Insights
Investing in AI governance isn’t keeping financial services agents live
Customer communications in financial services run under constant regulatory pressure. Every message, from routine payment confirmations to fraud alerts, handles sensitive customer data. All it takes is one bad interaction to unravel years of hard-earned customer trust. As AI takes on more of the customer relationship, the cost of getting it wrong gets higher.
Sinch’s new report, the AI Production Paradox, surveyed over 500 financial leaders across 10 regions to understand the ambitions behind their AI programs, and what’s holding them back.
Financial services is confidently deploying AI, and consumer trust is what’s on the line
Financial services is a complex environment to introduce AI into. For consumers, every financial interaction carries a degree of caution, and for good reason. Financial fraud is an everyday reality for millions of people.
The FTC’s 2025 consumer protection data shows nearly $1 billion in reported losses to business impersonators, with bank impersonators accounting for the highest losses. Juniper even warned about a tidal wave of impersonation fraud in the coming years.
In this environment, trust is earned through decades of reliability, strict regulatory compliance, and accountability. It’s no surprise, then, that almost a third of finance leaders cite compliance and legal concerns as the number one obstacle to AI deployment.
Despite this, Sinch research shows 61% of financial services organizations have gone live with AI communications agents and, 52% say they’re very confident in their readiness to deploy at scale.
of financial services organizations have deployed AI communications agents.
of financial services organizations are very confident in their AI readiness.
The industry is deploying AI through the complexity, and with confidence, because the potential rewards are worth it. While customer support is a priority for 50% of financial institutions, the industry isn’t stopping there. It’s pushing AI into the highest-stakes communications, where customer trust and security are on the line. Fraud prevention is the second most important AI goal at 43%, higher than any other industry. Identity verification follows closely behind at 33%.
Which areas of customer communication is your organization prioritizing for AI agent deployment? (Select up to three.)
Sinch research (2026) shows the financial services industry is deploying AI agents for customer service and support (50%), fraud prevention and security (43%), and identity verification and authentication (33%)
When a suspicious transaction gets flagged, an AI agent can reach the customer in seconds, confirm if it was them, and resolve it quickly before any permanent damage is done. In fact, in many cases AI-based fraud prevention will even stop the fraud before it actually happens, blocking certain actions before they impact the customer.
Customer trust hinges on how well AI agents handle these situations and the customer interactions that follow, and research shows this hard-earned trust is being put to the test.
Even the most governed AI programs are rolling back
In financial services, the stakes are high for every AI communication. That’s no surprise, since every interaction touches on customers’ money, data, and the trust they’ve placed in the organization holding them.
Financial institutions have responded the way you’d expect from a heavily regulated industry: by investing in governance. And to some extent, the investment is paying off. Our research found that each step up in guardrail maturity makes FinServ organizations twice as likely to be operating at the most advanced deployment level.
So, contrary to what the market had assumed, compliance complexity and governance aren’t holding companies back from deploying AI agent. But they’re not preventing rollbacks either. Our data shows 69% of the industry’s AI agents have been pulled back due to a governance failure, just five points below the global rollback rate average. And that number is higher with the most governed organizations.
“The industry has assumed that better governance leads to better outcomes. But that’s not enough: if governance was the fix, the most mature teams would roll back less, not more.”
Lower rollback rates aren’t necessarily an indication of an organization’s AI success. They could very well be a sign of poor monitoring. But when seven in ten organizations that have deployed an AI agent have had to roll something back, that points to a deeper issue the industry still hasn’t been able to fix.
In finance, every AI failure is a trust failure
When a financial services AI interaction fails, it puts consumers at risk and damages the relationship they’ve built with the company over years.
It’s an AI agent that doesn’t send the fraud alert it was specifically deployed to trigger. Or one that surfaces account details to the wrong person, or that confidently states the wrong balance or mortgage rate. Customer data exposure and hallucinations are the two leading causes of AI agent rollbacks, at respectively 27% and 21%.
In FinServ, these failure modes are compliance and trust failures at the same time. That’s why, for financial leaders, the most significant business impact of AI agent failures is reputational damage, ahead of operational strain – the top concern globally. That’s also the one consequence that cannot be fixed.
When an AI agent-driven customer interaction fails due to a technical issue (slow response, lost context, incorrect information), what is the most significant business impact?
Sinch research (2026) shows reputational damage and loss of customer trust outranks customer support load as the primary business impact of AI agent failure
The industry is underfunding the layer trust depends on
Financial services are investing heavily in their most important asset: customer trust.
In fact, trust, security, and compliance is the number #1 spending category in FinServ at 78%, well ahead AI agent development (67%). But that spending isn’t closing the gap between deploying an agent and keeping it live.
Which areas will receive the most investment focus for your AI communication agent deployment in 2026? (Select your top 3 priorities)
That’s because oftentimes, the problem isn’t the AI agent itself. It’s the infrastructure it’s built on. Our research found that communications infrastructure satisfaction is the strongest predictor of AI deployment success – stronger than AI investment level, maturity, or governance sophistication.
Yet, our findings suggest that infrastructure investment still has room to grow in the industry. While 83% of financial institutions agree that high-performing infrastructure is essential or very important to deploying AI safely, only 56% actually prioritize it when investing – five points below the 61% average.
of financial services organizations say high-performance comms infrastructure is essential or very important.
of financial services organizations are prioritizing communications infrastructure and reliability investment.
Despite infrastructure investment lagging slightly in financial services, the industry has started evaluating new providers. 83% of financial institutions in our study are already having active or exploratory conversations with alternative providers. In FinServ, businesses citing a lack of AI integrations are 2.4 times more likely to evaluate a new provider, and those citing poor reliability are 1.4 times more likely.
The AI Production Paradox: See how financial services compares to other industries
In financial services, every fraud alert that doesn’t fire, every balance stated with confidence and stated wrong, has real business impact. But scaling AI communications without risking customer trust goes well beyond governance investment. It comes down to the infrastructure underneath the agent, where compliance is built in and where every interaction either holds trust or breaks it.
The AI Production Paradox dives into insights from 2,527 enterprise leaders to understand where these failures start, what they cost, and why the infrastructure underneath the agent decides who pulls ahead. Learn how FinServ compares and what’s setting the most mature AI programs apart.
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