By Andy O’Dower, Vice President of Product Management for Voice & Video at Twilio.
FinTech moves fast. News is everywhere, clarity isn’t.
FinTech Weekly delivers the key stories and events in one place.
Click Here to Subscribe to FinTech Weekly's Newsletter
Read by executives at JP Morgan, Coinbase, BlackRock, Klarna and more.
In the race to modernize customer service, the industry has hit a dangerous blind spot. According to recent data, 90% of businesses believe their customers are satisfied with their AI interactions, yet only 59% of consumers agree.
In retail, that gap might cost you a sale. In Fintech, where trust is the currency of the realm, that gap costs you the customer.
As banking and insurance leaders rush to deploy Voice AI, many are falling into the trap of prioritizing conversational metrics — how natural the voice sounds or how well it mimics small talk in the lead up to a transaction. But for the customer trying to freeze a stolen credit card or check a pending transfer, personality is a distant second priority to performance.
The Currency of Resolution
The data is unequivocal: consumers are not anti-AI; they are anti-friction. In fact, more than two-thirds of consumers say they would actually prefer to use an AI agent if it fully solved their issue faster than a human.
This is the green light for Fintech CIOs. Your customers are giving you permission to automate, but with a caveat: it has to work. Half of all consumers who are dissatisfied with AI cite the simple fact that the agent "didn’t resolve their issue" as the primary reason.
For financial institutions, this means the metric for success shouldn't be containment rate (keeping people away from humans); it should be time to resolution. If your AI sounds like a human but takes three minutes to fail at checking a balance, you haven't innovated; you've just automated frustration.
Building the Hybrid Frontline
So how do you close the perception gap?
Instead of trying to overhaul your entire contact center with a black-box LLM, identify the primitive use cases that are high-volume and low-risk. In banking, this might be account verification, transaction history, or bill pay. These are the tasks where an AI agent, powered by real-time data pipelines, can outperform a human in speed and accuracy. To truly future-proof these efforts, organizations must utilize an integrated, flexible voice AI tech stack that layers onto existing systems, allowing you to swap models and adjust workflows as the technology evolves.
For complex, high-empathy moments like a mortgage application or a fraud dispute, the AI should serve as a bridge, not a barrier. It should gather the context and seamlessly transfer the customer to a human agent who has the full history on their screen before they even say hello.
Trust Through Transparency
Finally, in an industry built on security, robust verification and transparency are non-negotiable. Implementing voice AI demands robust verification measures that are woven into the fabric of the interaction to safeguard sensitive financial data. We expect regulatory pressure to increase, potentially requiring distinct disclosures when a customer is speaking to an AI.
Fintech leaders should embrace this. When an AI agent clearly identifies itself and then immediately demonstrates value — "I’m an AI assistant. I see you’re calling about the transaction at Target. Do you want to approve that?" — it builds more trust than a bot pretending to be "Sherri from the branch".
The technology is ready. The customers are willing. But to close the gap, we have to stop trying to trick them into thinking they're talking to a person, and start proving to them that they're talking to a solution.
About the author
Andy O'Dower is the Vice President of Product Management for Voice & Video at Twilio, where he leads product strategy and management to assist customers in building innovative customer engagement solutions.
He has over 20 years of experience in founding and scaling platforms in B2B, B2C, and platform API products. Throughout his career, he has built and led large cross functional teams, creating and scaling profitable software and platforms with hundreds of millions in revenue and millions of users. His experience includes working with startups like Curiosity and Snapsheet to Wowza video streaming. He holds an MBA from Rockhurst University and is based in Evergreen, CO.
