Hey, fintech fam 💜

Coming to you after a full week in Chicago! So much fun, so many memories, and also… I am very happy to be back home in New York.

Today, I’m sharing my recent conversation with Laurel Taylor, Founder and CEO of Candidly.

For nearly 10 years covering fintech, I’ve heard some version of the same promise: technology will eventually make financial advice truly personalized, accessible, and responsive to someone’s entire financial life.

We may finally be watching that promise come to life — at scale.

The next generation of financial wellness isn’t just going to tell someone to pay off debt or save more. It should understand their student loans, retirement benefits, savings goals, income, and financial tradeoffs, and help them figure out what to prioritize for their specific life.

That’s the bigger story Laurel and I get into today: how we move from financial tools that solve one problem at a time toward technology that can actually help people make better decisions across their entire financial lives.

Let’s get into it. ✨

Laurel Taylor always knew she was going to college. 

Her mother made sure of it, treating a college degree as the only reliable path up and out. 

What neither of them accounted for was the cost on the other side of that decision: they lost two decades of compounding interest on wealth while they paid down student debt.

That single trade-off, which is the invisible cost of financing the American dream, is the reason Candidly exists. 

Ten years later, Taylor is still solving the same problem, but on a scale she never imagined. 

I've been talking to Taylor since Candidly was still explaining itself to the market. This conversation, however, marked the company's tenth anniversary and was different. 

A decade in, she has the user numbers to back up what used to be a pitch: 86% of users take action the same day, 61% move into a different financial domain than the one they came in for.

The received wisdom in personal finance has always been sequential: pay down debt first, then start saving and investing.

Taylor's alma mater, MIT AgeLab, studied this behavior and confirmed that Candidly is onto something: people with student debt move through their finances in that order, fully resolving debt before retirement savings even begins. 

The problem is that delaying investing can sacrifice some of the most valuable years for compounding. Money invested earlier has more time to grow, making the years spent waiting to build wealth difficult to recover later.

Every year spent exclusively focused on debt can mean less time for retirement savings to compound.

Candidly's answer was the student loan retirement match, built on the Secure 2.0 legislation that went live in 2024 and lets employers match retirement contributions based on an employee's student loan payments. 

It's a small mechanical shift with an outsized advantage: debt payoff and wealth building stop being sequential and start happening at the same time. 

Taylor calls it simultaneous progress. It is also, functionally, a redesign of how an entire generation is taught to build wealth.

The student loan retirement match solved one instance of sequential thinking: debt versus savings. 

Taylor's argument extends into something bigger. 

Financial guidance today asks a person to solve one problem at a time, in isolation, across dozens of separate tools, when the problems were never actually separate to begin with.

"What do I do with my 401k? What do I do with my investments? What do I do with breaking the cycle so that my niece doesn't graduate with student debt?" Taylor said. "One is interdependent on the other." 

A decision to accelerate a 529 contribution changes what's available for a 401(k) match.

A decision to pay down debt faster changes the window left for compounding.

None of these decisions happen in a vacuum, but the tools built to manage them treat each one as if it does.

Taylor's word for the result is fragmentation, and she doesn't treat it as an inconvenience. She calls it "mass dysfunction", a description that reframes the problem entirely. 

The barrier to financial guidance was never that people lack access to information about their 401(k) or their student loans individually. We don’t have an information problem — we are nearly drowning in it. 

The real problem is that our financial lives are interconnected, but the systems managing them aren’t. Debt, savings, investing, and retirement are often treated as separate decisions, even though optimizing one can come at the expense of another.

Candidly's Intelligence Center is the attempt to close that gap, using a single conversational interface that pulls from what Taylor calls "deep deterministic domain intelligence" across student debt, 401(k)s, college savings, and now Invest America accounts. 

The retirement match proved the model works for two variables. The bigger bet is that it holds at scale, across everything.

That bet only works if the institutions holding the data and the distribution are willing to plug Candidly in. 

Candidly was built B2B from the start, which means the end user never opens a Candidly app; they encounter Candidly's guidance inside their employer's benefits platform, or inside the interface of a bank they already use. 

The goal was to meet workers "where they work, bank, and experience financial services," Taylor said, rather than ask them to adopt one more standalone tool. 

That's also why the client list matters as much as the user data. 

Bank of America, Merrill Lynch, Charles Schwab, Vanguard, PNC, and TIAA aren't just customers; they're the distribution layer that determines whether Candidly's guidance ever reaches the people who need it.

Getting embedded inside institutions like these is not the same problem as getting a consumer to trust an app. 

Candidly's client list doesn't tolerate ambiguity, and getting an AI system in front of their customers means clearing a bar most consumer-facing AI never has to meet. 

Taylor's framing for how Candidly earns that trust is what she calls the "sacred truths of the enterprise": 

This is where Taylor’s experience offers a useful lesson for fintech leaders. Calling a company AI-native is easy. Building AI that a regulated financial institution will put in front of its customers is much harder.

Few have built the enterprise harness required to actually deploy that AI inside a bank or financial institution’s compliance structure. 

"The margin for error is zero," Taylor told me, and she meant it as an operating principle, not a slogan.

This precision and refusal to settle for anything less than excellent is why Laurel Taylor is speaking at FTW: SF. 

She’s an expert in what it means to be AI-native and get enterprise-level buy-in. Get your VIP tickets now and meet speakers like Laurel Taylor backstage at FTW: SF! 

Taylor once told me, in a previous conversation, that she'd define success in one word: kindness. 

Reflecting on it now, she draws a distinction that matters more than it sounds: kindness is not the same as being nice, and it isn't the opposite of strength. 

Real kindness includes hard feedback and honest correction, extended from a position of power rather than avoidance. 

It shows up in Candidly's 86 NPS score and in a design philosophy that treats every user interaction as "judgment-free, empathy-rich," rather than in a tone that simply avoids friction.

In an industry racing to prove it's AI-native, the companies that will earn long-term trust are the ones that treat kindness and empathy as designed-in product requirements, not personality traits layered on top after the fact.

For years, fintech has promised that technology would make financial guidance more personalized, accessible, and responsive to a person's entire financial life.

AI may finally make that promise technically possible. But possibility and trust are different.

Taylor argues that the next generation of financial guidance can't simply layer a conversational interface over fragmented financial products. The intelligence has to understand how decisions about debt, retirement, savings, and investing affect one another, while operating with the precision regulated financial institutions require.

That's a much higher standard than calling a product AI-native.

And after a decade of watching fintech try to personalize finance, the more important shift to watch now may be this: not whether AI can give financial guidance, but whether the industry can build guidance consumers and financial institutions can actually trust.

How would you explain your fintech’s AI-native features to enterprise-level clients like Bank of America, HSBC, or JPM?

Fintech: your AI conversation starts here.

We will always love you, Dolly! 💕🪩

Honorable mentions: 9-5 (these lyrics hit even harder these days), Dumb Blonde, Just Because I’m a Woman.

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Thanks for spending time with me today!

Love,

Nicole 💜