer authorised it.
3. Context and Continuity – connecting each event to the broader behavioural and transactional story.

It’s no longer enough to detect the actor. We must understand the intention behind the act.

Redefining Trust in a Machine-Mediated World

At Outseer, we’re exploring how to detect authentic intent, not just human behaviour, by combining:

• Device intelligence that identifies the origin and trustworthiness of each session.
• Behavioural analytics that learn from the customer’s evolving digital patterns.
• Adaptive risk context that interprets signals in real time through our advanced risk engine.
• Payment intelligence that analyses both incoming and outgoing transaction patterns to spot subtle deviations that indicate manipulation or automation; mapping how funds flow, how often, and to whom.

By correlating these payment signals with behavioural and device data, Outseer can distinguish between genuine customer activity and automated actions disguised as routine transactions.

This approach ensures trust can persist, even when digital assistants or delegated systems perform actions on behalf of users.

A New Definition of Consent

Emerging regulation is already beginning to account for this shift.

Under PSD3 and the upcoming EU Payment Services Regulation, new technical standards will define how consent, authentication, and liability are handled when services act on a user’s behalf.

Meanwhile, eIDAS 2.0 and the European Digital Identity Wallet will give citizens verified credentials that can authorise payments, sign documents, and authenticate identity across platforms. The UK is now following suit with the recently announced Digital ID scheme.

We can already see early examples of delegated trust in Open Banking, where a customer gives a licensed fintech permission to access their account or initiate payments. In those journeys, the bank never sees the customer directly; it relies on a secure consent token passed through the regulated ecosystem. The same principle could extend to AI-driven agents acting on behalf of users, demanding equally strong proof of consent and liability boundaries.

These frameworks will lay the groundwork for trusted delegation; creating a provable consent trail that travels with every digital action.

The question for the industry will be: When AI acts on a user’s behalf, how do we prove that consent was truly informed and deliberate?

Preparing for the Agent Economy

Analysts predict that by the end of this decade, more than half of digital interactions will be automated or assisted by AI.

If even a small share of those involves payments or sensitive data, the implications for fraud detection are profound; not just for stopping fraud, but for keeping genuine transactions flowing.

When risk systems fail to recognise legitimate AI-assisted actions, the impact cascades quickly: false declines rise, call centres flood with complaints, and customers miss out on purchases they genuinely intended to make. For merchants, that means lost sales, damaged trust, and abandoned carts at unprecedented scale.

The path forward is clear:

• Anchor trust at the level of identity and intent.
• Design risk engines that adapt to delegated actions.
• Ensure consent remains verifiable, even when automated.

Fraud prevention has always been about understanding people. Now, it’s about understanding the relationship between people’s intentions, and the machines that act for them.

Because the future of fraud detection isn’t about the signals alone: behaviour, biometrics, devices, location, payments, money movement. Understanding fraud, and protecting genuine customers, means being able to ask why this action happened, and who it truly represents.

It’s part of a broader Outseer vision: to build a platformized approach that addresses the evolving challenges of fraud prevention. The platform unifies native signals that work in concert to reveal the true context of each transaction, combines AI-driven risk scoring with advanced science in its decisioning layers, and helps you stay ahead of major shifts such as the rise of agentic AI.

To learn more about the future of fraud prevention, join the Outseer Connect ’25 virtual event on 13 November.