Every year brings a new round of fintech predictions. Most of them are noise. But the trends emerging in 2026 are different — they’re structural. Open banking is no longer a regulatory experiment; it’s a baseline consumer expectation. AI has moved from internal tooling into customer-facing products. And fraud prevention is becoming a cross-institutional discipline that no single company can handle alone.
For legal tech professionals and compliance teams, these shifts create both opportunity and exposure. Open banking changes how financial data flows between institutions — which means new data processing agreements, updated privacy frameworks, and evolving regulatory obligations. AI in customer-facing financial products raises questions about liability, explainability, and consumer protection. And the fraud landscape is forcing companies to share intelligence across institutional boundaries, creating novel legal questions about data sharing, liability allocation, and regulatory jurisdiction.
Here’s what matters most — and what legal and compliance teams should be preparing for now.
Open Banking Has Become Non-Negotiable
The numbers are hard to argue with. Seventy-seven per cent of consumers now say their bank must be able to connect to the apps they already use. Sixty-six per cent say they’d consider switching their primary bank if it couldn’t. And more than 70% say they only trust banks that connect with fintech apps. Open banking has moved from a regulatory compliance exercise to a core consumer expectation — and any financial institution that treats it as optional is losing customers.
For legal teams, this means API data-sharing agreements are no longer edge cases — they’re becoming as routine as vendor contracts. The transition from PSD2 to PSD3 in Europe is tightening API performance requirements and expanding data access scope beyond payment accounts to investments, insurance, and pensions. In the US, the CFPB’s Section 1033 rulemaking is establishing open banking requirements through a different regulatory path. Compliance teams need to be across all of these simultaneously.
AI Is No Longer Internal — It’s Customer-Facing
Fifty-seven per cent of consumers now expect their fintech apps to use AI. This isn’t about chatbots that answer FAQ questions. It’s about AI systems that analyse spending patterns, predict cash flow problems, recommend financial products, and guide users through complex decisions. The shift from AI as an internal efficiency tool to AI as a customer-facing product creates a fundamentally different risk profile.
When AI is used internally to flag suspicious transactions, the consequences of a false positive are manageable — a human reviews it. When AI is directly advising a consumer on whether to take out a loan or switch bank accounts, the liability questions multiply. Who is responsible if the AI’s recommendation causes financial harm? How do you document that the model’s output was fair and non-discriminatory? What disclosures are required when a financial decision is AI-assisted versus AI-driven?
These aren’t theoretical questions. They’re the questions regulators are already asking — and the questions that will define the next wave of fintech compliance requirements.
Fraud Prevention Is Forcing Cross-Institutional Data Sharing
The US lost $12.5 billion to fraud in 2024, and generative AI could push those losses to $40 billion by 2027. The industry’s response is shifting from institution-level fraud detection to network-level intelligence — sharing signals across banks, fintech apps, and platform providers to spot patterns that no single company can see in isolation.
This is effective for fraud prevention but complex for legal teams. Cross-institutional data sharing raises questions about data minimisation, consent frameworks, processing agreements, and liability when shared intelligence leads to a false positive that harms a consumer. The legal infrastructure for network-based fraud prevention is still being built — and the companies contributing to that framework now will have an outsized influence on how it evolves.
The Infrastructure Cost Nobody Is Talking About
Beneath every trend on this list — open banking APIs, customer-facing AI, network-level fraud detection — sits a growing cloud infrastructure bill. Every API call, every model inference, every real-time fraud check runs on cloud compute. For fintech companies and the legal and compliance tech platforms that serve them, AI and cloud costs are becoming one of the largest variable expenses on the income statement.
The smart companies are managing this proactively. They’re routing simple AI tasks to cheaper models, batching workloads that don’t need real-time responses, and auditing their cloud commitments regularly. For companies scaling their AI and compliance infrastructure on Azure, there’s also an active secondary market — you can buy Azure credits at below-retail pricing from sellers with unused capacity, reducing your effective cloud cost without changing providers or renegotiating contracts.
What Legal and Compliance Teams Should Do Now
Update your API data-sharing agreements for PSD3/PSR and Section 1033 readiness. Build an internal framework for AI liability and disclosure in customer-facing financial products. Engage proactively with cross-institutional fraud intelligence initiatives — and make sure your data processing agreements support the data flows required. And audit your cloud commitments quarterly, because your infrastructure cost profile is changing faster than your annual budgets can track.
Frequently Asked Questions: Fintech Trends & Legal Implications
What percentage of consumers expect their bank to support open banking?
Seventy-seven per cent of consumers say their bank must connect to the apps they already use. Sixty-six per cent would consider switching banks if their current provider couldn’t. Open banking has moved from a regulatory exercise to a consumer retention issue.
How is PSD3 different from PSD2 for legal teams?
PSD3 shifts core payment rules from a directive (requiring national transposition) to a directly applicable regulation, eliminating the 27 different implementations that made cross-border compliance complex under PSD2. It also introduces explicit API performance requirements and works alongside the FIDA regulation to extend data sharing to investments, insurance, and pensions.
What are the liability risks of customer-facing AI in fintech?
When AI directly advises consumers on financial decisions, questions arise about responsibility for harmful recommendations, documentation of fairness and non-discrimination, and disclosure requirements for AI-assisted versus AI-driven decisions. These are the questions regulators are actively developing frameworks to address.
How much does fraud cost the US financial system?
The US lost $12.5 billion to fraud in 2024. Projections suggest generative AI could push annual fraud losses to $40 billion by 2027 as bad actors use AI to scale attacks faster than traditional detection tools can respond.
What is network-based fraud prevention?
It’s a cross-institutional approach where banks, fintech apps, and platform providers share fraud signals to detect patterns that no single company can spot in isolation — such as the same identity appearing across multiple apps or clusters of suspicious behaviour visible only at network scale.
What legal issues does cross-institutional fraud data sharing create?
It raises questions about data minimisation, consent frameworks, processing agreements between institutions, and liability when shared intelligence leads to false positives that harm consumers. The legal infrastructure for this model is still being developed across jurisdictions.
What is Section 1033 and why does it matter?
Section 1033 of the Dodd-Frank Act is the legal basis for the CFPB’s open banking rules in the United States. It requires financial institutions to share consumer financial data with authorised third parties at the consumer’s request — establishing US open banking requirements through a market-driven regulatory approach.
Are alternative payment methods growing?
Yes. P2P bank payments are projected to reach 184 million US mobile users by 2026. Pay-by-bank payments now account for 1.5% of all consumer transactions. The Clearing House reported a 28% increase in RTP transaction volume and a 405% increase in transaction value between Q4 2024 and Q4 2025.
How are lenders using alternative data for credit decisions?
Lenders are combining cash flow data, pay stubs, and utility bills with traditional credit scores to get a fuller picture of borrower capacity. API-based fintech tools and open banking regulations enable instant access to these alternative data sources, expanding financial access to the estimated 49 million Americans without traditional credit scores.
What is the biggest infrastructure cost driver in fintech right now?
AI inference — the cost of running AI models in production for customer-facing features, fraud detection, and compliance automation. Every API call, model query, and real-time fraud check runs on cloud compute, making AI and cloud costs one of the largest and fastest-growing variable expenses for fintech companies.