I think about the early ATMs now and then.

No one knew the “right” way to use them.

I imagine a customer in the 1970s standing there, card in hand, squinting at this unfamiliar machine and hoping it would give something back; trying to decide if it really dispensed cash…or just ate cards for sport. That quick panic when the machine pulled the card in is an early version of the same confusion customers feel today in digital banking.

People were not afraid of machines. They were afraid of not understanding what the machine was doing with their money.

Banks solved it by teaching people how to trust the process. They added clear instructions, trained staff to guide customers, and repeated the same steps until the unfamiliar felt intuitive. 

However the stakes and complexity are much higher now, and AI for financial product transparency is becoming essential to an optimized banking UX.

Today’s banking customer must move through automated underwriting, digital identity checks, algorithmic risk models, hybrid blockchain components, and disclosures written in language most people never use. Meanwhile, the average person is still struggling with basic money concepts.

 FINRA reports that only 37% of U.S. adults can answer four out of five financial literacy questions (FINRA Foundation, 2022).

Pew Research finds that only about half of Americans understand key concepts like inflation and interest (Pew Research Center, 2024).

Financial institutions are starting to realize that clarity is not a content task or a customer service perk. It is structural. 

It affects conversion, compliance, risk, and trust. It shapes the entire digital experience. And AI is accelerating the pressure to treat clarity as infrastructure.

When customers don’t understand, they don’t convert. When they feel unsure, they abandon the flow. 

HOW AI IS IMPROVING UX IN BANKING

(AND WHY INSTITUTIONS NEED IT NOW)

Financial institutions often assume customers will “figure it out.” They will Google a term, reread a disclosure, or call support if something is unclear. In reality, most customers simply exit the flow.

The CFPB shows that lower financial literacy leads to more mistakes, higher confusion, and weaker decision-making (CFPB, 2019). And when that confusion hits during a digital journey, customers leave quietly without resolving their questions.

This means every abandoned application costs money. Every misinterpreted term creates operational drag. Every unclear disclosure becomes a compliance liability. Institutions consistently point to misunderstanding as a major driver of complaints, errors, and churn (Lusardi et al., 2020).

Sometimes it feels like the industry built the digital bank faster than it built the explanation for it.

WHERE AI MAKES THE DIFFERENCE

Many discussions about AI in financial services focus on automation or chatbots, but the real opportunity lies in real-time clarity. Clarity that improves financial product transparency and streamlines customer experience without creating extra steps.

In-context explanations that improve understanding:

Research in educational psychology shows people learn best when information appears the moment they need it. Mayer (2019) demonstrates that in-context explanations significantly boost comprehension. Instead of leaving the app to search unfamiliar terms, customers receive a clear, human explanation on the spot.

Consistency across channels:

Language in banking is surprisingly inconsistent. Apps, websites, advisors, and support teams all use slightly different terms. Capgemini identifies cross-channel inconsistency as a major cause of digital frustration (Capgemini, 2023). A unified AI knowledge layer solves this by standardizing definitions everywhere.

Predictive clarity powered by behavioral insight:

Patterns like hesitation, backtracking, rapid clicking, or form abandonment often signal confusion. Behavioral economists note these patterns can predict drop-off before it happens (Loibl et al., 2021). AI can flag these friction points and help institutions fix them.

24/7 clarity. Not 9–5 support:

Accenture reports that most digital banking interactions now occur outside of business hours (Accenture, 2023). AI allows institutions to provide accurate, transparent explanations anytime, without relying solely on support teams.

At its core, AI doesn’t simplify financial products. It translates them.

WHAT STRONG AI-POWERED CUSTOMER EXPERIENCE LOOKS LIKE

Onboarding that explains itself:
Mortgage flows with one-sentence escrow definitions.
Credit card applications with visual utilization explanations.
Hybrid products that show exactly what blockchain is doing behind the scenes. The CFPB shows that simpler, clearer formats directly improve decision quality (CFPB, 2020).

A unified dictionary across channels:
The Federal Reserve emphasizes the importance of consistent terminology to help consumers make informed decisions (Federal Reserve Board, 2021). Some institutions now maintain a centralized term library that powers their entire ecosystem, creating a cohesive experience instead of fragmented messaging.

Personalization based on user behavior:
Educational nudges, simplified paths, multilingual explanations. Research shows these interventions boost customer confidence (Kozup & Hogarth, 2008). 

Transparent explanations for hybrid or blockchain-backed products:
Customers adopt new technology faster when they understand the mechanics behind it (University of Cambridge, 2021). AI can make complex automation and decentralized components understandable.

THE URGENT RESPONSIBILITIES THAT COME WITH THIS

GenAI can mislead customers without strong data governance and oversight. Poor training data, inconsistent terminology, or unmonitored AI systems create clarity gaps. That’s a problem because those gaps can become compliance issues. The Financial Stability Oversight Council warns that unmanaged AI introduces systemic risk (FSOC, 2023). The CFPB also emphasizes the need for compliant, accurate AI-generated content (CFPB, 2024).

Customers are also increasingly wary of data usage and privacy. Pew Research shows growing fear around how financial institutions use personal data (Pew Research Center, 2023). Trust requires transparency.

Clarity without governance is not clarity. It’s noise.

And institutions cannot afford noise.

WHAT INSTITUTIONS SHOULD BUILD RIGHT NOW

To make clarity foundational to customer experience, financial institutions need to invest in:

• Modern data pipelines to improve accuracy
• Consistent terminology and UX layers across channels
• Responsible AI frameworks with human oversight
• Cross-functional collaboration between compliance, design, product, and analytics
• Scalable architecture for automated and decentralized product components
• Human-plus-AI support models that enhance, not replace, advisors

When clarity becomes structural, trust becomes scalable.

WHY THIS MOMENT MATTERS

I keep coming back to the ATM because it perfectly shows what happens when technology outruns customer understanding. The machine wasn’t the problem. The knowledge gap was. Financial services are reliving that moment today. 

Customers cannot trust what they do not understand.

And institutions cannot scale what customers do not trust.

GenAI gives financial organizations a second chance to rebuild the clarity layer the industry has lacked for decades; and not as marketing. Clarity, in this new landscape, truly is infrastructure.

Related Reading

• The Personalization Gap Is Hurting Financial Services, Here’s How to Close It a discussion of how personalization shortfalls are holding back financial services from meeting customer expectations. 

• Closing the Gap Between Expectations and Experience in Financial Services analysis of rising customer expectations and why legacy systems struggle to keep up. 

• Trust Is the New Currency in Financial Services and Customers Are Setting the Terms  a look at how trust, speed, transparency, and control are reshaping what customers expect from their institutions. 

References 

Accenture. (2023). Banking top trends 2023. https://www.accenture.com

Capgemini. (2023). World retail banking report 2023. https://www.capgemini.com

Consumer Financial Protection Bureau. (2019). Financial well-being in America. https://www.consumerfinance.gov

Consumer Financial Protection Bureau. (2020). Improving the clarity of mortgage disclosures. https://www.consumerfinance.gov

Consumer Financial Protection Bureau. (2024). Supervisory highlights: Issue 30. https://www.consumerfinance.gov

Federal Reserve Board. (2021). Consumers and mobile financial services. https://www.federalreserve.gov

FINRA Investor Education Foundation. (2022). National financial capability study. https://www.finrafoundation.org

Financial Stability Oversight Council. (2023). Annual report. https://home.treasury.gov

Kozup, J., & Hogarth, J. (2008). Financial literacy, public policy, and consumers’ self-protection. Journal of Consumer Affairs, 42(2), 263–270.

Loibl, C., Grinstein-Weiss, M., & Koeninger, J. (2021). Consumer financial behavior in digital environments. Journal of Economic Psychology, 87, 102438.

Lusardi, A., Mitchell, O. S., & Oggero, N. (2020). The changing face of financial literacy. University of Pennsylvania, Wharton School.

Mayer, R. (2019). The Cambridge handbook of multimedia learning. Cambridge University Press.

Pew Research Center. (2023). Americans and data privacy. https://www.pewresearch.org

Pew Research Center. (2024). Americans and financial knowledge. https://www.pewresearch.org

University of Cambridge. (2021). Global blockchain benchmarking study. https://www.jbs.cam.ac.uk