Designing Trusted, Personalized Guidance for Digital Banking

Personalization can make digital banking more useful—but financial guidance is not like a product recommendation or a content suggestion.

When a bank uses a customer’s financial information to offer advice, the experience must earn trust. The advice has to be relevant, timely, understandable, and appropriate to the customer’s situation. It must also avoid the opposite failure: feeling invasive, presumptuous, or overly automated.

My work with BBVA combined two related challenges:

Create a modular online-banking design system that could serve common customer needs across 11 markets while allowing for local variation.
Explore how personalized, AI-enabled guidance could help customers make better financial decisions without undermining trust in the bank or creating concern around the use of sensitive financial data.

The result was a product direction that treated personalization as a design and trust problem—not simply a data or technology feature.

The challenge

BBVA’s banks in 11 countries faced similar digital-banking needs, but each market had its own product requirements, customer expectations, and local operating realities.

The immediate design challenge was to create a modular online-banking system that could meet approximately 80% of shared user needs while enabling local teams to adapt the remaining 20%.

At the same time, the organization was exploring the potential for more personalized financial guidance. The question was not whether a system could analyze banking data. The real questions were:

Would customers trust the bank to use their financial information in this way?
What types of advice would feel helpful rather than intrusive?
Which moments in a customer’s journey created a legitimate reason to offer guidance?
How could the experience communicate why a recommendation was being made?
Where should the product support a decision, and where should it avoid overstepping?

These questions were especially important because banking information is deeply personal, and advice can influence consequential customer decisions.

My role

I led work across customer research, UX design, systems design, prototyping, and strategic narrative.

My responsibilities included:

Guiding a modular online-banking design system for use across 11 BBVA markets
Facilitating research into customer trust, expectations, and concerns around personalized financial advice
Exploring which types of guidance customers considered relevant and credible
Identifying the moments when customers would be most receptive to advice
Translating research findings into product concepts, interaction patterns, and design-system implications
Helping align teams around a common approach to scalable customer experiences and localized implementation
Supporting the transition from vision and prototype work into product-development decisions

The opportunity

Traditional online banking often presents customers with balances, transactions, account details, and static product information. It gives them access to data but leaves much of the interpretation and decision-making to the customer.

The opportunity was to make the banking experience more useful by helping customers understand:

What has changed in their financial situation
What action may be useful or urgent
Whether they are progressing toward a goal
Where they may be at risk of a fee, missed payment, low balance, or other avoidable issue
Which financial products or behaviors might better support their stated priorities

The design challenge was to turn personal financial data into relevant guidance while retaining customer agency and trust.

Researching trust in AI-driven advice

Customer research focused on the emotional and practical conditions that determine whether personalized guidance is welcome.

The finding was not that customers wanted constant advice. They wanted help that felt earned.

Customers were more likely to trust guidance when it was:

Clearly tied to information they recognized and understood
Relevant to a current goal, recent behavior, or imminent decision
Specific enough to be useful, but not so directive that it felt controlling
Transparent about why it was being presented
Easy to ignore, dismiss, or explore further
Framed as support for the customer’s decision—not a demand or sales pitch
Consistent with the bank’s role as a trusted steward of sensitive information

Customers were less likely to welcome guidance when it appeared disconnected from their immediate needs, surfaced without explanation, felt overly promotional, or implied that the bank was monitoring every detail of their financial life.

That research reframed the work. Personalized advice had to be designed as a permission-based, contextual experience.

Defining appropriate advice

Not all advice carries the same level of sensitivity or risk. The team explored a range of guidance types and the conditions under which each would be appropriate.

Type of guidance	Example customer value	Appropriate conditions
Awareness	“Your balance is lower than usual for this point in the month.”	When the signal is clear, timely, and based on information the customer can recognize
Prevention	“An upcoming payment may put this account below your preferred balance.”	When there is a concrete, near-term risk and the customer has time to act
Progress	“You are on track toward the savings goal you set.”	When the customer has explicitly established a goal or preference
Education	“Here is how this transaction category affects your monthly spending picture.”	When customers need context, not a directive
Recommendation	“Based on your stated goal, you may want to explore this savings option.”	When the recommendation is relevant, explainable, optional, and clearly distinguished from impartial guidance
Escalation	“Would you like to speak with an advisor?”	When the decision is complex, high-value, emotionally sensitive, or outside the boundaries of automated guidance

This framework helped distinguish assistance from intrusion. It also provided a practical basis for deciding what should be automated, what should be explainable, and what should remain a human-advisor conversation.

Designing for the right moment

Personalized guidance is only useful when it arrives at a moment when the customer can understand and act on it.

The work examined several moments where advice could have a legitimate role:

When a customer logs in and a meaningful change requires attention
During a transaction or payment flow, before an avoidable consequence occurs
After a pattern becomes clear enough to support a useful observation
When a customer is reviewing spending, savings, or progress toward an explicit goal
When a customer has missed a payment, encountered an unexpected event, or needs help recovering
When a customer asks a question or expresses an intent that signals they want help

The key was not to make the dashboard louder. It was to make the advice more situational.

For example, a generic prompt such as “Explore ways to save more” may be easy to dismiss. A contextual message such as “Your utility payment is scheduled tomorrow, and the available balance in this account is below the amount you typically maintain” provides a clear reason for appearing and gives the customer an opportunity to act.

Product design principles

The exploration established several principles for trusted financial guidance.

Advice must be explainable

Customers should understand the basis for a recommendation. If a product cannot communicate why it is surfacing a message, it should question whether the message belongs in the experience.

Timing is part of the product

Even helpful advice can become irritating or alarming when it appears at the wrong moment. Guidance should be connected to a decision, a meaningful change, a stated goal, or an opportunity to prevent a negative outcome.

Customer control protects trust

Customers need the ability to explore, defer, dismiss, adjust preferences, or seek human support. Personalization should feel like assistance, not surveillance.

Guidance and sales must remain distinct

A product recommendation may be appropriate, but it should not masquerade as neutral financial advice. The experience should clearly distinguish educational support, risk alerts, personalized insights, and commercial offers.

The system must support consistency at scale

If personalized guidance is introduced across markets, the underlying patterns for explanation, timing, escalation, preference management, and user control need to be coherent. The modular system made it possible to define those patterns centrally while allowing local teams to adapt them responsibly.

Building a scalable system

The broader design-system work created a common foundation for digital banking across BBVA’s country operations.

The system was designed to support around 80% of shared user needs across 11 markets, while allowing local teams to address the remaining 20% based on market-specific requirements.

This approach was especially valuable for personalization. A bank can only introduce trusted guidance at scale if core interaction patterns remain consistent:

How an insight appears
How customers understand its basis
How a recommendation links to an action
How customers manage preferences
How exceptions and higher-risk scenarios are handled
When the experience escalates to a human advisor
How local teams adapt language, products, regulations, and market conditions without undermining the underlying customer experience

The design system was not simply a library of interface components. It was a way to create shared standards for decision-making, implementation, and customer trust.

The outcome

The BBVA system work improved the organization’s ability to move product ideas into implementation across markets.

During a roadshow, BBVA Mexico identified a feature it had been unable to implement for 18 months. The modular approach resolved the challenge, leading Mexico to adopt the broader system and launch the feature within three months.

The work also established a more thoughtful basis for personalized banking experiences. Rather than treating AI-driven advice as a generic feature, the concepts emphasized the conditions that make guidance worthy of customer attention:

It addresses a meaningful customer need
It appears at an appropriate moment
It is grounded in understandable information
It respects the sensitivity of financial data
It preserves customer choice
It knows when a human advisor is the better next step

What this work demonstrates

Challenge	Design judgment applied	Result
Shared banking needs across 11 markets	Identified what should be standardized and where local flexibility was essential	A modular system designed to address roughly 80% of common needs
Personalized guidance using sensitive financial data	Researched customer trust, expectations, and boundaries	A trust-centered approach to financial insight and advice
AI-generated or data-driven recommendations	Evaluated relevance, explainability, timing, customer control, and appropriate escalation	A framework for helpful guidance rather than intrusive automation
Financial advice can have real consequences	Distinguished low-risk awareness from higher-stakes recommendations	Clearer boundaries for automation, education, product offers, and human support
A feature stalled for 18 months in Mexico	Applied the modular system to a concrete local need	Feature launched within three months after adoption of the system

The core lesson

In financial services, personalization is not valuable because a system can analyze data. It is valuable only when customers believe the guidance is relevant, understandable, respectful, and in their interest.

The goal was not to make online banking more automated.

It was to make it more trustworthy, more useful, and better able to help customers act with confidence.