Amanda Pinto

UX Architect @VML

Amanda Pinto

UX Architect @VML

Driving Clarity, Confidence, and Conversion for Financial Products @FirstAbuDhabiBank

ROLE

UX Architect

TOOLS

Figma, Google Analytics 4,

Microsoft Clarity, Miro

DURATION

April 2025 to Present

A QUICK RECAP

I shaped the user experience for the bank’s most critical acquisition journeys, using research and design to improve how 4 million customers find, evaluate, and apply for financial products.

AT A GLANCE

Research and
Customer Insight

Multi-method research across 6 banking products, combining insights to inform product decisions.

Revamp of
High-Value Pages

Revamp of High-Value Pages

Restructured the homepage and credit card pages driving a +66% increase in application starts.

AI Advocacy for
Internal Workflow

AI Advocacy for Internal Workflow

Introduced AI-powered prototyping to speed up exploration and improve developer alignment.

Designing Complex Financial Tools

Designed loan calculators, navigation, and financial tools through testing, iteration, and close dev collaboration.

AI Assistant
Experience Design

AI Assistant Experience Design

Designed conversational flows, edge cases, and messaging for an AI-powered product discovery chatbot.

Design System Optimisation

Audited and redesigned 100+ components to improve usability, scalability, visual appeal and flexibility.

Under lock and key

This case study is shared under NDA. Enter the password to continue.

For access inquiries, email me.

CHALLENGE

FAB’s credit card portfolio had been expanding quickly, increasing choice but also introducing complexity for customers trying to compare and select the right card, particularly through the website.

DISCOVER

METHODOLOGY

To tackle this challenge, we decided to thoroughly understand their behaviour and preferences by speaking with customers who were either actively in the market for a new credit card or had obtained one within the last three months.

GA4

Web analytics analysis

5 In-depth interviews (elite customers earning >AED 50k)

In-depth interviews (elite customers
earning >AED 50k)

100 Survey respondents (elite customers earning >AED 5k)

Survey respondents (elite customers
earning >AED 5k)

Elite customers were harder to recruit, so we focused on in-depth interviews to understand their decision-making, starting with them because they have access to a broader range of products. For mass customers, a larger survey was used to validate patterns at scale. We also tried to run a survey for elite, to be able to generalise the findings more confidently, but the pool of survey respondents had just 1-2% elite customers.

Elite customers were harder to recruit, so we focused on in-depth interviews to understand their decision-making, starting with them because they have access to a broader range of products.

For mass customers, a larger survey was used to validate patterns at scale. We also tried to run a survey for elite, to be able to generalise the findings more confidently, but the pool of survey respondents had just 1-2% elite customers.

GOAL

Understand how different customer segments approach credit card selection and what drives their thinking from discovery → evaluation → application and use those insights to create better product-customer fit and ultimately drive an increase in applications.

DATA & WEB ANALYTICS

Data and analytics provided behavioural evidence of how users navigated and evaluated credit cards in a real environment. This helped identify patterns and form hypotheses to validate through qualitative research.

Users prioritise understanding before applying: ‘Learn more’ was consistently clicked more than 'Apply Now' across all devices.

Users arrive with strong intent and quickly validate their choice: 85% of organic users viewed only one card detail page before applying or submitting a lead.

Users use the comparison tool and go back and forth between cards with overlapping benefits: The most common comparisons were: Cashback and FAB Rewards Indulge together (8%), viewed Cashback and FAB-Z together (7%), all 3 being lifestyle/entertainment cards.

Data analytics revealed how users engaged with each card, including application rates, time spent evaluating product pages, and the sequence of cards visited, helping us build a clearer picture of decision-making behaviour on a per-card basis.

RESEARCH INSIGHTS

Customers begin their search with existing banks but will switch for tangible superior value.

MOTIVATION

"I already salary transfer to the same bank so it made sense [to get a card from the same bank]."

BEHAVIOUR

"There is a good ones on the market. So, like, if the banks is offering. I'm like,

okay. Go ahead.”

"There is a good ones on the market. So, like, if the banks is offering. I'm like, okay. Go ahead.”

PAIN POINT

”But recently, I'm not happy with the, ADIB…in last two months, I'm trying to get a value. So, I want to check more the benefits of the value.”

MASS SURVEY RESULT

37% stated that having an existing relationship with their bank was Neutral in importance when selecting a banking provider.

When respondents were asked their primary reasons were for considering a new credit card, 51% of respondents said they found a card with significantly better benefits/rewards and 29% of respondents said their existing benefits/rewards were no longer suitable.

37% stated that having an existing relationship with their bank was Neutral in importance when selecting a banking provider.

When respondents were asked their primary reasons were for considering a new credit card, 51% of respondents said they found a card with significantly better benefits/rewards and 29% of respondents said their existing benefits/rewards were no longer suitable.

Calculation of expected gains vs. costs is completed by most participants while comparing Credit Cards.

MOTIVATION

"If there were more benefits, way more than the value of the annual fee then I would be willing to pay it."

BEHAVIOUR

"Per month, I am spending for electric, for water, for school. So you can say from 15 to 20 thousand. If I'm not getting good cashback from that, it's unfair."

PAIN POINT

"You're giving me a cashback credit card. I

barely get around 600 dirham a year. And then you are giving me 600 dirham annual fees. So what's the point?"

"You're giving me a cashback credit card. I barely get around 600 dirham a year. And then you are giving me 600 dirham annual fees. So what's the point?"

DATA INSIGHT

Analytics shows that time spent on product pages before converting increases with card tier: Premium cards like Share Platinum (8 mins 52 secs average) have significantly higher engagement than lower-tier cards (e.g., Share Signature 2 mins 18 secs, Share Standard 4 mins 59 secs).

Since all pages are structured using a similar template, this indicates that customers spend more time evaluating higher-cost cards (Elite Card), supporting the idea that they are carefully calculating gains and losses before committing.

Analytics shows that time spent on product pages before converting increases with card tier: Premium cards like Share Platinum (8 mins 52 secs average) have significantly higher engagement than lower-tier cards (e.g., Share Signature 2 mins 18 secs, Share Standard 4 mins 59 secs).

Since all pages are structured using a similar template, this indicates that customers spend more time evaluating higher-cost cards (Elite Card), supporting the idea that they are carefully calculating gains and losses before committing.

Participants filter credit cards by key criteria to shortlist options before conducting deeper research.

MOTIVATION

"So then I will just note down which ones have the free for life and then compare only between those cards."

BEHAVIOUR

”I told ChatGPT that my bank is charging me an annual fees for my

credit card. So I'm looking for, free for life credit card that still give me the benefits of the cashback."

”I told ChatGPT that my bank is charging me an annual fees for my credit card. So I'm looking for, free for life credit card that still give me the benefits of the cashback."

PAIN POINT

”These are my needs. Then they'll give me the options. Not a hundred of

options, and it's like, okay. I need only three. Why do I need a hundred? You're just losing the customer. "

”These are my needs. Then they'll give me the options. Not a hundred of options, and it's like, okay. I need only three. Why do I need a hundred? You're just losing the customer. "

DATA INSIGHT

10.71% of total clicks on the credit card overview page were on the filter chips on mobile and 7.42% on desktop, indicating high engagement in general.

Several participants leveraged AI for Credit Card discovery and filtering.

MOTIVATION

"I need a clear view and understanding of the cards available on the market"

BEHAVIOUR

”I told ChatGPT that my bank is charging me an annual fees for my

credit card. So I'm looking for, free for life credit card that still give me the benefits of the cashback."

”I told ChatGPT that my bank is charging me an annual fees for my credit card. So I'm looking for, free for life credit card that still give me the benefits of the cashback."

PAIN POINT

”I need the online application to ask

me "What do you need?". Like an AI embedded in it."

”I need the online application to ask me "What do you need?". Like an AI embedded in it."

MASS SURVEY RESULT

50% are Very familiar with AI and use a variety of tools in their personal/professional lives.

68% are willing to use AI to Compare specific features side-by-side. 55% are open to using AI to Get answers to general questions about card benefits or terms.

However, 58% of respondents expressed concerns related to Security and privacy of personal financial data.

50% are Very familiar with AI and use a variety of tools in their personal/professional lives.

68% are willing to use AI to Compare specific features side-by-side. 55% are open to using AI to Get answers to general questions about card benefits or terms.

However, 58% of respondents expressed concerns related to Security and privacy of personal financial data.

All research insights were tagged and documented within the internal CRM, allowing themes to be referenced across teams and reused in future product and content decisions.

DATA & WEB ANALYTICS

Data and analytics provided behavioural evidence of how users navigated and evaluated credit cards in a real environment. This helped identify patterns and form hypotheses to validate through qualitative research.

Users prioritise understanding before applying: ‘Learn more’ was consistently clicked more than 'Apply Now' across all devices.

Users arrive with strong intent and quickly validate their choice: 85% of organic users viewed only one card detail page before applying or submitting a lead.

Users use the comparison tool and go back and forth between cards with overlapping benefits:
The most common comparisons were: Cashback and FAB Rewards Indulge together (8%), viewed Cashback and FAB-Z together (7%), all 3 being lifestyle/entertainment cards.

Data analytics helped us put application submissions, time spent on page before applying and % of users who landed on a certain card page before applying or the cards they visited in their journey to paint a more detailed picture on a per-card basis.

DEFINE

MEETING BUSINESS STAKEHOLDERS

Discussions around product-customer fit and how similar themes to research have come up across multiple verticals.

Surprise around certain results like Elite customers being open to non-premium, Mass products as long as the card fits their need.

Collectively agreed that there is high interest in cashback and rewards but not enough value presentation on the website currently.

Pushed for fast-tracking our recommendation to introduce cashback calculators and advanced card filtering as the tangible next step.

Prompted discussion with communications teams to introduce Whatsapp as a support channel and a "Schedule a callback" feature on existing lead generation forms.

Emphasised the need to focus on our AI projects moving forward such as introducing our FAB AI assistant.

DESIGN - CASHBACK CALCULATOR

GUERRILLA TESTING | NEED & COMPREHENSION VALIDATION

Participants were shown the table currently available on the live website explaining the benefit mechanics are were asked to explain:

"What do you understand from this table in terms of the benefits?"

"If you were to spend AED100 of groceries, assuming you met the overall minimum spend, what benefits would you receive?"

Participants were shown the table currently available on the live website explaining the benefit mechanics & were asked to explain:

"What do you understand from this table in terms of the benefits?"

"If you were to spend AED100 of groceries, assuming you met the overall minimum spend, what benefits would you receive?"

The results of this testing indicated:

Rewards Misconception
Majority of participants assumed they would receive both cashback and FAB Rewards.

Table Frustration
Half of the participants were confused and frustrated by the detailed rewards table.

Most participants had pending questions after viewing the table;

One participant wanted to know exactly what select categories were without needing to open a .pdf

One participant wanted to have an idea of their total spend so they could better compare cashback

One participant had a question about the validity of FAB Rewards

One participant wanted to know if FAB Rewards could be redeemed for other benefits

One participant asked if customers would receive an additional 0.15% for select categories despite the category offering exactly 0.15% cashback.

BENCHMARKING

Banks reviewed included HSBC, Liv Bank, Emirates Islamic Bank & ADCB, local banks that had relatively different approaches to their cashback calculator

Most calculators focus on direct monthly/yearly cashback earned.

Some also highlight other reward types like miles (Liv Bank) or "Other Value Benefits" such as, movie tickets, airport lounges (Emirates Islamic).

Input Granularity varies from simple ("Total Monthly Spend") (Liv Bank) to highly detailed category breakdowns (HSBC).

CALCULATOR INPUTS

Benchmarking showed a variation in calculator inputs, with some having detailed category-level spending and others focusing only on primary spending categories.

To understand the right balance, we designed two versions to do some guerilla testing with:

A simplified version (Option 1) with five key spending categories and slider inputs

A detailed version (Option 2) with ten categories, reflecting the full rewards structure

All participants found rewards easier to understand in calculator format. There were no misconceptions about the cashback mechanics.

Sliders were more effective in visually communicating spending ranges and maximum thresholds, helping users understand how cashback scaled and where reward caps applied.

The simplified version risked oversimplifying the rewards structure especially for certain "select categories" that were left out but might be common spending categories for certain customers.

The detailed version introduced too much cognitive load and limited understanding of ranges accepted.

Alongside this cashback calculator, we had been working on loan calculators. To optimise those, we had conducted similar guerilla testing specifically on inputs.

This research revealed text inputs were used as a primary input method on both desktop and mobile, sliders were also important tools for interaction and customer exploration. Due to the difficulty of inputting specific numbers with a slider on mobile, we found that implementing +/- buttons helped with exploration on mobile devices.

We considered this when making the final decision on the for both cohesion and usability best practices.

TAKEAWAY

Ultimately, the challenge wasn’t whether to simplify or detail - it was balancing accuracy with cognitive load.

Since we planned to do some more usability testing later on, we put a pin in this dilemma and focused on results.

CALCULATOR RESULTS

1 Clear calculation of total FAB Rewards (loyalty points) based on spending ultimately reducing effort on the customer's end.

2 Customers thought they received both loyalty rewards and cashback from the table we tested with. This "or" and visual separation clarifies that cashback is one way to redeem rewards.

Customers thought they received both loyalty rewards and cashback from the table we tested with initial. This "or" format and visual separation clarifies that cashback is one way to redeem rewards.

3 Calculation of equivalent cashback value based on spending and approved conversion rate, saving customer's the effort of opening terms and conditions,

4 “Apply now” CTA to convert intent at the exact moment customers see personal value by capitalising on the confidence that this tool should bring to them. This also tracks calculator effectiveness as a conversion tool.

5 "Discover Other Redemption Options" secondary button encourages customers to understand the broader rewards system and exposes additional value.

6 Terms and condition note including legal information as well as a hyperlink to a how-to page walking customers through the redemption process on the mobile app.

DESIGN CRIT

After sharing the designs with my line manager, a researcher, and the project manager, we reviewed the solution in context.

While the design could reduce mental calculation, we identified a remaining friction point: if the annual fee sat higher on the page and the calculator lived further down, users still had to scroll to calculate ROI. This introduced an unnecessary extra step, weakening the overall flow.

CALCULATOR 1.0

Based on the initial exploration and interal design critique, I put together a preliminary version of the design.

1 Clear calculation of total FAB Rewards (loyalty points) based on spending ultimately reducing effort on the customer's end.

2 Customers thought they received both loyalty rewards and cashback from the table we tested with. This "or" and visual separation clarifies that cashback is one way to redeem rewards.

Customers thought they received both loyalty rewards and cashback from the table we tested with initial. This "or" format and visual separation clarifies that cashback is one way to redeem rewards.

3 “Apply now” CTA to convert intent at the exact moment customers see personal value by capitalising on the confidence that this tool should bring to them. This also tracks calculator effectiveness as a conversion tool.

4 "Discover Other Redemption Options" secondary button encourages customers to understand the broader rewards system and exposes additional value.

FIGMA MAKE PROTOTYPING

AI-assisted prototyping supported early testing, client validation, and development readiness.

Early validation and testing using a near-final design would enable quick iteration based on observed friction rather than assumptions

Micro-interactions (sticky bars, smooth sliders, feedback states) could be visualised early to assess usability and comfort

Mobile flows could be put directly in clients’ hands during stakeholder meetings, reflecting real usage (70%+ of users engage on mobile)

Clearer interactions would support a smoother development handoff and give more control to our design team to design our expectations

USABILITY TEST OUTCOMES AND FINAL DESIGN

Usability testing validated the design and clarified where final refinements were needed.

1 Focused inputs on high-value categories

During testing, some users entered high rent amounts (AED 9,000–10,000). Because rent earned only 0.15% cashback, it ended up overshadowing higher-value categories and making the card feel less rewarding. We refined the calculator to focus only on categories offering 3%+ cashback.

1 Made total spend explicit to reduce mental calculation

We noticed users repeatedly scrolling back to add up their inputs and compare cashback with their spending. To make this easier and faster, total spend was surfaced directly in the results view.

WHAT HAPPENED NEXT

We had a final presentation with our external stakeholders who signed off the design!

What was more exciting… they really felt the tool had the potential to be helpful, so much so that they were on board to begin designing a similar tool for co-branded cards with different reward structures.

What was more exciting… they really felt the tool had the potential to be helpful, so much so that they were on board to begin designing a similar tool for co-branded cards with different reward structures (in progress as we speak!)

A tech feasibility meeting was scheduled to assess the design, available resources, efforts required and subsequent timelines.

The feature is currently in development.

This feature is currently in development.

Amanda Pinto

UX Architect @VML

WORK

> FIRST ABU DHABI BANK

ABOUT

RESUME

Dubai, UAE | London, UK | New York, USA

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@Amanda Pinto 2025

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Thanks for scrolling.

@Amanda Pinto 2025

LET'S CONNECT

INSTAGRAM

Amanda Pinto

UX Architect @VML

Amanda Pinto

UX Architect @VML

A QUICK RECAP

I shaped the user experience for the bank’s most critical acquisition journeys, using research and design to improve how 4 million customers find, evaluate, and apply for financial products.

AT A GLANCE

Research andCustomer Insight

Multi-method research across 6 banking products, combining insights to inform product decisions.

Revamp of High-Value Pages

Revamp of High-Value Pages

Restructured the homepage and credit card pages driving a +66% increase in application starts.

AI Advocacy for Internal Workflow

AI Advocacy for Internal Workflow

Introduced AI-powered prototyping to speed up exploration and improve developer alignment.

Designing Complex Financial Tools

Designed loan calculators, navigation, and financial tools through testing, iteration, and close dev collaboration.

AI AssistantExperience Design

AI Assistant Experience Design

Designed conversational flows, edge cases, and messaging for an AI-powered product discovery chatbot.

Design System Optimisation

Audited and redesigned 100+ components to improve usability, scalability, visual appeal and flexibility.

CHALLENGE

FAB’s credit card portfolio had been expanding quickly, increasing choice but also introducing complexity for customers trying to compare and select the right card, particularly through the website.

DISCOVER

METHODOLOGY

To tackle this challenge, we decided to thoroughly understand their behaviour and preferences by speaking with customers who were either actively in the market for a new credit card or had obtained one within the last three months.

GA4

Web analytics analysis

Web analytics analysis

5

In-depth interviews (elite customers earning >AED 50k)

In-depth interviews (elite customers earning >AED 50k)

100

Survey respondents (elite customers earning >AED 5k)

Survey respondents (elite customers earning >AED 5k)

GOAL

Understand how different customer segments approach credit card selection and what drives their thinking from discovery → evaluation → application and use those insights to create better product-customer fit and ultimately drive an increase in applications.​

DATA & WEB ANALYTICS

Data and analytics provided behavioural evidence of how users navigated and evaluated credit cards in a real environment. This helped identify patterns and form hypotheses to validate through qualitative research.

Users prioritise understanding before applying: ‘Learn more’ was consistently clicked more than 'Apply Now' across all devices.

Users arrive with strong intent and quickly validate their choice: 85% of organic users viewed only one card detail page before applying or submitting a lead.

Users use the comparison tool and go back and forth between cards with overlapping benefits: The most common comparisons were: Cashback and FAB Rewards Indulge together (8%), viewed Cashback and FAB-Z together (7%), all 3 being lifestyle/entertainment cards.

Data analytics revealed how users engaged with each card, including application rates, time spent evaluating product pages, and the sequence of cards visited, helping us build a clearer picture of decision-making behaviour on a per-card basis.

RESEARCH INSIGHTS

Customers begin their search with existing banks but will switch for tangible superior value.

MOTIVATION

"I already salary transfer to the same bank so it made sense [to get a card from the same bank]."​​

"I already salary transfer to the same bank so it made sense [to get a card from the same bank]."​​

BEHAVIOUR

"There is a good ones on the market. So, like, if the banks is offering. I'm like,

okay. Go ahead.”​

"There is a good ones on the market. So, like, if the banks is offering. I'm like, okay. Go ahead.”​

"There is a good ones on the market. So, like, if the banks is offering. I'm like, okay. Go ahead.”​

PAIN POINT

”But recently, I'm not happy with the, ADIB…in last two months, I'm trying to get a value. So, I want to check more the benefits of the value.”​

”But recently, I'm not happy with the, ADIB…in last two months, I'm trying to get a value. So, I want to check more the benefits of the value.”​

MASS SURVEY RESULT

MASS SURVEY RESULT

37% stated that having an existing relationship with their bank was Neutral in importance when selecting a banking provider.

When respondents were asked their primary reasons were for considering a new credit card, 51% of respondents said they found a card with significantly better benefits/rewards and 29% of respondents said their existing benefits/rewards were no longer suitable.​

37% stated that having an existing relationship with their bank was Neutral in importance when selecting a banking provider.

When respondents were asked their primary reasons were for considering a new credit card, 51% of respondents said they found a card with significantly better benefits/rewards and 29% of respondents said their existing benefits/rewards were no longer suitable.​

Calculation of expected gains vs. costs is completed by most participants while comparing Credit Cards.

MOTIVATION

"If there were more benefits, way more than the value of the annual fee then I would be willing to pay it."

"If there were more benefits, way more than the value of the annual fee then I would be willing to pay it."

BEHAVIOUR

"Per month, I am spending for electric, for water, for school. So you can say from 15 to 20 thousand. If I'm not getting good cashback from that, it's unfair."

"Per month, I am spending for electric, for water, for school. So you can say from 15 to 20 thousand. If I'm not getting good cashback from that, it's unfair."

PAIN POINT

"You're giving me a cashback credit card. I

barely get around 600 dirham a year. And then you are giving me 600 dirham annual fees. So what's the point?"

"You're giving me a cashback credit card. I barely get around 600 dirham a year. And then you are giving me 600 dirham annual fees. So what's the point?"

DATA INSIGHT

DATA INSIGHT

Analytics shows that time spent on product pages before converting increases with card tier: Premium cards like Share Platinum (8 mins 52 secs average) have significantly higher engagement than lower-tier cards (e.g., Share Signature 2 mins 18 secs, Share Standard 4 mins 59 secs).​

​

Since all pages are structured using a similar template, this indicates that customers spend more time evaluating higher-cost cards (Elite Card), supporting the idea that they are carefully calculating gains and losses before committing.​​

Analytics shows that time spent on product pages before converting increases with card tier: Premium cards like Share Platinum (8 mins 52 secs average) have significantly higher engagement than lower-tier cards (e.g., Share Signature 2 mins 18 secs, Share Standard 4 mins 59 secs).​

​

Since all pages are structured using a similar template, this indicates that customers spend more time evaluating higher-cost cards (Elite Card), supporting the idea that they are carefully calculating gains and losses before committing.​​

Participants filter credit cards by key criteria to shortlist options before conducting deeper research.

MOTIVATION

Research and
Customer Insight

Revamp of
High-Value Pages

AI Advocacy for
Internal Workflow

AI Assistant
Experience Design

In-depth interviews (elite customers
earning >AED 50k)

Survey respondents (elite customers
earning >AED 5k)

Understand how different customer segments approach credit card selection and what drives their thinking from discovery → evaluation → application and use those insights to create better product-customer fit and ultimately drive an increase in applications.

"I already salary transfer to the same bank so it made sense [to get a card from the same bank]."

"I already salary transfer to the same bank so it made sense [to get a card from the same bank]."

okay. Go ahead.”

"There is a good ones on the market. So, like, if the banks is offering. I'm like, okay. Go ahead.”

"There is a good ones on the market. So, like, if the banks is offering. I'm like, okay. Go ahead.”

”But recently, I'm not happy with the, ADIB…in last two months, I'm trying to get a value. So, I want to check more the benefits of the value.”

”But recently, I'm not happy with the, ADIB…in last two months, I'm trying to get a value. So, I want to check more the benefits of the value.”

When respondents were asked their primary reasons were for considering a new credit card, 51% of respondents said they found a card with significantly better benefits/rewards and 29% of respondents said their existing benefits/rewards were no longer suitable.

When respondents were asked their primary reasons were for considering a new credit card, 51% of respondents said they found a card with significantly better benefits/rewards and 29% of respondents said their existing benefits/rewards were no longer suitable.

Analytics shows that time spent on product pages before converting increases with card tier: Premium cards like Share Platinum (8 mins 52 secs average) have significantly higher engagement than lower-tier cards (e.g., Share Signature 2 mins 18 secs, Share Standard 4 mins 59 secs).

Since all pages are structured using a similar template, this indicates that customers spend more time evaluating higher-cost cards (Elite Card), supporting the idea that they are carefully calculating gains and losses before committing.

Analytics shows that time spent on product pages before converting increases with card tier: Premium cards like Share Platinum (8 mins 52 secs average) have significantly higher engagement than lower-tier cards (e.g., Share Signature 2 mins 18 secs, Share Standard 4 mins 59 secs).

Since all pages are structured using a similar template, this indicates that customers spend more time evaluating higher-cost cards (Elite Card), supporting the idea that they are carefully calculating gains and losses before committing.

"So then I will just note down which ones have the free for life and then compare only between those cards."

"So then I will just note down which ones have the free for life and then compare only between those cards."

credit card. So I'm looking for, free for life credit card that still give me the benefits of the cashback."

”I told ChatGPT that my bank is charging me an annual fees for my credit card. So I'm looking for, free for life credit card that still give me the benefits of the cashback."

options, and it's like, okay. I need only three. Why do I need a hundred? You're just losing the customer. "

”These are my needs. Then they'll give me the options. Not a hundred of options, and it's like, okay. I need only three. Why do I need a hundred? You're just losing the customer. "

”These are my needs. Then they'll give me the options. Not a hundred of options, and it's like, okay. I need only three. Why do I need a hundred? You're just losing the customer. "

"I need a clear view and understanding of the cards available on the market"

"I need a clear view and understanding of the cards available on the market"

credit card. So I'm looking for, free for life credit card that still give me the benefits of the cashback."

”I told ChatGPT that my bank is charging me an annual fees for my credit card. So I'm looking for, free for life credit card that still give me the benefits of the cashback."

me "What do you need?". Like an AI embedded in it."

”I need the online application to ask me "What do you need?". Like an AI embedded in it."

50% are Very familiar with AI and use a variety of tools in their personal/professional lives.

68% are willing to use AI to Compare specific features side-by-side. 55% are open to using AI to Get answers to general questions about card benefits or terms.

However, 58% of respondents expressed concerns related to Security and privacy of personal financial data.

50% are Very familiar with AI and use a variety of tools in their personal/professional lives.

68% are willing to use AI to Compare specific features side-by-side. 55% are open to using AI to Get answers to general questions about card benefits or terms.

However, 58% of respondents expressed concerns related to Security and privacy of personal financial data.

Users use the comparison tool and go back and forth between cards with overlapping benefits:
The most common comparisons were: Cashback and FAB Rewards Indulge together (8%), viewed Cashback and FAB-Z together (7%), all 3 being lifestyle/entertainment cards.

Participants were shown the table currently available on the live website explaining the benefit mechanics are were asked to explain:

"What do you understand from this table in terms of the benefits?"