ING Senior Assist

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At a glance

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Challenge: Older users can still hesitate in sensitive payment flows, even when the interface itself is technically clear.

My role: Solo UX researcher and designer. I shaped the interaction design, decision logic, AI boundaries, and prototype logic behind the concept.

‍Research: Based on my experience at the ING service desk, I observed that hesitation was often driven less by inability than by uncertainty. Many older users were not looking for speed, but for reassurance before completing sensitive tasks.

What I explored: How service-like guidance, deterministic flow logic, voice support, and carefully bounded AI could make high-risk banking moments feel calmer and more predictable.

Why it matters: Emotional design is not only about empathy. In sensitive banking flows, reassurance can also affect support load, efficiency, and customer independence.

UX methods & deliverables: User journey mapping · Task flow design · Interaction-state model · AI boundary mapping · Figma prototype · Usability testing plan

How I approached the project

I translated real service-point observations into a guided banking flow for older users. The work focused on identifying moments of hesitation, mapping the payment journey, defining predictable interaction states, and prototyping a calm step-by-step experience in Figma.Because this was a concept project, I did not run a full usability test. I included a usability testing plan to show how the flow could be evaluated around clarity, reassurance, and user control.

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Designing confidence in high-risk digital banking

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Digital banking is fast.
But money is emotional.

During three years working at the ING service desk, I supported customers — many of them seniors — who were not struggling with intelligence, but with uncertainty. High-risk actions such as making payments triggered hesitation, repeated verification, and visible anxiety. The interface was not unusable. It felt unsafe. ING Senior Assist explores whether digital banking can feel as calm and supportive as being guided by a service desk representative — specifically for senior users who experience uncertainty in high-risk financial moments.

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The Core Question

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Should critical financial flows rely on adaptive AI personalization —
or on deterministic, predictable system behavior?

Rather than adding more intelligence, the design challenge became:

When should intelligence step back?

The goal was not to make the system feel smarter.
It was to make it feel steadier.

In high-risk financial moments, reassurance depends less on adaptation than on visible consistency. A system that changes too much may feel efficient from a technical perspective, but unstable from a human one. This project began from that tension. It asked whether confidence could be designed through guidance, pacing, and confirmation — while keeping the critical flow itself predictable.

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What I Observed

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Working directly with customers revealed something analytics cannot show: hesitation.

Not confusion.
Not inability.

Hesitation.

Some users read every word before tapping.
Some verified amounts three or four times.

Most of them only felt confident continuing once the steps were clearly guided and the right buttons were pointed out after they explained what they wanted to do.

The interface was functioning correctly.
But many users still paused.

They weren’t looking for speed.
They were looking for reassurance. In high-risk financial moments, they needed visible confirmation — that the amount was correct, the account was correct, and nothing would happen without their clear intent. More than anything, they needed the steady guidance a service desk representative provides.

Over time, patterns emerged:

• Hesitant but compliant
• Anxious and verification-seeking
• Confident but easily derailed

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These patterns did not point toward a lack of ability.
They pointed toward a mismatch between what the interface provided and what the moment required.

The existing flows were built for completion.
What these users often needed was confirmation.

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What I owned

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I owned the concept and interaction design end to end: from identifying the user problem to shaping the flow logic, interface behavior, and prototype.

The design had to balance reassurance, accessibility, and feasibility in a sensitive financial context. AI was included only where it could support the interaction without taking control away from the user.

This meant designing not only screens, but also the rules behind the experience:

• What should remain fixed
• What should respond
• What should be highlighted
• What should wait

The work was therefore as much about defining the system’s boundaries as it was about shaping the interface.

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Design decisions

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Every design decision was grounded in observed behaviour, not assumption.
In this flow, reassurance mattered more than speed.

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This helped turn the concept from a screen-level redesign into a more structured interaction model. The goal was not to make the flow feel faster, but to make each step feel understandable, predictable, and safe.

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Research Approach

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This project is grounded in field exposure.

Over three years at the service desk, I supported real customers navigating real financial actions. Due to privacy regulations, no customer data was recorded or stored. Insights were derived from behavioral observation and direct conversation. This was complemented by heuristic evaluation of existing payment flows, informal interviews about emotional experience, and simulation experiments in Google Colab. The goal of the simulation was not to optimize prediction. It was to evaluate predictability.

This distinction mattered.

The question was not whether the system could become better at guessing what a user might need next. The question was whether that kind of adaptive behavior would make high-risk moments feel less stable. In this context, perceived consistency was more important than optimization.

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Why AI can help — and where it should stop

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AI is useful in this concept, but only in carefully bounded ways.

It can help understand spoken input, reduce friction for users who find typing or navigation stressful, and support a more natural interaction in moments where reassurance matters. It can also support guidance cues and contextual reassurance — not by changing the flow, but by helping the system respond in a way that feels attentive, calm, and easy to follow. This is where AI adds value: not by taking control, but by making support more accessible inside the flow itself.

That also made the boundary clear.

AI should not reorder the critical flow.
It should not auto-advance steps.
It should not make payment decisions.
And it should not adapt confirmation logic in ways that reduce predictability.

In this concept, AI assists.
It does not decide.

That boundary was important to me because it reflects how I think about AI in product design. The question is not only what AI can do. The question is where its flexibility supports trust, and where that same flexibility starts to undermine it.

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Transferable principle for financial AI

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The AI layer was designed to support the user, not act on their behalf. It could help with input, guidance, and checking, but the final decision stayed visible and explicit.

This same principle could apply to other financial tasks such as invoice preparation, account changes, or categorisation: AI can reduce effort, but it should not reduce user control.

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Google Colab Simulation

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To evaluate whether AI-driven personalization would improve or undermine confidence, I simulated senior interaction profiles using anonymized and synthetic behavioral assumptions. I compared deterministic decision logic with probabilistic ML adaptation.

I wasn’t testing how accurately the system could predict behavior.
I was testing whether adaptive behavior would make the interface feel less stable. Because no real customer data could be used, these behavioral profiles were generated using ChatGPT and structured into simulation-ready variables. The synthetic data was then modeled in Google Colab to compare deterministic decision logic with probabilistic machine learning adaptation.

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In financial contexts, stability is not technical uptime.
It is psychological consistency.

Small variations increase cognitive monitoring during high-risk actions. When users must “check the system,” trust drops. To make this difference visible, I mapped both approaches across three dimensions:

• Step order stability
• Confirmation consistency
• Visual emphasis variation

The goal was not optimization.
It was predictability.

This helped clarify an important design principle:

In sensitive financial flows, it is not enough for the system to work.
It also has to behave in a way that feels dependable from one moment to the next.

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Decision Logic vs. Machine Learning

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Machine learning can optimize flows.
It can reduce friction.
It can personalize.

But it also introduces variation.

In a payment context, variation becomes uncertainty.

For digitally confident users, adaptive shifts may feel efficient.
For anxious users in high-risk moments, they feel unstable.

In regulated banking environments, explainability matters.
Predictability matters more than personalization.

The decision was deliberate:

• Critical payment steps use deterministic, human-designed decision logic
• AI is limited to supportive roles — voice interaction, guidance cues, contextual reassurance

AI assists.
It does not decide.

The payment logic remains fixed.
The sequence remains fixed.
The confirmations remain fixed.

AI is present only where it can support the user without destabilizing the flow. It helps interpret spoken input. It supports guidance. But it does not change the structure of the system underneath. In a context where trust is tied to consistency, restraint became a design decision.

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Why a Separate Experience

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Many accessibility improvements benefit everyone.
ING Senior Assist explores a different approach: intentional segmentation.

The slower pacing, repeated confirmations, and persistent visual guidance are designed for users who value reassurance over speed.

For younger users prioritizing efficiency, this experience would feel slow.

Creating a dedicated flow allows both groups to receive an interface aligned with their expectations — without compromising either. This is not simplification. It is intentional pacing.

The design does not try to create one ideal experience for everyone.

Instead, it acknowledges that different users can need different kinds of support in the same task. What feels smooth to one person can feel rushed to another. A separate experience made it possible to treat reassurance as a primary design goal, rather than as a secondary layer added to a speed-first flow.

A short prototype shows how reassurance, confirmation, and voice-supported input work together in the payment flow

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Task flow focus

I used the payment flow as a representative high-stakes task to explore how guidance, pacing, confirmation, and user control could work across the wider banking experience.The goal was not to redesign only one payment screen, but to define a repeatable interaction pattern for sensitive banking flows where users need clarity before moving forward.

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Interaction-state model: Neutral → Guided → Confirmed

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To make reassurance repeatable, I defined a simple interaction model: Neutral → Guided → Confirmed.

The model describes how the system behaves during sensitive financial actions: it listens, repeats back what it understood, waits for explicit confirmation, highlights only the next safe action, and keeps the confirmed state visible.

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The interface is inspired by the physical service desk.

At a bank branch:

• The representative points to what comes next
• They wait
• They confirm before proceeding
• They do not act without explicit consent

The digital flow mirrors this behavior:

• Buttons begin neutral
• After a short delay, the next step is softly highlighted
• When selected, the choice remains visibly confirmed
• Nothing progresses automatically

The system does not rush.
It reassures.

Rather than pushing the user through the flow, the interface behaves more like a steady presence. It shows where attention is needed, waits for action, and makes the result of that action visible. The aim was not to create a more animated interface, but a more trustworthy one.

This is not animation.
This is a conversation. After the user speaks their choice, the interface waits, points, and confirms — the way a person at the service desk would.

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The Guiding Presence

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Rather than simplifying the interface further, the focus moved toward introducing a guiding presence — similar to the role of a service medewerker.

Voice prompts the next step. The interface listens, confirms what it heard, and waits for approval before moving forward. The glow shows where attention is needed.

“Voor wie is de betaling?”
“Welk bedrag wilt u overmaken?”
“U wilt €300 overmaken. Klopt dat?”

The flow itself stays rule-based and unchanged.
AI interprets voice input. It does not make decisions.

Reassurance becomes part of the interface — not something you have to leave the app to find.

The support many users previously sought from staff is brought into the flow itself — not as automation, but as guided structure. The product does not try to replace human care with intelligence. It translates some of the most useful qualities of that care into interaction behavior: pacing, confirmation, and visible attentiveness.

The system prompts, listens, confirms, and waits.
Nothing moves forward until the user says so.

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Design System: Reassurance as Interaction Architecture

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ING Senior Assist is built on a controlled interaction grammar.

Reassurance is not added through messaging.
It is embedded into state behaviour.

All interactive elements follow a consistent three-state structure:

Neutral
Guided
Confirmed

Neutral removes bias.
Guided introduces soft emphasis after a timed delay.
Confirmed creates persistent visual stability. The guided state is expressed through a soft orange glow. The glow is diffused and gradual — never abrupt, never flashing. It appears after a short delay to avoid perceived urgency.

It functions as directional guidance, similar to how a service desk representative subtly indicates the next step with a gesture. It does not command action. It suggests sequence.

When the user selects an element, the glow transitions into a persistent outline. The outline remains visible to anchor the decision in memory. This sequence is universal. It does not change per screen. Predictable state behavior reduces cognitive overhead and reinforces trust. The same pattern — voice in, glow out, confirm — is designed to work across the entire app. Every flow. Every step. One consistent language of guidance. Because trust only works if it’s consistent.

This was one of the most important design decisions in the project.

Instead of solving reassurance screen by screen, the concept defines it as a reusable system behavior. The interface does not need to become louder to be more supportive. It needs to become more legible, more stable, and more consistent in how it responds.

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Trade-offs

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This experience is slower.
It requires explicit confirmation steps.
It does not auto-optimize for speed.

But in financial contexts, speed is not the primary KPI.

Trust is.

Limiting machine learning in critical flows reduces automation potential, but increases explainability and perceived safety.

That trade-off is intentional.

The concept gives up some efficiency in order to preserve confidence.

That was not a compromise made reluctantly.
It was the point.

A system that feels slightly slower, but easier to trust, may ultimately support more independent use than a system that is optimized for speed but experienced as uncertain.

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Validation plan

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Because this was a concept project, I did not run a full usability test. The next step would be to test whether older users understand each step, feel in control, and can complete the payment flow without needing extra reassurance from a service representative.

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Example test tasks

• Start a payment using voice-supported guidance  

• Confirm the recipient and amount  

• Explain what will happen before pressing the final confirmation button  

• Identify whether the system is guiding, confirming, or waiting for user input  

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Success indicators

• Users understand what is happening at each step  

• Users feel guided without feeling pushed forward  

• Fewer moments of hesitation or repeated checking  

• Clear understanding of the final confirmation step  

• Users can explain what the system will and will not do

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What i would measure

• Task completion without staff intervention

• Number of repeated checks before final confirmation 

• Time spent on the confirmation step

• Self-reported confidence before and after the flow

• User ability to explain what the system will do next

• Support questions around “what should I press?” or “is this correct?”

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Business Impact & Hypothesis

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Many of the conversations I handled at the service desk were not about technical errors.
They were about reassurance.

“What do I need to press?”
“Is this the right button?”
“Can you check if I did it correctly?”

These interactions take time, even when nothing is wrong. If a large bank handles approximately 200 reassurance-driven interactions per day, and each interaction costs around €5–€10 in staff time, that represents roughly €365,000–€730,000 per year in operational effort. The hypothesis is simple: if predictable system behavior increases psychological safety in high-risk payment flows, reassurance-driven interactions may decrease. Even a modest 10% reduction could create measurable operational savings — while improving user confidence. Trust is not only emotional. It has operational consequences.

What is often dismissed as “just needing help” can represent a meaningful service burden at scale. This is why emotional safety is not separate from business thinking here. It is part of it.

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Outcome

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ING Senior Assist reframes digital banking as a confidence-building environment.

It demonstrates:

• Strategic restraint in AI deployment
• Translating field experience into system logic
• Designing interaction timing intentionally
• Prioritizing emotional safety in regulated contexts

This project is not about making banking smarter.

It is about making confidence visible.

More specifically, it explores what happens when reassurance is treated as part of the product itself — not as a customer service layer that begins only after the interface fails.

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Reflection

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Designing for aging is not about larger fonts alone.
It is about mental models formed in different technological eras.

Many older adults grew up with mechanical interfaces where controls were visible and fixed. Modern layered digital systems require navigation, abstraction, and adaptation.

When interfaces change silently, trust erodes.

Every generation eventually becomes unfamiliar with new technology.
Designing for predictability today is designing for everyone’s future self.

This project changed the way I think about intelligence in product design.

Not every problem asks for more adaptation.
Sometimes the better question is how to make the system clearer, steadier, and easier to trust.

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Prototyping & AI Use

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This prototype was built in Figma Make, with AI used as a thinking partner during iteration. ChatGPT helped test interaction logic and refine timing patterns, particularly around guidance and confirmation states. All explorations were based on synthetic behavioral assumptions. No internal banking data or customer information was used.

AI assisted the work.

The decisions were mine.

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