Plaid has introduced LendScore 2 and LendScore Arc, extending cash-flow underwriting with a transformer-based model that learns from the order, timing, context and interaction of transactions. The company is also launching Instant Link, which lets consumers permission cash-flow data for future credit applications without reconnecting an account each time.

Arc uses Plaid's Sequential Foundation Model and combines sequence-derived signals with established underwriting features. Plaid says it has developed an explainability framework based on Integrated Gradients, translating model contributions into standardised reason codes, and is working with Fairplay on fair-lending testing and controls. Scores are delivered through Plaid's consumer reporting agency, with applicable rights under the US Fair Credit Reporting Act.

Plaid reports that its upgraded core model provides 42 per cent greater predictive power than traditional credit data alone, while specialised models produced different approval or delinquency improvements in internal testing. Those performance figures are vendor claims and may not transfer directly to another lender's portfolio, population or economic conditions.

The more durable significance is architectural. Cash-flow underwriting is moving from engineered variables towards sequence models that can detect patterns across time. That increases the importance of consent, adverse-action explanations, bias testing, drift monitoring and clear governance over which transaction patterns a lender is permitted to use.

Execution Level Governance- What audit-ready agent governance actually looks like
David Girvin, founder and CEO of Assury argues that model-in-the-loop review, AI governing AI, is fundamentally unreliable for regulated environments: even the best-performing models miss a meaningful share of violations, the reviewing model is typically provided by the same vendor being reviewed, and prompt injection or context poisoning can compromise both the acting agent and its supposed overseer simultaneously. He makes the case for deterministic, architecturally enforced controls instead, walking through Assury’s approach of autonomy zones, session risk accumulation, and credential starvation, which lets a compromised agent be cut off from its tools instantly rather than relying on time-boxed access. The conversation touches on why David is sceptical of just-in-time credentialing as a solution for agent security more broadly, since agent sessions don’t run on predictable human timescales, along with the current gap between how identity and security vendors are pitching agent protection and what he sees happening at the execution layer in practice. He also discusses the compliance and audit implications of probabilistic decision-making, arguing that regulated industries will increasingly need tamper-evident, hash-chained audit trails that can withstand scrutiny from auditors and regulators who are only beginning to understand agentic risk, and reflects on a named frontier lab’s own published framework as an example of the gap between research and practitioner reality. Elsewhere, David reflects candidly on building a bootstrapped security company in an increasingly crowded market, why he turned down aggressive VC funding to stay in control of the product, and what a credible third-party assessment of his own gateway would need to look like given that Assury sits directly in the execution path for every customer’s agents.
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