AI in Financial Services.
Richard Thiessens
EVP, Chief Technology Officer
Have you ever felt simultaneously ahead of the curve and behind it? That’s how I often feel when reflecting on generative AI within the financial services sector. I’ve always been someone who digs into how things work—whether it’s racing engines, hacking, or now, artificial intelligence. I experiment in my home “lab,” playing with open-source large language models and dabbling in supervised training. While I’m far from an AI expert, I consider myself a “big picture” thinker, and I share this background to give context to my perspective.
Although artificial intelligence has been around for decades, the recent surge in attention is focused almost entirely on generative AI. So, when I refer to AI in this article, that’s the lens I’m using.
Cautious Optimism
There’s a palpable tension in our industry—between the fear of missing out and the fear of getting it wrong. AI’s capabilities are undeniably powerful, yet leveraging them responsibly is a real challenge. Most smaller financial institutions can’t afford the time or cost to build AI tools internally, leaving us dependent on third-party vendors. But this dependency comes with significant risks.
Recently, JPMorgan issued a stark open letter warning of the rush to deploy AI without adequate safeguards. Among their concerns:
- 78% of enterprise AI deployments lack proper security protocols
- Most organizations can’t explain how their AI makes decisions
- Security vulnerabilities have tripled since mass AI adoption
Their message was clear: Speed is outpacing security. The letter concluded that only institutions prioritizing security will endure the coming “AI reckoning.”
It reminded me of Jeff Goldblum’s classic line from Jurassic Park:
“Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.”
I thought of that quote soon after ChatGPT launched. People were eager to explore, impressed by its leap beyond voice assistants like Siri or Alexa. But many shared sensitive data with the tool without considering how it might be used or stored. One widely reported case involved a Samsung engineer inadvertently leaking proprietary code to ChatGPT, which then absorbed that information for future interactions.
Fear and Hype
Some people fear what they don’t understand. Others fear AI precisely because they do. This fear—combined with all the hype—has created real obstacles to leveraging AI effectively.
In the banking world, risk management, regulatory compliance, and privacy are part of daily life. That makes leaders quick to shy away from anything with a high perceived risk, especially when the long-term ROI of technology investments can be hard to justify. For years, only the biggest banks could afford to build their own tech. Now, more innovative institutions—regardless of size—are taking the leap.
But building cutting-edge AI in-house is a different beast altogether. The R&D costs and competition are steep. Most financial institutions have to rely on third-party tools, which creates new risks around security, control, and explainability.
Still, it’s easy to see why there’s so much pressure. Forecasts from Goldman Sachs and others estimate AI could contribute $7 trillion to global GDP in the next decade. Just type “AI economic impact” into your search engine of choice—Google, or maybe ChatGPT, Gemini, or Bard—and you’ll find no shortage of staggering predictions.
This leaves banking leaders in a tough spot—especially those leading tech strategy. You can’t afford to be left behind, but you also can’t afford a major security misstep. You know you need to do something, but the how and when aren’t always clear—especially when everyone seems to have a shiny solution to sell.
Human Intelligence
As transformative as AI can be—analyzing data at lightning speed, generating creative content, even deepfaking videos—it is, at its core, just a tool. The real differentiator is not the AI itself, but how humans use it.
We must empower our people to use AI wisely. That starts with education, not just in terms of security protocols but in understanding how to collaborate with AI effectively. After all, the root cause of most cybersecurity breaches? Human error—often despite hours of required training.
The first and most important step is addressing the human element. Better outcomes come from smarter usage. That’s where education & prompt engineering comes in: the more skilled users are at framing questions and guiding the AI, the better the results. Human thought leadership with AI’s capabilities. Humans must first understand how, when and why to use AI.
Balanced Innovation
Some banks are still holding back. Others are enthusiastically showcasing AI tools to peers and racing to adopt them internally. The key is balance. We need to learn from the mistakes of others, conduct thoughtful due diligence, and educate our teams—without falling behind in this AI-driven transformation.
I remain optimistic about AI’s role in financial services. I believe third-party partnerships will lead the charge, but I’m also excited to see what innovation comes from institutions bold enough to build their own tools.
In the end, our greatest asset isn’t artificial intelligence—it’s augmented intelligence, where humans and machines work together, guided by purpose, caution, and vision. Much like this article. I started with my own research, wrote a 1200-word version and then collaborated with AI. I hope you’ve enjoyed the result.
About the Author.
Richard Thiessens is an Executive Vice President and Chief Technology Officer at FinWise Bank. As the Bank’s CTO, Richard identifies, creates, and utilizes various forms of technology to deliver on the Bank’s strategic plans.
He oversees the Bank’s Technology Department and is responsible for Software Engineering, Data Services, Information Systems, Cyber Security, and Project Management.
Richard was interviewed for Independent Banker’s “Out of Office” March 2025 series. He shared one of his favorite hobbies outside the office, making his own wine, cider and mead.
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