Plaid Teaches AI to Understand Financial Behavior
Two borrowers can have the same income, the same account balance, and even the same overdraft history, yet represent very different credit risks. Traditional models often struggle to tell them apart. Plaid believes foundation models can.
This year, the company introduced a two-layer foundation model architecture for financial data. The transaction model makes sense of individual financial events, while the sequential model looks at their order, cadence, and relationships, similar to how a large language model understands words in context rather than in isolation.
That richer understanding is improving lending, fraud detection, and payment risk across Plaid‘s network of more than 12,000 financial institutions and 9,000 apps. Its transaction foundation model improved income classification accuracy by 48%, while its sequential foundation model reduced credit default risk by 13.6% at the same approval rate.
The foundation can also feed into products such as LendScore, Plaid‘s real-time cash flow-based credit risk score, which reduced lending risk by 41% compared to a traditional benchmark, without sacrificing approvals.
Suddu Seshadri, Plaid‘s Head of Data & AI, explained that the company has evolved from a data connectivity company to an intelligence platform. This change was driven by the need to provide more intelligent finance solutions, where AI reasons on top of financial data.
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Plaid sees financial activity across 12,000 institutions and 9,000 apps, and with the digital financial ecosystem flourishing, the next phase will be intelligent finance. From its connectivity, Plaid has built dimensionality around identity, connections, and transactions, which has helped answer critical questions around fraud, payment risk, and credit.
The compounding effects of this network are fueling Plaid‘s next evolution. As Seshadri noted, the consumer demand for self-driving money is clear, with 86% of adults in the US already using AI to better understand and manage their money.
They were convinced that financial data needed foundation models, not just better traditional risk models, due to the characteristics of financial data, such as cryptic transaction descriptions and limited labeled data. Foundation models allow them to learn a reusable representation of financial activity once and adapt it across many tasks.
It combines network data with models built specifically for financial activity. The transaction model learns the economic meaning of individual events, while the sequential model learns order, timing, and changes in behavior over time.
This shared understanding can then improve product-specific systems across payments, fraud, and lending. For example, Plaid‘s transaction foundation work has improved income classification and loan payment detection.
One of the key benefits of Plaid‘s approach is that it can improve products as different as lending, fraud detection, and payments at the same time, by turning underlying concepts into reusable representations. Each product can then combine these representations with its own data, labels, thresholds, and controls.
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Seshadri noted that the fundamentals of good financial data have not changed, but what has changed is how much value models can extract from context. A transaction means more when you understand the activity that came before it, its timing, its relationship to other events, and whether behavior is changing.
The approach earned Plaid the Data Innovation Award at Tearsheet‘s AI Innovation Awards 2026. The award recognizes Plaid‘s innovative use of foundation models to improve financial services.
In the middle of this evolution, it’s clear that Plaid is playing a critical role in shaping the future of financial services, by providing a platform that enables the creation of more personalized, useful, and safe financial products. The use of foundation models is a key part of this effort, as it allows Plaid to extract more value from financial data and provide better insights to its customers.
For instance, financial inclusion efforts can benefit from Plaid‘s foundation models, as they can help provide more accurate and personalized financial services to underserved populations. This, in turn, can help reduce the risk of financial exclusion and promote greater economic stability.
Furthermore, the impact of Plaid‘s foundation models can be seen in the improvement of lending, fraud detection, and payment risk across its network. The reduction of credit default risk and the improvement of income classification accuracy are just a few examples of the benefits that Plaid‘s foundation models can bring to the financial services industry.
