AI guardrails: Building trust in fintech’s next frontier

By now, you've likely picked a side: either you're in the 'experiment fast and break things' camp, racing to embed AI everywhere, or you've grown skeptical - convinced it's overhyped after months of breathless pace. Yet in nearly every leadership conversation I'm in, the question inevitably circles back to the same place: "Can we use AI to do this?"

The reality sits somewhere between the two. According to McKinsey, 65% of organizations have adopted AI in at least one business function - nearly doubling from just a year ago. Yet only a third of those firms said they're actively working to mitigate cybersecurity risks - a decline from last year.

That contradiction says everything about where we are with AI: businesses are charging forward, but too many are leaving their defenses behind. The irony, as McKinsey notes, is that the companies that do invest in risk management also see the highest returns on their AI investments.

In financial services - where trust is foundational - the lesson cannot be understated. And for those who don't heed it, deploying AI without policies and governance carries existential risks. 

Where AI is already touching fintech 

Today, AI is a force multiplier, touching everything from onboarding and know-your-customer (KYC), where verification happens in minutes, to money laundering and fraud detection patterns, where blind spots may have previously existed. Across the broader financial services industry, it is reshaping credit risk, refining assessments in real time, and transforming customer support by scaling service without sacrificing quality.

Although the gains are obvious, the flip side is just as apparent. Each of these touchpoints deals with highly sensitive data, easily becoming a liability if it isn't bound by robust, thoughtful controls. What looks like innovation one day can quickly turn into a reputational disaster the next if trust isn't built in. Already, AI is a major source of bank fraud, costing them billions of dollars each year.

The real goal of AI in fintech shouldn't be automation for its own sake. Nor should it be about ticking boxes on a pilot checklist. The actual prize lies in using AI for two things: automation with tangible benefits and augmentation that positively impacts the workforce. 

On the automation front, AI can take over the repetitive, time-consuming work that clogs up teams. Think of agents that can handle routine onboarding, fraud-detection, or customer support tasks in seconds, saving hours of manual effort. And analysts that can query complex databases on demand, turning raw data into usable insights without needing an entire analytics team on call. Each of these frees people to focus on judgment, strategy, and creativity.

Then there's augmentation to elevate human talent. That starts with building fluency and ensuring that employees, from the day they're hired, are comfortable working alongside AI. Equally, it is about empowerment and giving teams access to tools that make them more productive. 

Why guardrails matter

But all of this only works when there are practical guardrails - policies, governance, structures and oversight that ensures that AI is as trustworthy as it is powerful. 

Guardrails are not one-off documents filed away in a compliance binder. They're living frameworks. They spell out what data can and cannot be used. They define how decisions are logged and audited. And they make clear what happens when something goes wrong.

One IBM study found that 13% of organisations reported breaches of AI models or applications. Of those affected, a staggering 97% had no access controls in place. 

I've seen this play out up close. In one proof-of-concept we ran to automate onboarding and KYC processes, the efficiency upside was obvious. But the harder question wasn't how much effort we saved - it was what risks may occur if we placed decision making trust to AI's potential hallucinations. That's why we built a multi-tier compliance agent that can verify the work of the AI agent, pairing it with human-in-the-loop review to expose weaknesses. The point isn't to avoid automation, but rather to make it safe. 

In financial services, guardrails need to cover several layers at once. They have to filter out harmful or biased output that could lead to discriminatory loan advice or misleading wealth guidance. They must be able to spot hallucinations - the "right sounding" but factually incorrect content that AI can generate - because even small errors can cause a breach. 

Beyond that, guardrails need to validate results against hard criteria, such as only verified know-your-customer (KYC) documents and robust personal data protections. Finally, all output must be aligned with brand and customer expectations, avoiding the drift into irrelevant, confusing, or tone-deaf responses that can quietly chip away at trust.

Looking ahead

The temptation in fintech is to treat AI like a race, with speed as the only metric that matters. In my opinion, the winners won't be the firms that deploy first. They'll be the ones that deploy responsibly, with trust at the center. That means embedding compliance into every AI use case, treating cyber risk as inseparable from business risk, and training employees to not only use AI but to use it responsibly.