Open Banking Fees: How AI Cuts Costs and Risk

A single metric explains why open banking fees are suddenly everyone’s problem: JPMorgan says its APIs are being “pinged” about 2 billion times per month, up from 1 billion in 2023, supporting roughly 11,000 apps (up from 5,000 two years ago). When usage doubles that fast, “free” data access stops feeling free.

That’s why open banking fees are sparking such a heated fight between banks, data aggregators, fintechs, and consumer advocates. Banks argue they’re carrying real infrastructure costs and security exposure. Aggregators and fintechs argue fees can become a tollbooth that blocks competition. And regulators—especially the CFPB—are in a strange spot, because the U.S. open banking framework is being rewritten while the agency itself faces uncertainty.

For this AI in Payments & Fintech Infrastructure series, I want to take a clear stance: the fees debate is less about whether data should cost money and more about whether the ecosystem can prove it’s using data responsibly. AI helps there—not as hype, but as plumbing: reducing unnecessary calls, enforcing consent, detecting misuse, and making cost allocation auditable.

Why open banking fees are showing up now

Answer first: Open banking fees are emerging because data access has shifted from occasional screen-scraping to industrial-scale API traffic, and someone has to pay for uptime, security, and governance.

Open banking in the U.S. grew up in a “mostly free” era. Aggregators normalized the idea that a consumer’s permission equals unlimited access. That worked when traffic was smaller and monitoring was cruder.

Now the economics are different. Banks are dealing with:

• Compute and bandwidth costs: high-frequency calls, broad data pulls, and “always-on” refresh cycles.
• Operational overhead: access management, incident response, vendor oversight, and customer support when something breaks.
• Security and liability exposure: every new connection increases the attack surface and the blast radius of credential or token misuse.

JPMorgan’s position (as described in the source material) is basically: aggregators are drawing more data than needed, too often, and that load is a tax on bank infrastructure. The bank moved to per-call pricing for digital data requests, rather than a flat monthly model.

Here’s the uncomfortable truth: usage-based pricing often shows up when governance is weak. If participants can’t prove they’re minimizing data scope and cadence, pricing becomes the blunt tool that forces discipline.

The hidden driver: “data minimization” is now a cost line

Banks and consumer advocates are both circling the same point from different angles: too much data is being captured, retained, and reused.

• Banks worry data is being stored and used by unregulated entities.
• Consumer advocates worry the rule will be relitigated or unenforced, leaving consumers with theoretical rights.
• Fintechs worry fees will make popular app experiences slower or more expensive.

If you want one phrase that predicts where this goes: data minimization becomes a pricing conversation.

The regulatory backdrop: the rule is in motion, the referee might be missing

Answer first: The open banking fee debate is happening while the CFPB reworks its open banking rule, and the enforcement outlook is uncertain—raising the odds of fragmented standards.

The CFPB’s open banking rule was designed to increase competition by giving consumers more direct control over their financial data: what’s shared, with whom, and for what purpose.

But the industry is in court, the rule is being revised, and the CFPB itself faces budget and leadership turbulence. When the regulator’s future looks shaky, market participants do what they always do: they build private ordering mechanisms (contracts, fees, bilateral agreements) to reduce uncertainty.

That dynamic matters for product leaders in fintech infrastructure:

• You can’t plan around one stable national standard.
• You should expect multiple “open banking flavors”—different bank terms, different pricing, different technical requirements.
• The winners won’t just be “connected everywhere.” They’ll be efficient, compliant, and measurable everywhere.

Where AI actually helps: reducing calls, proving consent, and pricing fairly

Answer first: AI can lower open banking costs and risk by optimizing data cadence, enforcing purpose-based access, detecting anomalous usage, and producing audit-ready evidence.

When people hear “AI in payments,” they often jump straight to fraud models. That’s part of it, but open banking infrastructure has a more basic bottleneck: waste. Wasteful pings. Wasteful scope. Wasteful retention.

AI-driven platforms can reduce that waste while making governance easier to prove.

1) AI-driven “cadence control” cuts API traffic without breaking UX

If a bank is getting hit 2 billion times per month, even a modest efficiency improvement is huge. The trick is to reduce calls without degrading experiences like budgeting apps, cash-flow underwriting, or account-to-account payments.

Practical pattern:

• Predict when fresh data is actually needed (based on user behavior, historical volatility, and risk level).
• Refresh on meaningful triggers (balance change thresholds, posted transactions, payroll patterns), not on a fixed timer.

This is where a lightweight ML model can outperform hard-coded rules. Rules create cliffs (refresh every X minutes). Models create nuance (refresh more when volatility rises).

Snippet-worthy reality: If your refresh policy is “every 5 minutes for everyone,” you’re paying premium pricing for low-value data.

2) Purpose-based access control: AI helps enforce “only what you need”

A core bank complaint in the debate is that aggregators may be pulling as much as 50% more customer information than needed. Whether that figure is universal or not, the pattern is familiar: broad access is easier than precise access.

AI can support purpose-based control in two ways:

• Classification: automatically tag data fields and endpoints by sensitivity and purpose (payments, underwriting, PFM, identity, disputes).
• Policy enforcement: block or step-up authentication when a request doesn’t match the approved purpose, scope, or consent window.

This turns “trust us” into “here’s the policy log.” Which becomes important if regulators ask questions—or if partners disagree about fees.

3) Anomaly detection for aggregators: treat API usage like fraud

In payments, anomaly detection flags unusual transactions. In open banking, the equivalent is unusual data access:

• Sudden increases in call volume
• Repeated calls for static data
• Unusual geographic or device patterns
• Multiple apps requesting the same data via the same token

Treating this as a fraud-like problem does two things:

1. It reduces risk (credential stuffing, token abuse, unauthorized retention).
2. It strengthens your negotiating position on fees—because you can prove you’re not the noisy neighbor.

4) Transparent cost allocation: AI makes fee models less arbitrary

Fees become contentious when they feel punitive or opaque. AI can help build pricing and showback that’s easier to defend:

• Attribute calls to apps, workflows, and user actions.
• Separate “customer-initiated” refreshes from background polling.
• Quantify the marginal cost of high-frequency endpoints.

If you’re a fintech or aggregator, this is not just about saving money. It’s about avoiding surprise unit economics when a large bank flips the pricing switch.

What fintech and payments leaders should do in Q1 2026

Answer first: Assume fees expand, design for efficiency, and build compliance evidence into your data layer—not as a legal afterthought.

This debate is landing right as many teams are planning 2026 roadmaps. Here’s what I’d prioritize if I were responsible for payments or fintech infrastructure.

A practical checklist for “fee-ready” open banking

1. Measure your ping-to-value ratio

   • Define what counts as value (payment initiation, underwriting decision, user-visible insights).
   • Track calls per value event. Reduce the denominator, not just the numerator.

2. Implement intelligent caching with clear TTL rules

   • Cache stable fields (account metadata, routing details) longer.
   • Use shorter TTL for volatile fields (available balance) only when required.

3. Move from time-based polling to event- or trigger-based refresh

   • If your providers support webhooks or change notifications, use them.
   • If not, emulate triggers with volatility scoring.

4. Store consent like you’ll have to prove it in a dispute

   • Capture purpose, scope, duration, and revocation.
   • Keep immutable logs and make them easy to export.

5. Minimize data retention by default

   • Keep what you need to deliver the service.
   • Set deletion and re-consent timers that align to product purpose.

6. Prepare for tiered access and step-up verification

   • Assume some data will become “premium” or require additional checks.
   • Build UX patterns that don’t crumble when step-up is required.

What this means for AI in payments specifically

AI isn’t just “nice to have” here. It’s the difference between:

• A platform that pays whatever the bank asks, and
• A platform that can show, with numbers, why its usage is disciplined.

And that discipline directly impacts adjacent payments capabilities:

• Account-to-account payments: cleaner, faster verification with fewer calls.
• Fraud detection: better signal quality when data collection is intentional.
• Routing optimization: lower operational cost means more room to choose the best rail (ACH, RTP, FedNow, cards) for each payment.

People also ask: will open banking fees hurt consumers?

Answer first: Fees will hurt consumers if they push fintechs to monetize more aggressively or degrade service, but they can help consumers if they reduce over-collection and force better governance.

The consumer impact depends on whether fees produce:

• Better behavior (less data pulled, less retained, fewer breaches), or
• Simple rent-seeking (fees as a competitive barrier).

My bet is we’ll see both in 2026. That’s why the operational layer matters so much: if your platform can reduce calls and prove compliance, you’re less exposed to fee shock and more resilient if rules change.

A better way to think about open banking fees

Open banking fees feel like a fight over dollars, but they’re really a fight over standards: how much data is appropriate, how often it should move, how long it should live, and who’s accountable when it’s mishandled.

Banks are right about one thing: unlimited, opaque access creates bad incentives. Fintechs are right about one thing too: unpredictable fees can throttle competition. The practical path between those positions is measurable efficiency and auditable governance.

If you’re building in payments or fintech infrastructure going into 2026, design your open banking layer so you can answer three questions instantly:

• Why did we request this data?
• Did the customer consent to this specific use?
• What did it cost, and did it create measurable value?

If you can answer those cleanly, open banking fees become manageable. If you can’t, they become a recurring tax—financial and operational.

Where do you think the market lands: standardized pricing across banks, or a patchwork of private “data toll roads” that fintechs have to navigate one integration at a time?