Podcast 158: Are banks becoming irrelevant in the AI era? With Theodora Lau

For most of the last decade, the question facing digital banks was operational. Better app. Faster onboarding. Lower cost-to-serve.

In this episode of the Fintech Garden Podcast, Theodora Lau argues the question has moved. The competitive frontier in 2026 is no longer between banks. It is between the entire banking industry and the AI tools that customers now use as their first financial advisor.

JD Power’s most recent data: 53% of US consumers have asked AI for financial advice in the past three months.

Most of those conversations happen without the bank.

We’re in the iPhone Moment for AI

Theo opens with an analogy from her own life.

She was working for a mobile carrier in 2006–2007 when she first saw the iPhone. She lined up at the store on launch day. She came back from an overseas trip a week later with $300 in roaming charges, because she could not stop opening the weather app.

That obsession felt trivial at the time.

In retrospect, it was the signal. A new form factor was about to restructure how people related to information, commerce, and money. Most operators in 2007 did not recognize it.

AI is in the equivalent moment now.

The technology has been embedded in financial systems for years. What is changing is not the capability. It is the relationship — who controls the user’s path to a financial decision, and how that path is shaped.

The institutions that built their value around being the customer’s first call are watching the call go elsewhere.

Banks Know Mechanics. AI Demands They Understand Intent.

A persistent gap in financial product design: banks know what customers transact, but not why.

What was paid. When. Where. In which currency. To whom.

These mechanics are recorded with near-perfect precision.

The intent behind them is almost entirely missing.

Was the user buying dinner or restocking inventory? Saving for a holiday or covering an emergency? Pursuing a long-term goal or reacting to a short-term shock?

AI’s value in financial services is not primarily automation. It is orchestration — pulling the data banks already hold around the user’s actual life and surfacing it back as something contextually useful.

The product mindset has to flip.

Not “what can I sell you?” “What do you need, and how do I meet you there?”

This is the change most product roadmaps still do not reflect.

Two Things Every Bank Should Be Doing in the Next 24 Months

Theo’s answer to the “what now” question is operational.

Get the data foundation right. When customer information lives across 20 or 30 systems, no AI strategy produces useful output. The retrieval fails before the model gets to reason. “What is your data strategy” is the first question Theo asks operators.

Update the systems behind the strategy. Customer expectations on speed are no longer negotiable. Real-time service is the floor, not the ceiling. If the underlying systems cannot deliver it, the AI layer on top cannot save the experience.

There is a third priority operators talk about less than they should.

The org has to change. Most banks discuss leveraging AI in the workforce of the future. Few have started the actual reskilling, upskilling, and structural reorganization that comes with it. The cultural and training work runs in parallel with the technical work, not sequentially after it.

Banks that delay the organizational change pay a compounding cost.

Transparency Is Non-Negotiable

Financial services is a regulated industry. That constraint is the asset most often misread as a burden.

Whether a human or an algorithm makes a lending decision, it requires the same things.

Audit trails. Explainability. An expert-in-the-loop at the final checkpoint.

Theo uses the highway analogy: drivers need traffic rules, speed limits, and signals before they adopt a road at scale. The same is true for financial products.

AI deployments that cannot be defended in front of a regulator are products that cannot scale. AI deployments that can be explained back to the customer are products that earn trust.

Both halves matter. The regulator side is the floor. The customer side is the moat.

The AI-Native Bank Isn’t About the Interface

Asked what an AI-native bank should actually look like in five to ten years, Theo declines to name a form factor.

Mobile app. Chatbot. Multi-channel. Branch with AI underneath. All possible.

The defining characteristic is not the interface.

It is the depth of understanding the institution has of the customer. Anticipating needs rather than reacting to them. Knowing what the customer’s financial life looks like rather than presenting a product catalogue.

The mistake operators make is treating “AI-native” as a UX direction.

It is a relationship and a data direction.

The interface will follow whatever surface the customer happens to be using. If that surface is a personal AI assistant, the interface is conversational. If it is a branch visit, the interface is the conversation with the staff member. If it is a mobile app, the interface is the app.

The bank’s job is to be useful through all of them.

Where Humans Still Matter

The conversation is explicit on the limits.

Wealth management decisions should keep humans in the loop. Credit decisioning needs an expert at the final checkpoint. Anything that has to defend itself to a regulator needs a human signature, not just an algorithmic one.

And one Theo emphasizes that operators routinely underestimate.

A meaningful share of people who call their credit union are not calling because they have a question. They are calling because they want to talk to a human.

When Igor pushes back — suggesting bots are becoming good enough that the distinction may not matter — Theo’s response lands hard.

“Would you like to replace yourself with a bot and have the bot interview me?”

The point is not nostalgia. Some interactions exist precisely because the human element is the value, not the inefficiency.

The financial institutions that strip those interactions in pursuit of automation savings tend to discover the cost in churn, complaint volume, and brand erosion downstream.

The Real Question: Are Banks Even in the Conversation?

This is the part of the episode that should make every bank product team uncomfortable.

A US consumer asking for financial advice in 2026 is increasingly:

Uploading a CSV of their finances to ChatGPT. Asking Claude for a debt-reduction plan. Asking Perplexity to compare digital banks. Asking the frontier AI of their choice for a credit-score improvement strategy.

The conversation runs end-to-end between the user and the AI model.

The bank may surface in the answer. The bank may not.

If it does not, the customer’s decision is made without it.

For acquisition, this is structural. For retention, this is structural. For brand, this is structural.

The strategic question has shifted from “how do we win against competitors” to “how do we show up at all.”

From SEO to GEO

The acquisition model that fintech operators built over the last decade depends on the customer encountering a message.

A search result. A notification. An ad. An in-app prompt.

AI agents do not see messages. They synthesize answers.

The industry is starting to use the term Generative Engine Optimization — GEO — as the successor to SEO. The rulebook is mostly empty.

There is no clear advertising market. There is no settled mechanism for showing up in answers. There is no agreed methodology for measuring presence.

It is wild-west territory. The companies that figure it out first will own a layer of customer access that most banks are not yet tracking.

Theo’s framing is honest: “I don’t think anyone has the answer. Or if you do, you’ll be the next billionaire.”

That is not deflection. It is an accurate description of where the industry actually is.

The New Street Belongs to Whoever AI Promotes

High street banks earned the name because they owned the streets. Customers walked past. Customers walked in.

Mobile changed the geography. Apple and Google owned the new street. Banks rented application slots in the app stores. Approval, compliance, and distribution all sat with the platform owner.

The AI-native era moves the street again.

OpenAI. Anthropic. The frontier model providers. Or — Theo’s sharper framing — whichever surface the AI happens to be promoting.

Banks do not need to be acquired by the AI providers. They need to be visible in the answers AI provides.

The institutions that figure out which signals matter — content, partnerships, data presence, structured availability — will own the next decade.

The institutions that do not, will not.

And the Question No One Is Asking

Theo closes on a point that is harder to write a product strategy around, but harder to ignore.

The industry conversation about AI is dominated by one frame.

Efficiency. Streamlining. Doing more with less.

Layoffs at the largest banks are running into the tens of thousands. Smaller institutions are not far behind. The press releases describe “operating model transformations” and “AI-enabled productivity gains.” The substance is the same.

Theo’s closing question is whether the design conversation is balanced.

Whether the same energy directed at automation is also being directed at:

The populations who do not want to interact with AI. The workers whose roles are being eliminated. The communities that may not benefit from the model that the industry is building toward.

This is not the standard fintech closing. It is the most important one.

Shareholder value and sustainable business models are real concerns. They are not the only concerns.

An industry that forgets the second category eventually loses the first.