February 19, 2026
Why intelligent finance needs purpose-built AI infrastructure
Over the last decade, I’ve spent my career working on large-scale data networks. One pattern shows up consistently: the first challenge is connecting systems. The harder challenge comes later, when those systems need to learn from the data flowing through them.
Finance has reached that point.
Artificial intelligence is accelerating this transition. As AI systems become more capable, expectations for financial products are rising. Consumers expect experiences that are faster, more personalized, and more responsive to what’s actually happening in their financial lives, while maintaining control and transparency.
Just a decade ago, getting permissioned access to financial data was a hard problem for fintech builders. Open finance has largely solved that, allowing consumers to move data securely so builders can work with real financial signals rather than brittle integrations and manual workarounds.
The challenge today is turning connected data into understanding. Connectivity tells you what happened. Intelligent finance requires systems that recognize patterns, context, and outcomes as information changes and money moves over time. Meeting that challenge demands infrastructure purpose-built for finance, capable of delivering the insights, personalization, and adaptive decision-making consumers want, reliably at scale.
What intelligent finance requires from AI infrastructure
General-purpose AI is optimized to understand and generate language, not to reason about financial activity. It can summarize and classify effectively, but it struggles with the signals that actually drive outcomes in finance.
Those signals do not live in isolated transactions or static labels. They emerge at scale, across financial data, and over time. Understanding how spending patterns relate to income stability, or whether transaction sequences indicate real fraud, requires models that learn from how money actually moves.
In practice, intelligent finance works when three things line up:
Real financial data at scale. Limited data sets deliver limited results. Effective financial models require diverse activity across payments, lending, investing, and spending to surface meaningful patterns
Models trained specifically for financial activity. These models need feedback loops grounded in real outcomes. They must distinguish between legitimate complexity and actual risk, and adapt as patterns change.
Infrastructure built for production. The threshold for “good enough” is higher when systems are making financial decisions or blocking fraud. This requires security standards and the ability to govern and improve models safely once they are deployed.
Plaid is uniquely positioned across all three. Over time, we’ve built specialized, financial models that power our products and support the shift toward intelligent finance. We refer to this as Plaid Intelligence.
What this looks like at Plaid
More than half of Americans with a bank account have used Plaid, and over a million connections happen every day. Today, we apply modern AI techniques across this data network, and the benefits to businesses and consumers are measurable.
Fraud detection is getting sharper as models see behavior across institutions, products, and attack types. Our Trust Index now catches 30% more fraud than earlier versions by analyzing transaction history alongside network patterns.
Payment risk decisions also improve as models learn from cash flow patterns and account activity at scale. Plaid Signal has analyzed more than $185 billion in payments, helping customers approve more legitimate transactions while identifying risky ones.
These products are live and continue to improve as they learn from real outcomes. The models behind fraud detection and payment risk remain purpose-built for their domains. What is evolving is development of a network intelligence layer. Plaid Intelligence can learn from broad patterns across our network and strengthen our models with reusable signals and representations. We are investing in this foundation to ensure gains are durable and extend across products, not confined to a single model.
As part of that evolution, we’ve built our first transaction foundation model for finance. Trained on de-identified data across the Plaid Network, it develops a deeper understanding of individual transactions, identifying merchant identity, payment context, and financial attributes beyond raw bank descriptions.
Early results show meaningful gains in tasks like categorization and entity recognition. The model can more reliably distinguish between payroll deposits and peer-to-peer transfers, resolve fragmented merchant names across institutions, and identify subscription services even when naming conventions vary.
As this understanding strengthens, insights are expected to improve and become more accurate and resilient in concrete ways. Income verification becomes more dependable, as the system recognizes income stability across a range of pay patterns, not just traditional payroll formats. That reduces manual review and shortens decision times for the benefit of consumers.
Additionally, we’re continuing to develop intelligence to complement existing systems used for fraud detection and payment risk assessment.
We’re already seeing second-order effects, including fewer brittle edge cases and faster iteration cycles. As the foundation model improves, Plaid products improve alongside it, making it easier for customers to build consumer financial experiences that adapt to real life.
Why data quality matters as much as models
In machine learning, especially in finance, data quality and infrastructure matter as much as model architecture. Advanced models trained on narrow or poorly governed data can produce confident but unreliable results.
Consider fraud. If a system only learns from attacks targeting a single institution, it will recognize yesterday’s patterns but miss coordinated behavior that spans apps, accounts, and payment rails. Sophisticated fraud rarely appears obvious in isolation. It emerges in connections across the network. A broader network view changes what is detectable and helps identify the types of fraud many companies struggle with today, including first-party fraud, account takeovers, and coordinated fraud rings.
What infrastructure changes are required for agentic finance
There is a question I sometimes hear from partners: if AI agents begin handling financial tasks directly, what changes about the underlying infrastructure?
The key shift is that agents do not just analyze financial data, they act on it. They move money, block transactions, make eligibility decisions, and initiate workflows directly. That raises the cost of being wrong and makes reliability and governance far more important. Agents need more than raw transactions and balances. They require a system that understands financial context before taking action.
But intelligence alone is not enough. As agents act on behalf of consumers, people must be able to delegate decisions confidently, knowing their data and money are being used safely and within the scope of their permission. That requires identity, authentication, data stewardship, and the controls needed to deploy AI responsibly in financial systems.
What we are building at Plaid is both an intelligence layer and a trust layer for these agentic experiences. That combination makes agentic finance deployable in the real world.
What comes next
It’s still early, but the pace of change is unmistakable. Consumer expectations are shifting rapidly and capabilities that felt experimental a year ago are becoming standard. Network effects are compounding, and models improve faster once embedded in production systems.
I think we will look back on this period as the moment financial intelligence became durable infrastructure that businesses could actually rely on. Intelligence will be embedded in systems that banks depend on, regulators understand, and consumers trust.
For companies building in this environment, success will depend on more than adding AI features. It will require investing in the data foundations and trust layers needed to deploy intelligence safely at scale.
That is the work in front of us. Plaid is building the intelligence and trust infrastructure that intelligent finance requires, so developers, fintechs and banks can focus on creating better financial experiences for consumers.