Hey, fintech fam 💜

I’m deep in FTW: San Francisco agenda mode right now, and let me tell you, it’s getting good. 👀 More speakers, sessions, and programming are dropping very soon.

But first, we have fintech news to discuss.

And because you made it here, I’ve got a special FTW: SF discount code waiting for you at the bottom of today’s newsletter after you catch up on the stories shaping our industry this week.

Let’s get into it. ✨

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Every Thursday, I break down the fintech stories that matter most — grounded in my reporting, interviews with industry leaders, and what I’m seeing unfold across the industry.

We spend a lot of time talking about the ambitious things AI agents could eventually do in financial services.

Move money. Make purchases. Monitor portfolios. Detect fraud.

But one of the most useful AI agents in wealth management might start with something simpler: fixing the paperwork.

Zeplyn.ai, led by Era Jain, Co-Founder and CEO, has launched an integration that brings AI into the Schwab account-opening process, allowing its agent to populate Schwab's digital account-opening workflow and prepare applications for advisor review and submission.

The agent can also bring holdings and transaction data into advisor briefs, connecting more of the administrative work surrounding a client relationship.

Importantly, the advisor stays in the loop.

Rather than allowing an agent to open an account autonomously, the system prepares the application for a human to review and ultimately submit.

Early pilots reportedly reduced Not-In-Good-Order (NIGO) submissions by roughly 80%.

For anyone outside of wealth management, “not in good order” sounds like some obscure compliance term.

For advisors, it's a very real operational headache.

An account application arrives with missing information, an incorrect field, a signature issue, or another error that prevents it from being processed. Someone has to find the problem, contact the client, fix it, and send everything back through.

Multiply that across hundreds or thousands of accounts and suddenly a tiny paperwork problem becomes an expensive workflow problem.

And that is exactly where AI agents start proving their value.

Yesterday, I published a new Forbes column about why the future of AI in fintech is about more than the model.

This is what I mean.

The enterprise AI conversation is quickly moving beyond whether a model can produce an impressive answer toward whether it can reliably complete a specific job inside an existing business.

Zeplyn isn't asking an AI agent to replace the financial advisor.

It's asking the agent to handle pieces of the process that advisors probably don't want to spend their time doing anyway.

Zeplyn's existing meeting assistant already automates preparation, note-taking, follow-ups, task management, and updates to systems of record. Schwab's Advisor Services marketplace says the product can save advisors more than 12 hours per week.

Account opening takes that proposition another step downstream.

Now AI isn't only summarizing what happened in the client meeting. It's beginning to help execute the next operational workflow.

The winning financial AI agent may not replace the advisor. It may fix the paperwork.

And honestly? That's a much more believable near-term future for agentic AI in wealth management.

The most compelling enterprise use cases I'm seeing aren't necessarily the ones handing an AI agent complete autonomy (at least, not yet).

They're narrow, repetitive workflows where the rules are relatively clear, the administrative burden is high, and a human remains responsible for the consequential decision.

That's especially important in wealth management, where advisors are ultimately being paid for judgment, relationships, and financial expertise, not their ability to copy information between systems.

If AI can give advisors more time to actually advise while reducing operational errors along the way, that's a pretty easy ROI story to understand.

Prediction markets have spent the past year fighting for consumer attention.

The next phase could be much bigger: what if consumers don't have to visit a prediction market at all?

Apex Fintech Solutions announced it is pushing prediction markets deeper into brokerage infrastructure, allowing financial platforms to integrate event contracts through APIs rather than build all of the futures infrastructure themselves.

We've already gotten a glimpse of where this can go.

tastytrade recently became the first brokerage to use Apex's turnkey futures commission merchant infrastructure for prediction markets, putting event contracts alongside the stocks, options, and futures its customers already trade.

Now add Kalshi into the infrastructure equation, and the bigger distribution opportunity becomes pretty obvious.

Prediction markets are becoming an embeddable financial product.

Instead of asking someone to discover a prediction-market platform, create another account, fund it, and build a new financial habit, event contracts can increasingly appear inside financial products where customers already have money.

That's a completely different distribution game.

And prediction markets have plenty of momentum to distribute.

During this summer's World Cup, Kalshi recorded roughly $27 billion in trading volume and attracted around 3 million users, according to Reuters.

But rapid growth is also bringing much greater scrutiny.

This week, Kalshi announced a multi-year partnership with Nasdaq to use its market-surveillance technology to help detect potential manipulation and market abuse. Kalshi has been expanding its surveillance capabilities as regulators scrutinize insider trading and other risks across prediction markets.

That matters even more if these products are about to become easier to distribute.

The interesting question isn't simply whether prediction markets keep growing.

It's what happens when prediction markets stop being a destination and become a feature.

Imagine opening the same app where you hold stocks and ETFs and seeing contracts on interest rates, inflation, commodity prices, elections, sports, or other real-world outcomes.

Suddenly the lines between investing, hedging, information, speculation, and entertainment get considerably blurrier.

That creates a huge distribution opportunity for prediction markets but also places more responsibility on the infrastructure beneath them.

Who determines which contracts appear? What disclosures surround them? How do platforms think about suitability? Market manipulation? Gamification?

And perhaps most interestingly: who owns the customer?

Prediction-market companies may build the markets and liquidity while brokerage platforms increasingly control the interface through which consumers encounter them.

We've seen this movie elsewhere in fintech.

When a financial product becomes an API, distribution can move very quickly.

I did not have “the Starbucks wallet becomes relevant to agentic AI” on my fintech bingo card this week, but here we are. 😆

Rain announced Wednesday that it acquired Ansa, a startup building branded stored-value infrastructure for merchants. Ansa founder Sophia Goldberg is joining Rain as Head of Payments.

The basic idea behind Ansa is incredibly easy to understand.

Think about the money sitting inside your Starbucks app.

Customers load money into a branded wallet, then return to the same merchant to spend it. For the merchant, that can mean lower payment costs, more loyalty, and a financial relationship with the customer that extends beyond a single transaction.

As Goldberg put it: “Every great merchant or consumer brand eventually wants their own version of the Starbucks wallet.”

The problem is that building one is hard.

Merchants already have point-of-sale systems, payments infrastructure, reconciliation processes, fraud controls, rewards programs, and plenty of other technology they don't particularly want to rip apart.

Ansa's interesting solution was to work with the rails merchants already use.

The company struck an arrangement with Mastercard that allowed customers to spend a stored balance in-store through existing Mastercard terminals, while online and in-app transactions could be authorized directly through Ansa's API.

Under Ansa, that balance was still closed-loop: money loaded for a particular merchant stayed with that merchant.

Rain wants to take the concept further.

Combined with Rain's Visa and Mastercard relationships, the company says partners will eventually be able to extend stored balances beyond one merchant's registers to participating businesses elsewhere.

And then things get really interesting.

Rain explicitly connected the Ansa acquisition to something else it has been building: agentic payments.

The connection actually makes a lot of sense.

Ansa has spent years building programmable balances with rules attached to them:

Who can spend?

Where can they spend?

How much can they spend?

How long does that money remain available?

Those are remarkably similar questions to the ones fintech is now trying to answer for AI agents.

Rain is already working on controlled agentic payments that give an AI agent a limited budget rather than unrestricted access to someone's money. The company argues Ansa's experience building rules around stored balances can become part of that infrastructure.

In other words:

The infrastructure that controls how humans spend stored value might also help determine how AI agents spend our money.

This acquisition is a great example of how technologies built for one era of fintech can become the infrastructure for the next.

Stored value was largely a merchant-loyalty-and-payments story.

Programmable money turns it into something bigger.

If AI agents are eventually going to transact on behalf of consumers and businesses, giving them access to an unrestricted bank account or card isn't a particularly comforting proposition.

They'll need boundaries.

Give this agent $500. Let it spend only with these merchants. Don't let it exceed $100 per transaction. Kill its spending authority after Friday.

Suddenly the boring mechanics of stored value start looking a lot like the permission layer for agentic commerce.

And I suspect we're going to hear a lot more about that.

There’s a reason these are the stories we're paying attention to.

AI agents are entering financial workflows.

Prediction markets are moving into brokerage infrastructure.

Payments are becoming programmable enough that eventually humans may not be the only ones initiating transactions.

Fintech infrastructure is being rewritten in real time.

And we're bringing the people building it together in San Francisco.

Fintech Week: San Francisco is happening September 29 through October 1, bringing 500+ fintech leaders together to talk about agentic AI, payments, security, banking, infrastructure, and where our industry goes next.

Since you’ve made it to the bottom of this newsletter, here’s a gift: 20% off passes to the event!

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That wraps up today’s edition—thanks for reading! Until next week, keep innovating and challenging the status quo.

See you Tuesday!

Love,

Nicole 💜