Why fintech keeps missing first-generation Americans, with Kristy Kim, TomoCredit

Why this conversation matters now: #

There are roughly 30 million Americans with no credit history and no way into the credit system that determines whether they can rent an apartment, buy a car, or get a mortgage. Most of them are not in that position because they cannot afford these things. They are in that position because the US credit system does not know how to read a customer whose life pattern is saving rather than borrowing. In this episode of the Fintech Garden Podcast, Kristy Kim — CEO and co-founder of TomoCredit — walks host Igor Tomych through what fintech keeps getting wrong about first-generation Americans, and what a product designed correctly for that market actually looks like. The conversation is grounded in her own founding story, extended into the strategic questions around AI, regulation, and the shape of the US immigrant-fintech landscape over the next three years.

The founding story is the product story: #

Kristy moved from Seoul to the United States on her own at age 11. She studied hard, went to UC Berkeley, landed a job in investment banking in San Francisco — the linear version of the American dream she had grown up hearing about. She checked every box. Then she was rejected repeatedly for apartments, and later for a car loan at a Lexus dealership. She was earning a six-figure salary. She had savings. And the credit system did not know how to read her. That specific moment — the gap between what she had actually built and what the system could see — is the entire product thesis. TomoCredit was built for the millions of other Kristys the system was designed to miss.

“Cash rich, credit poor” is positioning, not a diagnosis: #

The product slogan TomoCredit uses — cash rich, credit poor — is deliberate. It reframes what the credit system treats as a customer failure into a system limitation. Kristy is direct about why the wording matters. If you tell an immigrant customer that they have a credit problem, you confirm the shame they already feel about being illegible to the system. If you tell them the system cannot see their actual financial picture, you shift the framing. TomoCredit’s underwriting analyses cash flow — money coming in, money going out, patterns over time — and assigns a score that captures what the customer has always been able to see about themselves. The system is what needed the technology, not the customer.

Save-first cultures collide with a borrow-first system: #

The cultural layer under this problem is worth naming. In many countries — Korea being Kristy’s example, but the pattern extends far more broadly — the financial culture is save-first. You do not borrow. You build savings, live within your means, and treat debt as something to avoid. The US credit system operates on the opposite premise: you cannot build a credit history unless you borrow. Immigrants who arrive with responsible at-home behaviour end up penalised for it. It is not just an information gap or a technical mismatch. It is a fundamental cultural collision, and fintech products that do not account for it end up unwittingly asking customers to abandon financial discipline in order to become legible to a system that then rewards them for having done it. Products designed correctly should not ask customers to change who they are.

The outsider mindset was an advantage: #

Asked how she navigated the notoriously complex US regulatory environment — federal plus state, consumer protection layered on top of banking regulation — Kristy is clear that not knowing the “right” way to do things helped her. Coming from investment banking and tech rather than traditional retail banking meant she was not carrying industry assumptions about what was possible. She was not jaded. She was not too skeptical. The mental model she brought was: regulators and policymakers are people. Rules evolve. If you engage constructively, follow the framework as it currently exists, and provide the evidence that a better approach helps consumers, you can influence how the system evolves over time. Not every regulator has considered immigrants without credit history when writing the rules — but they will, and founders who show up with the data and the customer stories are how that happens.

AI as coaching, not decisioning — where the regulatory line sits: #

One of the more useful practical distinctions in the conversation is around where AI can operate freely in financial services and where it cannot. Making a lending decision or determining a credit score is heavily regulated territory that requires audit trails, explainability, and human oversight. Coaching a customer through what their credit score means, why it moved, and what specific behaviours would improve it is a different category. It is closer to financial wellness, less heavily regulated, and — increasingly — an area where AI produces genuine value. TomoCredit’s strategy is to lean into the coaching layer, where users can upload their financial picture, get plain-English analysis, and understand what to do next. The distinction between the regulated decisioning layer and the coaching layer is one operators building in this space should be thinking about carefully.

Three customer profiles, three different products: #

A recurring point Kristy makes is that “immigrant” is not a monolithic customer segment. She identifies at least three distinct profiles her team designs for. Expats and skilled workers arrive with established income and savings — their credit problem is being unable to access meaningful borrowing capacity, not being unable to access $500 in an emergency. Immigrants from lower-income backgrounds face a different problem: cash liquidity for near-term needs, without the borrowing history to unlock even small credit lines. And native-born Americans with thin credit files — often younger customers who have avoided traditional debt — face yet another version of the same underlying gap. Two customers with the same credit score can be radically different in cash position, savings behaviour, and product need. The current system flattens all three into one number.

Cash flow underwriting is finally becoming mainstream: #

The strategic reframe in this section of the conversation is that Kristy started building on cash flow underwriting when it was still a niche approach. Institutions treated it with skepticism. Today, largely driven by AI, cash flow underwriting is becoming the mainstream approach. Buy Now Pay Later operators use it. Neobanks use it. Alternative lenders use it. A wave of well-funded, immigrant-focused fintechs has raised serious capital over the last 18 months. This is not a headwind for TomoCredit — Kristy argues that the mainstreaming is good for the entire category. The size of the US market, with more than 20,000 banks and hundreds of millions of consumers, means competition between fintechs matters far less than the shared work of reshaping how the system reads people. Multiple companies will succeed. The one that positions itself as an expert on a specific segment will win that segment.

Fragmentation is good news for startups: #

A subtle point that pushes back against conventional wisdom: US market fragmentation, which most fintech operators complain about, is a gift to startups. Kristy references the alternative — the Korean super-app model, where one dominant player like KakaoBank or Toss handles everything from investing to bank accounts to money transfers. Consumer-side, that model is convenient. Founder-side, it is nearly impossible to break into. In the US, the fragmented landscape means a startup does not have to be excellent at everything. It can be excellent at one thing. That is a much winnable competitive position. Founders considering where to build should think carefully about this tradeoff.

The Jamie Dimon principle — specialists beat generalists: #

The closing line of the episode has become an internal mantra at TomoCredit and is one of the sharper founder framings in fintech this year. Kristy does not need to outsmart Jamie Dimon at running a global bank. She needs to outsmart Jamie Dimon at one specific thing — understanding thin-file and no-file customers, and building products designed around their actual financial lives. On that specific dimension, Kristy argues her team has more expertise than the world’s largest banks. This is more than founder confidence. It is a genuine strategic principle. Incumbents optimise for breadth. Startups win by being unarguably better at a specific segment. When the segment is 30 million people, that specific-better-than approach becomes a real business.

Why listen: #

This episode is one of the more clear-eyed founder conversations in fintech this year. Kristy brings the rare combination of personal experience with the problem, operational depth in solving it, and the willingness to reframe the industry conversation rather than just describe it. For founders building in underserved credit markets, product leaders at banks thinking about how to serve the thin-file population, and anyone interested in how AI is reshaping the boundary between financial coaching and financial decisioning, the episode offers substance without the usual fintech marketing gloss. The “cash rich, credit poor” reframe alone is worth internalising — it is one of the cleanest positioning statements in the current fintech landscape.