How Wells Fargo builds responsible artificial intelligence

“With great power, there must also come great responsibility,” isn’t just a Spider-Man catchphrase. It’s how companies like Wells Fargo are approaching emerging artificial intelligence technology.

The rise of AI is making daily tasks like banking easier, but when it’s not used responsibly, these new tools rightfully create suspicion. Responsible AI can change that.

“Our responsibility to our customers is that we are fully transparent, we protect their data and their money, we give the best advice based on what we know, and we don’t cause harm,” said Chintan Mehta, Wells Fargo’s chief information officer and head of Digital Technology & Innovation. “For us, responsible AI means AI that satisfies these tenets.”

Tech experts at Wells Fargo build trust and responsibility in AI in four ways: eliminating bias, providing transparency, offering alternatives to customers, and building partnerships designed to research and promote ethical use of AI.

Beating bias in AI

AI is only as good as the data it’s trained on. With limited or untested data, algorithms can learn false assumptions or parrot incorrect or incomplete information.

That’s why Wells Fargo’s technology teams have several methods for validating the data and AI algorithms and systems they use. For example, adverse effect checks compare AI models for testing to identify potential biases, and then developers go back to check and correct algorithms.

Beyond that, independent groups review AI-based products and experiences at multiple development points to make sure they’re ready for customers.

“Even though it’s a heavy lift, this process of testing and validating helps us keep up as new models and new techniques come along,” Mehta said.

AI with clear decision-making

Transparency is built into each stage of AI development at Wells Fargo. For example, knowing how and why AI makes a decision should be clear and traceable. Developers use open-sourced platforms with built-in decision-making transparency and data mapping.

Clear decision-making means Wells Fargo AI models won’t “hallucinate,” a phenomenon typical of large chatbot language models where an AI creates inaccurate, contradictory, or invented information and presents it as factual.

“You never let an AI neural network reach conclusions without knowing why,” Mehta said. “The data we use is extremely explainable and has a lineage that we can track.”

Meeting customers where they are

While AI is designed to make banking easier, it’s not how every customer wants to bank. Wells Fargo offers customers multiple ways to engage, including digital and employee-assisted experiences.

“Wells Fargo offers options for customers to engage using tools and services that are the right fit for them personally,” Mehta said.

Wells Fargo is a founding member of the corporate affiliate program at Stanford University’s Institute for Human-Centered AI, or HAI. The partnership helps the company build better AI through employee education and research. So far, more than 4,000 Wells Fargo employees have taken part in the program.

“The program helps us by connecting with companies that are actually experiencing the problems and potentials of AI in their own businesses,” said James Landay, vice director of HAI and a professor in Stanford’s engineering and computer science departments. “What we mean by human-centered AI … is how do we make our AI serve humanity in a positive way without the negative outcomes?”

Collaboration supporting responsible AI use is increasingly important. Reported AI incidents have risen sharply in recent years, according to Stanford’s AI Index 2026 Annual Report, which cites the open AI Incident Database. In 2025, there were 362 reports of AI causing or nearly causing harm.

“The real dangers of AI are the dangers that are already here,” Landay said.

The aim of Wells Fargo’s transparency and safety infrastructure is to ensure AI helps customers bank more quickly and easily while protecting their information and maintaining their trust.

“With AI, customers are getting faster, more intuitive experiences than they would have otherwise had,” Mehta said. “We’re doing all we can to be thoughtful about how we’re building AI.”