Why AI Could Change Fintech Valuation More Than Fintech Itself

For much of the past decade, the European and global venture landscape ran on a familiar disruption narrative, in which capital was readily available, and execution speed could obscure structural fragility. That period now appears to be ending.

One perspective on this shifting frontier comes from Michelle Luan, a London-based investment professional who has spent the past five years working across technology-enabled businesses in natural resources, consumer sectors, finance, AI, and agentic commerce.

“Founders walked into pitch meetings armed with hockey-stick growth projections, heavily insulated by complex software jargon, and braced for a standard programmatic tussle over revenue multiples,” Luan says. 

The era is closing, and not because the metrics changed. The next chapter of the sector will not be defined merely by the software it ships, but by how artificial intelligence acts as an additional lens on the valuation process itself. While founders race to embed generative models into their product, institutional investors are quietly using similar technologies to strip away the narrative veneer of pitch decks and go straight to the structural plumbing: the diligence itself.

“AI may reshape the financial mechanics of company valuation as much as it reshapes the underlying products,” Luan says. “Valuation was never really about the number a founder puts forward. It is about how much of the founder’s story an investor believes. AI changes how fast and how thoroughly that story gets tested. Diligence that used to take a team several weeks: reading the data room, modelling the capitalisation (cap) table, and checking the regulatory position, increasingly takes days. The risks that used to stay hidden until the term sheet gets found in the first week are priced in.”

The claim sounds backwards until you follow the mechanics. When a credit committee or a venture board pushes back on price, the resistance rarely starts with the valuation metric itself. The harder question, the one that quietly kills more transactions than price ever does, is asked much earlier, and out of the founder’s earshot: Is this company safe to back?

Investors do not simply buy a growth story at face value. They stress-test every assumption, every macroeconomic exposure, and every regulatory claim. As machine learning accelerates the velocity, granularity, and depth of that stress-testing, the pillars of structural de-risking are no longer static checklists worked through by hand over weeks. They are inputs that automated diligence will surface, benchmark, and discount within days of a data room opening.

Regulation: Defusing the Boundary Risk

For any technology-enabled enterprise operating in the modern financial sector, the regulatory cross-examination arrives with brutal speed. “Is the company properly authorised by the Financial Conduct Authority or its equivalent, does it need to be, and if a licence takes long enough to get, will the delay cost the business its edge?” Luan says.

AI cuts into this risk from two directions, and founders tend to see only one of them coming.

“Investors no longer have to take the regulatory story on trust,” Luan says. “Authorisation registers, enforcement actions, the gap between what the deck claims and what the regulator’s records show; that comparison used to take a specialist. Increasingly, it is one of the first checks an AI-assisted diligence process can run.”

The second direction is newer. A fintech using machine learning in credit decisions, fraud detection or customer interaction now has AI-specific obligations of its own to map: the EU AI Act for anyone touching European customers and growing supervisory expectations in the UK around how AI-driven decisions are explained and governed. 

“The perimeter has doubled,” Luan says. “Financial services rules on one side, AI rules on the other, and investors expect a credible read on both.”

Capitalisation: The Legibility of Control

Once investors validate an operating model, they pivot to a deeper vulnerability: who genuinely holds ultimate voting control over the corporate vehicle? The pitch deck does not always make this clear, whereas the cap table tends to be more revealing.

“An early advisor sitting on 8% for old work, a departed co-founder still holding equity, a stack of SAFEs that may not have been modelled through conversion: none of these is fatal on its own,” Luan says. “But together they tell an investor that control is uncertain, and uncertain control means a harder time getting decisions made later.”

“What AI changes is who finds the mess and how early,” Luan says. “Unmodelled SAFEs have often surfaced only in the later weeks of diligence. Now conversion scenarios can sometimes be run in hours, occasionally before a first meeting. If you have not modelled your own cap table, there is a chance someone else’s tools will, with the results feeding into the offer.”

Governance: Proving the Infrastructure Outlives the Key Person

The next layer of institutional risk sits within operational governance: the precise mechanics of executive sign-offs, the independence of the board of directors, and the singular question fintech founders historically despise addressing: “What happens if the key person steps away?” Luan says.

“If an investor concludes that operations would freeze if the key person stepped away, that reliance forces the incoming capital partner to absorb a structural hazard. Investment committees will rarely ratify a substantial deployment of capital into a company that cannot survive the loss of a single point of failure,” Luan says.

“AI diligence reads documents, not people,” Luan says. “And the documents are the evidence that a company runs on process rather than on one individual. That individual is not always the founder. Often it is a CTO who holds the whole operation together, and the business works precisely because they do. I have seen it happen: a company runs well for years, then one key person leaves and it quickly stops working, as if the soul has left the body. Investors often needed months alongside a company to spot that dependency. Increasingly, it can surface in the data room.”

The consequence for undocumented companies is blunt. “If how decisions get made, who signs off, and what the board has agreed exist only in one person’s head, the tools come back blank, and the blank is the answer,” Luan says. “It suggests to the committee what it most fears: that little here moves without that person. Companies that write their governance down can make it easier to prove the one thing every investor needs to see: that the business outlives any key person.”

For AI-native companies, there is a second twist, and it moves the target. 

“If the value of the business sits in a model and the data that trains it, then the key person question becomes: who controls those, and what happens if that person leaves? Some investors are starting to ask it in those terms.”

Strategic Alignment: Articulating the Exit Paradigm

Strategic alignment surfaces late in the deal cycle, and it holds the power to dismantle multi-million-pound transactions at the final hurdle. It centres on an essential, yet frequently unasked question: do the executive team and the incoming capital partners desire the identical outcome on the exact same time horizon?

“A founder building for fifteen years and a fund needing an exit inside seven are both reasonable. Together in one deal, they are a slow-motion problem that appears at the worst possible moment, usually when the next round or a sale is on the table,” Luan says.

Here, AI can work in the founder’s favour, provided they make use of it.

“Fund behaviour is data,” Luan says. “Hold periods, exit patterns, follow-on rates, all of it is researchable, and AI tools can help compress that research, in some cases from weeks into days. Founders can now run diligence on their investors with similar rigour investors run on them. Forcing these hard alignment discussions early may feel uncomfortable, but it is far cheaper than discovering the gap after the money is in. And founders increasingly have the tools to do it.”

Timing: Mastering the Institutional Clock 

“Timing is a wild card that cuts sharply in both directions. There is the founder’s moment, whether the market is ready for what they are building. And there is the investor’s internal clock, which dictates whether a specific transaction fits the fund’s current capital deployment and vintage lifecycle. A fund can love your company and still pass because of where it sits on its own calendar,” Luan says.

That clock used to be invisible from the outside – it no longer is. 

“It used to take an insider to know that a fund was late in its deployment cycle or had already filled its allocation in your category,” Luan says. “Although AI does not remove that guesswork, it can shrink it. Signals such as vintage years, deployment pace, and portfolio construction can often be pieced together from public data rather than through weeks of warm introductions. Founders will not get a perfect read, but they will get a much better one.”

The Architect of Tomorrow’s Valuations

The overarching reality that scaling executives must confront is clear: the historical gap between a highly polished pitch presentation and broken institutional plumbing is now impossible to hide. The architect of tomorrow’s valuations is not a new metric or a new fund. It is the technology now reading every data room. 

Her conclusion, after five risks and one shifting frontier, is not that founders should fear the scrutiny. 

“The deals that close cleanly are the ones where the plumbing was sorted before anyone opened a model,” she says. “That was true before AI. What AI changes is that there is nowhere left to hide the alternative.”

Those best placed for this shift may not be the loudest storytellers, but the people who can work across both institutional finance and the technology now examining it, a combination that is still relatively uncommon. Luan is that pairing: a financial strategist trained on Wall Street, fluent in the technology Silicon Valley ships next. She spent her career reading the source code of institutional finance: the committees, the diligence, the questions asked out of the founder’s earshot. It is the kind of intersection where Luan works. 

• Capital Markets Pedigree: A former investment banker in New York and London, Luan says she advised and executed on M&A and capital-raising transactions ranging from US$500 million to US$5 billion across technology-enabled businesses in natural resources and consumer sectors. She applies the same committee-grade risk discipline across finance, AI, and agentic commerce.
• Academic Foundation: Luan’s academic grounding is anchored by an MBA from Cornell University, where she now serves as a Board Member of Cornell Club of UK. She also holds an MPA from the University of Pittsburgh and a BS in Computational Finance from Saint Vincent College.
• A Transatlantic Bridge: Luan mentors early-stage scientific founders scaling toward international markets through activator programmes in Europe and the US, putting their regulatory readiness, investor due diligence, and commercial case through the same scrutiny an investment committee would apply, before founders ever face one.

As artificial intelligence begins to audit, cross-reference, and benchmark corporate datasets, a clean and structurally sound corporate process is no longer just an administrative preference; it can be a meaningful signal to investors. In an increasingly competitive market, an advantage tends to sit with those who can translate the language of artificial intelligence into terms investors can measure. That translation is precisely the trade Michelle Luan has spent her career learning and the one she now practises at the frontier. 

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