How a ‘Self-Driving Wallet’ Platform is Using AI to Personalize Credit Card Offers

Artificial intelligence is having its moment in finance, yet few are seeing its potential more than Kudos.

The company, which began as a browser plugin helping consumers choose the most optimal card for checkout, is now a stand-alone, full-featured platform offering a “self-driving wallet,” according to CEO Tikue Anazodo.

At its foundation is an AI-driven engine reading spending patterns, predicting future necessities, and offering weekly suggestions tuned for individual users.

“The more we know, the more advice we are able to give,” Anazodo told CardRates.com. “Google doesn’t know your credit. Google doesn’t know what you are seeking to buy. We do.”

Kudos has built up one of the strongest industry datasets in the background, pooling together credit reports, transaction activities by individual accounts via a Plaid integration, and objectives submitted by users.

Its recommendation system scrutinizes the data in an effort to maximize credit card spending and saving potential from a consumer’s underlying behavior, not surface characteristics like ranges of scores.

The result is a platform that gives advice that is both actionable and dynamic as a reaction to fluctuating financial circumstances.

Unlike most platforms that show only partner offers, Kudos displays over 3,000 American card offers — even from issuers it is not affiliated with.

Every recommendation is backed by Anazodo’s definition of “math you can see,” including the calculation of first-year and second-year rewards and how users will stand to gain by maximizing their wallets.

“If your wallet is in good shape, maybe you’re leaving $18 on the table. But if it is not, the potential is in the hundreds,” he added. “We make it simple for you, even if we don’t make a dime on the card.”

This analytical approach instills confidence and presents Kudos as a company that genuinely cares about helping users. Around 90% of the information the site offers is not monetizable in any way.

Instead, it’s about providing information that helps cardholders make smarter decisions — reminding a user when credits are about to expire, alerting them to bad card choices, or approximating interest charges from current balances.

Even card-agnostic advice, like how to save on insurance premiums, is trend-based and individually analyzed.

The architecture of Kudos’ personalization is an RAG (Retrieval-Augmented Generation) comprising two streams of information: a complete database of every U.S. credit card and anonymized behavior information from over 350,000 members.

“You change week by week, your behavior changes, and hence insights change,” Anazodo said. “If you just bought a home, which changes your spending orientation, our system is attuned to that, and it will adjust.”

The in-app Kudos’ Maria GPT enables users to ask finance questions and receive customized answers. These are triggered from an associated consumer’s data, and they can often point to potential solutions, for example, saving $1,000 in a period of 12 months, based on spending habits.

The chatbot is trained for this monetary context in particular, and it is one of Kudos’ broader ambitions to substitute static advice with dynamic advice.

It trains its models on anonymized information only and never divulges member information. Card numbers are locally encrypted using zero-knowledge technologies, a detail that even Kudos can’t access. Members connect their entire list of credit cards within two weeks of signing up.

Kudos built its platform using anonymized datasets and keeps privacy and consent at its core, promising responsible AI use.

It is also a basic principle of how AI-driven personalization is achieved. The company intentionally avoids mystery scoring models or affiliate-driven prioritization in favor of exposing raw dollar results for all its recommendations, which allows users to see in plain sight exactly what they get in return.

Kudos sees its role not just as a recommendation tool, but as part of a broader trend toward vertically segmented AI platforms.

For Anazodo, the finance industry has a window of opportunity to leapfrog traditional search engines by offering users responses specific to their everyday experience, as opposed to generally applicable advice.

Anazodo thinks the industry is moving away from generalized intent to awareness by context, and if it’s able to demonstrate to consumers what is going to occur based on having seen it before, they make better decisions. “That is personalization that works.”

With the upcoming launch of a new “future spend snapshot” functionality, that ability will be amplified further, and users will be able to see in advance recurring charges and spending commitments.

And with about 15% of Kudos’ users already using the app to track and manage business charge cards, there appears to be a runway for growth into commercial users.

With transparency, AI breadth, and singular focus on user outcomes, Kudos is perhaps setting a new bar for personalized finance tools — one that not only tells users the action to take but explains why it matters.

Kudos is not alone in using AI to personalize investment choices, but it is one of the most ambitious. What gives it an edge is the way it brings together actual behavior — what they’re spending, what they’re saving for, where they’re spending too much — with dynamic recommendations that change as those habits change.

In other words, instead of basing advice on cookie-cutter suggestions or suggesting partners that only offer a referral bonus. Kudos calculates recommendations based on how users use their cards.

And as a result, the information it gives isn’t blanket — it’s specific to every individual’s circumstances, and it changes when the circumstances shift. Kudos is a part of a larger movement in which tools are becoming more dynamic and less fixed.

These are not merely affiliate sites or finance calculators — they’re systems that get to know the individual over time and adjust accordingly. Other platforms following in a comparable direction are:

Cleo, which enables users to track expenditures using a chat-based budgeting assistant.

Monarch Money, a planning tool that forecasts future net worth and identifies space to save.

Radius AI by LendingClub displays customers’ evolving financial health in real time.

Tally, a payment automation solution that keeps charges in check.

Trim, which checks for recurring charges and offers services to reduce household bills.

The industry is clearly heading toward more flexible financial support that adapts when life doesn’t go as planned.