Predictions tell us what’s likely to happen. Resolutions tell us what we want to happen.
On their own, neither is enough.
Predictions without resolutions turn into excuses — reasons to accept outcomes we don’t actually like. Resolutions without predictions turn into wishful thinking — aspirations untethered from reality.
A priority is what you get when you combine the two.
Each priority below takes one or more predictions as a given, anchors them to one or more of the resolutions above, and translates that combination into a concrete area of focus for 2026.
They’re not guarantees. They’re not silver bullets. They’re bets about where effort is most justified, even when the odds are uncertain.
With that in mind, here are my priorities for banking and fintech in 2026:
1. Sustain open banking progress without regulatory clarity.
Predictions: U.S. open banking stalls (and Canada advances).
Resolutions: Leave the ecosystem better than you found it. | Do not lose to Canada.
Regulatory clarity is unlikely to arrive in the U.S. open banking market in 2026. But that doesn’t mean progress has to stop.
Even under a much more proactive and well-staffed CFPB, there were always limits to what rulemaking alone could accomplish. Section 1033 of Dodd-Frank simply isn’t very detailed or prescriptive. And now, in a post-Chevron world, any attempt by regulators to stretch beyond the statute is almost guaranteed to invite immediate legal challenges.
The reality is that many of the hardest problems in open banking — technical integration, information security, liability allocation, third-party risk management — can’t be solved by regulation alone anyway. They require cooperation between private companies on all sides of the market. Much of that work has already been happening, piece by piece, through bilateral commercial agreements and industry standard-setting efforts like the Financial Data Exchange (FDX).
Right now, tensions in the open banking ecosystem are high, and trust is in short supply. But as the business benefits of open banking become even more apparent — especially to banks — the incentives to cooperate will grow. That cooperation may be reluctant, uneven, and occasionally combative, but it’ll still be progress.
Keeping the ball moving in 2026 matters because it puts the industry in a far stronger position when the regulatory logjam inevitably breaks. The goal isn’t to wait for permission. It’s to be ready. As MX says in its piece:
… the demand for Open Banking is unmistakable: consumers are asking for more control over their financial data, and institutions want to modernize, reduce friction, and replace legacy data access methods with secure, API-based connectivity. Market momentum hasn’t slowed — it’s simply waiting for regulatory alignment. The winners will be the financial institutions that use this moment to modernize their data infrastructure, invest in secure APIs, and be ready to scale the moment regulatory alignment arrives.
Also, I would really prefer not to spend the next few years having my Canadian readers explain to me why their system works better than ours.
2. Invest early in AI governance and model risk management.
Predictions: LLMs remain constrained in high-stakes decisions. | Supervision loosens, confidence rises.
Resolutions: Build companies that will last.
Because AI adoption is accelerating faster than regulatory scrutiny, companies have a narrow window to invest before the stakes rise.
Right now, regulators are more focused on encouraging innovation than reining in excesses (not a criticism — just an observation). Fair lending and model risk management don’t feel urgent. And, for the most part, LLMs still aren’t being used to make high-stakes customer decisions directly.
That combination makes this the perfect time to invest.
Companies that wait until regulators care — or until AI systems are already embedded in customer-facing decisions — will find themselves scrambling. Retrofitting governance, explainability, and fairness onto systems that are already in production is expensive, disruptive, and often ineffective.
By contrast, companies that invest early in model risk management, data lineage, explainability, and fairness testing will build real internal muscle memory. They’ll understand where AI helps, where it doesn’t, and where humans need to stay firmly in the loop.
Investing countercyclically feels unintuitive in the moment. In hindsight, it’s what separates durable institutions from cautionary tales.
3. Make stablecoin-powered money visible and legible.
Predictions: Stablecoin adoption accelerates (unevenly).
Resolutions: Defeat financial nihilism.
When money becomes harder to see, trust erodes.
As stablecoins increasingly sit underneath consumer-facing financial products — especially in BaaS, embedded finance, and global money movement — users will often have no idea what rails their money is actually running on.
If money is spread across accounts powered by different technologies, issuers, and settlement systems, consumers lose the ability to see their full financial picture clearly. And when people can’t see or understand where their money is, nihilism sets in fast.
We’ve been here before. Open banking data aggregation helped restore coherence in a fragmented account landscape by giving consumers a unified view, even when the underlying infrastructure was messy.
That same functionality needs to be extended to stablecoins.
Practically speaking, that means aggregation, transparency, and standardized, permissioned access to account and transaction data (starting with read-only access, given the irreversibility of most crypto transactions).
If stablecoins are going to play a meaningful role in everyday finance, they need to make money systems feel more navigable, not less.
4. Build responsible consumer risk management tools.
Predictions: Speculation becomes easier to package and scale.
Resolutions: Defeat financial nihilism. | Focus on long-term customer outcomes.
If speculation is going to keep expanding — and it is — then ignoring it or scolding consumers for participating isn’t a strategy.
What is worth taking seriously is the motivation behind the behavior. Many consumers aren’t chasing excitement so much as trying to cope with uncertainty: volatile markets, unstable income, rising housing costs, and a general sense that the financial ground keeps shifting beneath them.
Betting on a football game or Time’s Person of the Year isn’t an effective hedge. But the underlying motivation is rational. The failure isn’t on the consumer side — it’s that we’ve given them very few responsible tools to manage risk in the first place.
We now have more primitives than ever to work with: real-time cash-flow data, AI, innovative insurance products like payment protection insurance, and yes, even prediction markets. Used thoughtfully, these tools can be combined into consumer-facing risk management experiences that reduce fragility rather than amplify it (I walked through one hypothetical example, focused on housing risk, here).
If we don’t build and market these products intentionally, someone else will build and market worse versions of them. And those versions will almost certainly reinforce the idea that the only rational response to uncertainty is to gamble.
5. Normalize self-exclusion in financial products.
Predictions: Speculation becomes easier to package and scale.
Resolutions: Defeat financial nihilism.
If speculative financial products are going to be widely available, consumers should have better tools to protect themselves from their own worst impulses.
Self-exclusion is a well-tested concept in gaming. People can proactively set limits, lock themselves out, or require cooling-off periods — especially when they know their future judgment may be worse than their present judgment. Financial services should have adopted this approach years ago.
Some banks already have. In the UK, many institutions offer ‘gambling blocks’ that let customers voluntarily block gambling transactions. In the U.S. — where mainstream access to gambling is newer — banks largely haven’t taken this step.
They should. And they should extend the same logic beyond cards to ACH transfers, wires, and real-time payments flowing to speculative platforms. Why shouldn’t a consumer be able to say, in advance, “Don’t let me send money to Robinhood, Coinbase, Kalshi, or DraftKings after midnight,” or “Require a 24-hour delay before I can move funds into this account”?
As speculative platforms increasingly go after direct deposits, this may soon stop being just a consumer issue and start becoming an employer one as well (Chime’s Enterprise division might want to kick the tires here).
If we’re serious about helping people make better financial decisions over time, giving them tools to pre-commit — and to protect themselves in moments of weakness — is an obvious place to start.
6. Make internal scoring models legible to customers.
Predictions: Credit scoring fragments faster than consumer understanding.
Resolutions: Defeat financial nihilism. | Leave the ecosystem better than you found it.
As credit scoring fragments, consumers are increasingly unsure which signals actually matter — and that confusion breeds cynicism.
The old promise — “this is your credit score, and here’s how to improve it” — no longer holds. There is no single, universal answer anymore. Pretending otherwise only deepens the sense that the system is arbitrary and rigged.
You can’t solve this problem for the entire ecosystem. But you can solve it within your own walls.
Cash App has shown the way. By exposing its internal scoring logic, explaining what behaviors matter, and showing customers how their actions translate into outcomes, it replaces mystery with legibility. That clarity builds trust and gives customers a sense that the system is navigable, not capricious.
Opacity feeds financial nihilism. Transparency fights it. Making internal models customer-facing won’t just reduce confusion — it creates better engagement, more informed cross-sell, and stronger long-term relationships.
And, the value of transparency extends beyond just credit scoring. Consumers are overwhelmed by multiple financial accounts at multiple providers, unclear transaction data, and a constant stream of generalized information about how they should manage their finances. They want a financial partner that helps them make sense of the noise and then actually do something. This is exactly the environment where clean transaction data and consistent categorization — something MX has long focused on — becomes a prerequisite for meaningful guidance, not just noise.
7. Use agentic AI to optimize consumer savings outcomes.
Predictions: Agentic commerce hype outpaces real consumer demand.
Resolutions: Focus on long-term customer outcomes.
If agentic AI is going to matter for consumers, it shouldn’t start with spending.
What people actually struggle with isn’t finding new ways to transact — it’s saving consistently and ensuring their savings compound as much as possible over time. Inertia, not intent, is the real enemy.
This is where agentic AI could deliver genuine value. A well-designed savings optimizer could continuously monitor cash flow, balances, rates, and constraints, and help consumers maximize yield without constant manual intervention. Not by taking full control (at least not yet), but by removing friction, surfacing tradeoffs, and nudging money toward its most productive place.
We already know the upside is real. Small improvements in yield compound meaningfully over time, especially for households living close to the margin. Yet most consumers leave real money on the table simply because optimization is tedious.
It’s not obvious that banks or fintechs are best positioned to build this. It may come from a company with different incentives and distribution — a Credit Karma or Apple or Walmart.
If agentic AI is going to justify its hype, perhaps we should start by helping people earn more on the money they already have.
8. Shift loyalty from products to relationships.
Predictions: Competition intensifies through simultaneous entry and exit.
Resolutions: Build companies that will last.
In industries that have already become highly competitive, we can see what winning looks like. Airlines are the clearest example.
Loyalty programs like SkyMiles don’t just reward transactions — they reward relationships. They create gravity. They align incentives across the business and make the best customers feel known and valued.
Banking is now entering a similar phase. Switching costs are falling. New entrants keep showing up. Distribution advantages that once felt permanent are eroding.
Banks should be well-positioned to respond. They sit on decades-long customer relationships, rich behavioral data, and frequent touchpoints across a customer’s financial life.
But much of that advantage is trapped. Legacy core systems, fragmented data architectures, and product-centric operating models make it hard to see customers holistically — let alone reward them consistently across products. Data that could power relationship-based loyalty is siloed, stale, or operationally inaccessible.
So the constraint isn’t just urgency. It’s also translation.
In a louder, more competitive environment, durable winners will be the institutions that break out of product silos and use their data to recognize, reward, and deepen relationships over time. Loyalty won’t come from any single product feature. It will come from making customers feel understood — and making that understanding economically meaningful.
9. Design multi-player banking for aging households.
Predictions: The “great wealth transfer” unfolds as a long transition.
Resolutions: Focus on long-term customer outcomes.
Most financial products are still designed for a single user making decisions in isolation. That model breaks down quickly as people age.
What’s far more common in real life is a gradual transition: adult children helping parents manage bills, monitor accounts, avoid scams, and make sense of increasingly complex financial decisions — often informally, often under stress, and often without the right tools.
Today, families are forced to hack together solutions using shared passwords, ad-hoc account access, or full legal instruments like powers of attorney that are either too blunt or too late. None of that reflects how financial responsibility actually shifts over time.
The opportunity here isn’t capturing a future inheritance. It’s building trust during a long transition — by enabling shared visibility, graduated permissions, alerts, and safeguards that preserve independence while reducing risk.
Products that solve these coordination problems won’t just serve aging parents better. They’ll earn the trust of the next generation, too. And that’s what durable, outcome-oriented banking relationships actually look like.
(Editor’s Note — I wrote, in a lot more depth, about the importance of life stage-based product design in a different sponsored deep dive essay with MX, which you can read here.)
10. Reintroduce place into digital financial products.
Predictions: Competition intensifies through simultaneous entry and exit.
Resolutions: Defeat financial nihilism. | Build companies that will last.
As financial products become more centralized and interchangeable, they also become easier to distrust.
One promising countertrend is the reintroduction of place into digital finance.
Companies like Bilt and Block are experimenting with ways to embed local geography, merchants, and communities into financial products — not as nostalgia, but as a way to create relevance, loyalty, and real-world utility. These efforts recognize something important: people don’t experience their financial lives in the abstract. They experience them where they live.
For community banks in particular, this should be a wake-up call. As consolidation accelerates, “local” can’t just be a slogan or a branch footprint. It has to show up in product — through local rewards, partnerships, data, and networks that reflect how money actually moves within a neighborhood.
Done well, neighborhood-scale financial networks create positive feedback loops: stronger local economies, deeper customer loyalty, and a clearer sense that participating in the financial system actually benefits the places people care about.
