Account Executive

Personetics has partnered with Plaid to give banks and credit unions a broader view of customer finances. The integration combines account information from a bank's own systems with data from other connected accounts, including transactions, liabilities and investments. Financial institutions can access Plaid's open banking connections and Personetics' AI-based personalisation through a single integration. Personetics will use its AI models to identify insights, recommendations and suggested actions, delivered through banks' existing digital channels. The partnership aims to help banks retain deposits and identify cross-sell opportunities. For consumers, the integration is designed to support tailored digital banking experiences, helping them save more, avoid fees and manage debt based on their complete financial picture across multiple accounts.

From connectivity to intelligence: How Plaid is teaching AI to understand financial behavior. * Plaid won the Data Innovation Award at Tearsheet's 2026 AI Innovation Awards. * Suddu Seshadri, Plaid's Head of Data & AI, discusses the firm's move from connectivity to intelligence and its bet on finance-specific AI foundation models. Javarya Kamran | August 18, 2026 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. This 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. The approach earned Plaid the Data Innovation Award at Tearsheet's AI Innovation Awards 2026. Tearsheet spoke with Suddu Seshadri, Plaid's Head of Data & AI, about why Plaid moved beyond data connectivity, what drove its decision to build foundation models for finance, and how AI is changing what makes financial data useful. Q: Plaid has evolved from a data connectivity company to an intelligence platform. What drove this change? Suddu Seshadri, Plaid: Plaid sees financial activity across 12,000 institutions and 9,000 apps. As fintech took shape, the first challenge was bringing financial data online and enabling people to access it and connect it. Plaid powered this shift, providing the infrastructure to make access possible. Now, with the digital financial ecosystem flourishing, the next phase will be intelligent finance, where AI reasons on top of this financial data. From its connectivity, Tearsheet has built dimensionality around identity, connections, and transactions. This helped build an incredibly deep network and helped Tearsheet answer critical questions around fraud, payment risk, and credit. The compounding effects of this network are fueling its next evolution. Q: What convinced Plaid that financial data needed foundation models, not just better traditional risk models? Suddu Seshadri, Plaid: The consumer demand for self-driving money is clear. In Spring 2026, Tearsheet released consumer research with the Harris Poll that revealed 86% of adults in the US were already using AI to better understand and manage their money. And they're not just using it; they want AI to do more for them. As a result, Tearsheet want to ensure its customers have access to the richest data available as they build new intelligent finance experiences. While traditional models can perform very well on a defined task, each one often has to rebuild the same underlying understanding of transactions, income, cash flow, and behavior. Financial data also has characteristics that make this difficult: transaction descriptions can be cryptic, labeled data is limited, and timing often changes the meaning of an event. Foundation models allow Tearsheet to learn a reusable representation of financial activity once and adapt it across many tasks. Q: What makes Plaid's approach to building its foundation models fundamentally different? Suddu Seshadri, Plaid: Tearsheet build models to help improve how financial services work: its models are designed to make financial products more personalized, useful, and safe. From a decade-plus of powering digital finance, Plaid has built a powerful financial data network. Now, Tearsheet is using it to build models that can reveal and make sense of the transactions and patterns that make up a financial life. Tearsheet combine network data with models built specifically for financial activity. Tearsheet is not asking a general-purpose model to guess from raw bank text. Its transaction model learns the economic meaning of individual events, while its sequential model learns order, timing, and changes in behavior over time. That shared understanding can then improve product-specific systems across payments, fraud, and lending. For example, its transaction foundation work has improved income classification by 48% and loan payment detection by 14%. Q: How does one shared AI foundation improve products as different as lending, fraud detection, and payments at the same time? Suddu Seshadri, Plaid: Lending, fraud, and payments rely on many of the same underlying concepts: income, recurring obligations, cash-flow stability, account behavior, and changes over time. The shared foundation turns those concepts into reusable representations. Each product then combines them with its own data, labels, thresholds, and controls. That means an improvement in the underlying representation can benefit several products, without treating a lending decision, a fraud decision, and a payments decision as the same problem. Q: Has AI changed what "good financial data" looks like? If so, how? Suddu Seshadri, Plaid: The fundamentals have not changed. Good financial data still needs to be permissioned, accurate, current, reliable, and traceable. 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. Sequence and relationships were always important. Modern models make it possible to use them at a much greater scale.

Personetics partners with Plaid to help banks win account primacy with Open Finance Intelligence. Personetics, the AI Cognitive Banking Platform, today announced a partnership with Plaid, the data network powering the digital financial ecosystem. The integration gives banks and credit unions something they've rarely had before: a comprehensive view of a customer's financial life, combining their own account data with transactions, liabilities, investments, and related financial activity from wherever a customer's money lives. That broader view turns fragmented financial data into actionable insights, helping institutions better understand what customers need and how to engage them at the right moments. The integration combines on-CUbroadcast and open banking data to give Personetics a more complete understanding of a customer's financial life. Personetics applies proven AI models, informed by years of experience helping financial institutions translate financial data into actionable engagement, to surface personalized financial insights, recommendations, and next best actions. Delivered directly within the bank's existing digital channels, these experiences help customers better understand their finances and take meaningful action. For banks and credit unions, the joint solution combines Plaid's open banking connectivity with Personetics' personalized engagement capabilities through a single integration path. The solution turns the data into actionable, personalized experiences that help financial institutions deepen customer relationships and remain at the center of their customers' financial lives. The outcomes follow: deposits retained rather than lost to other accounts, primacy reinforced over time, and new openings for cross-sell as a fuller financial picture comes into view. For consumers, this means a smarter, more personalized banking experience. Bringing together data from connected accounts helps consumers save more, avoid unnecessary fees, manage debt, and make more informed financial decisions, all within the digital banking experience they already use. Tools such as Personetics' Engagement Builder can help turn these open banking insights into personalized, needs-based campaigns, such as identifying opportunities to consolidate debt, reduce fees, or increase savings. "Banks and credit unions have spent years trying to earn a bigger share of their customers' financial lives, and open banking finally gives them a real way to do it," said Udi Ziv, CEO of Personetics. "When we can bring in data from accounts a customer holds elsewhere, we can surface insights that reflect their full financial picture and help them take smarter action based on it, not just what's sitting in one account. That's what builds primacy over time, and it's why we see this as such a natural fit with Plaid." Beyond a comprehensive set of ready-to-deploy Open Finance Intelligence, Personetics enables banks and credit unions to turn Plaid data into actionable, personalized experiences at scale. Through Engagement Builder, financial institutions can design and launch their own custom insights, triggers, and personalized engagement journeys, without having to build AI models from scratch. This flexibility allows them to quickly create differentiated experiences, from identifying debt consolidation opportunities and reducing fees to increasing savings and targeted financial guidance based on each customer's complete financial picture. "Financial institutions want to serve the whole customer via their own digital platforms. Historically, customers couldn't stay within those platforms and get a 360o view of their finances through a purpose-built UX," said Adam Yoxtheimer, Head of Partnerships at Plaid. "That's why we are excited to partner with Personetics, who have designed experiences that live within those platforms, enabling financial institutions to identify opportunities and take action across their customers' financial lives, not just what flows through a single account." By combining Plaid's open finance connectivity with Personetics' AI-driven personalization, the two companies are giving banks and credit unions a more complete way to understand their customers and act on that understanding through personalized experiences within the primary account relationship.