Fintech Maximalism: AI’s New Job in Payments

A funny thing happened after the 2021 funding peak: the fintech companies that didn’t implode quietly got better.

Mark Goldberg calls it “fintech maximalism”—a phase where the fintechs built over the last 5–10 years, especially the ones that kept executing through the 2021–2024 downturn, are showing up as compounders rather than hype cycles. I buy that framing. Not because the market suddenly got nicer, but because fintech has matured into something less glamorous and more durable: infrastructure.

And once fintech becomes infrastructure, the job changes. Reliability matters more than growth-at-all-costs. Fraud pressure rises. Margin compression is real. Regulators get louder. Customers expect instant, always-on payments that don’t break during peak season. That’s where AI stops being a feature and starts being a requirement—particularly in payments infrastructure, transaction routing, risk scoring, and fraud detection.

What “fintech maximalism” actually signals

Answer first: Fintech maximalism is a sign that fintech is moving from product novelty to platform maturity—and that complexity is now the main constraint.

Goldberg’s point (as captured in the RSS summary) is subtle: the winners aren’t just surviving; they’re compounding. Compounders don’t win by launching flashy new features every quarter. They win by doing the unsexy work—improving unit economics, expanding distribution, tightening risk controls, and building systems that scale.

That shift matters for anyone building or buying fintech—especially in payments—because payments businesses are naturally maximalist:

• More payment methods
• More geographies
• More compliance regimes
• More fraud patterns
• More partner dependencies (banks, networks, processors, orchestration layers)
• More customer segments with conflicting needs

Maximalism isn’t a mood. It’s the unavoidable result of growth.

The “quiet execution” era created a different kind of fintech

Answer first: The 2021–2024 pullback forced fintech teams to prioritize resiliency, not just distribution.

During the boom, plenty of fintechs could mask weak fundamentals with cheap capital. During the winter, they had to learn basic survival skills:

• Reduce approval rates where fraud risk is concentrated
• Cut customer acquisition channels that didn’t pay back
• Improve authorization performance instead of adding “one more integration”
• Replace manual ops with automation because headcount was no longer the answer

The companies that learned those skills are now positioned to scale in 2025–2026 without breaking everything.

Payments is the proving ground for maximalism

Answer first: If you can run high-scale payments with low fraud and low cost, you can run almost anything in fintech.

Payments infrastructure is where maximalism shows up first because every growth lever increases complexity:

• Add new markets → you inherit local schemes, settlement rules, and fraud ecosystems
• Add new verticals (travel, marketplaces, subscriptions) → you inherit new dispute patterns
• Add more payment types (cards, ACH, RTP, wallets) → you inherit orchestration and reconciliation complexity

And customers don’t care how hard it is. They just want acceptance rates up, fraud down, and funding on time.

A concrete example: holiday traffic is a stress test

Answer first: Q4 load spikes expose weak routing, weak fraud controls, and brittle ops.

It’s December 2025. Many merchants are coming off their heaviest weeks of the year—promotions, gift cards, subscription renewals, and cross-border shopping. When volume spikes, three things typically happen:

1. Fraud attempts spike (especially account takeovers and card testing).
2. Issuer behavior changes (authorization becomes more conservative in certain corridors).
3. Ops teams get overwhelmed (manual review queues balloon, chargeback workflows back up).

In a maximalist environment, you can’t “staff your way out” of these problems. You need systems that adapt.

Why AI becomes essential (and not in a buzzword way)

Answer first: AI is the only practical way to manage payments complexity at scale: it improves decisions, reduces manual operations, and reacts faster than rules.

Rule-based systems still matter, but they hit a wall once:

• fraud patterns mutate daily,
• data sources multiply (device, network, behavioral, KYC/KYB, transaction graph), and
• latency constraints tighten.

AI works best in payments when it’s used for high-frequency decisions with measurable feedback loops. That’s why it fits payments infrastructure so well.

1) AI-driven fraud detection that respects conversion

Answer first: The goal isn’t “lowest fraud.” The goal is “lowest fraud at a target approval rate.”

Most companies get this wrong. They celebrate fraud losses dropping while ignoring that they accidentally killed approvals for good customers.

A mature AI fraud detection approach looks like:

• Real-time risk scoring per transaction (milliseconds matter)
• Behavioral signals (session velocity, device reputation, login anomalies)
• Graph-based features (shared identifiers across accounts, merchants, BINs)
• Adaptive step-up (3DS, OTP, manual review only when risk crosses a threshold)

If you’re operating at scale, false positives are a revenue leak. AI helps reduce those by learning which signals actually correlate with loss—by segment, corridor, and payment method.

2) Smarter transaction routing and payment orchestration

Answer first: AI routing improves authorization rates and reduces cost by choosing the best path for each transaction.

As fintech maximalism expands your options—multiple processors, multiple acquiring banks, multiple payment rails—you need logic to choose well.

AI-assisted routing typically optimizes for:

• Approval probability (issuer- and corridor-aware)
• Cost (interchange, processing fees, network fees)
• Latency and reliability (avoiding degraded endpoints)
• Policy constraints (geo rules, MCC restrictions, risk appetite)

This isn’t theoretical. Even small improvements compound. A 0.5% lift in authorization rate can be massive at scale, especially for thin-margin merchants.

3) Automated dispute handling and chargeback prevention

Answer first: AI reduces chargeback losses by improving evidence quality and preventing repeat offenders.

Disputes are a paperwork business disguised as a fintech workflow. AI can help by:

• categorizing disputes and predicting win probability,
• assembling evidence packs automatically,
• identifying friendly fraud patterns,
• flagging policy or UX issues driving “item not received” claims.

A practical stance: if your disputes team is mostly copy/pasting, you’re leaving money on the table.

4) Ops automation: reconciliation, exceptions, and alerts

Answer first: AI is increasingly an operations tool, not just a risk tool.

Maximalist fintech stacks generate an avalanche of exceptions:

• missing settlement files,
• mismatched amounts,
• duplicate captures,
• partial refunds,
• ledger vs processor drift.

AI can triage exceptions, prioritize the ones that affect cash position, and suggest likely root causes based on historical patterns. That’s how you scale without growing an army of analysts.

The compounder playbook for fintech infrastructure teams

Answer first: Compounders treat AI as a system capability: data discipline, experimentation, and governance—not a one-off model.

If you’re building in payments or buying payments infrastructure, here’s what I’ve found separates “AI theater” from real outcomes.

Build the data foundation before the model

Payments AI fails for predictable reasons: fragmented data, inconsistent identifiers, and missing feedback loops.

Minimum viable foundation:

• A unified event schema (auth, capture, refund, dispute, payout)
• Stable entity resolution (customer, device, merchant, bank account)
• Labeled outcomes (chargebacks, returns, fraud confirmed, manual review decisions)
• Clear latency tiers (real-time vs near-real-time vs batch)

If your teams can’t agree on what “customer” means, your model won’t save you.

Measure the right metrics (and don’t lie to yourself)

For payments infrastructure, the scoreboard is usually:

• Authorization rate (overall and by segment/corridor)
• Fraud loss rate (basis points of volume)
• False positive rate (good transactions blocked)
• Manual review rate (and review accuracy)
• Cost per transaction (including tooling and people)
• Time-to-detect new fraud patterns

A mature team reports these weekly, not quarterly.

Use AI where feedback loops are tight

AI works best where you can learn quickly:

• transaction approvals and declines,
• dispute outcomes,
• account takeover signals,
• payout failures and retries.

It’s harder (not impossible) where labels are ambiguous or delayed—like long-tail compliance risk.

Treat governance as a product feature

Answer first: In fintech, AI governance isn’t paperwork; it’s uptime and trust.

You need:

• audit trails for decisions,
• model monitoring for drift,
• clear override paths,
• human review tooling for edge cases,
• controls to prevent bias and unfair outcomes.

If regulators, banking partners, or enterprise customers can’t understand your controls, your growth ceiling arrives early.

People also ask: what changes in 2026?

Are we heading back to “growth at all costs”?

Not fully. Capital is more selective now. The premium is on efficient growth—companies that can expand while keeping fraud, losses, and ops costs under control.

Will AI replace risk teams?

No. It changes the job. The best risk teams become system designers and investigators, not spreadsheet jockeys. Humans still handle novel attacks, policy decisions, and partner negotiations. AI handles scale.

Where should a fintech start with AI in payments?

Start where you can get measurable wins in 60–90 days:

1. Improve fraud scoring with better features (device + behavior)
2. Add smarter routing based on historical approvals
3. Reduce manual review with targeted automation

Then invest in the longer projects (graph models, end-to-end orchestration optimization, automated reconciliation triage).

Fintech maximalism rewards teams who operationalize AI

Fintech maximalism isn’t about doing more for the sake of more. It’s about handling the reality that fintech has become dense, interconnected infrastructure.

If you’re building in the AI in Payments & Fintech Infrastructure world, the opportunity is straightforward: companies that treat AI as a core operational capability will compound—because they’ll approve more good transactions, stop more bad ones, and run leaner while scaling.

If you’re evaluating your 2026 roadmap right now, here’s the practical next step: map your payment decision points (routing, fraud, disputes, payouts, reconciliation) and identify where AI can reduce latency, cost, or loss without hurting conversion.

The big question heading into next year: when payments complexity keeps rising—and it will—will your stack learn faster than the fraudsters do?