Fintech 2026

USA – NEW YORK Trends and Developments Contributed by: Sam Davidson, Davidson Firm PLLC

New York’s Algorithmic Turn: AI Accountability, Charter Strategy and Embedded Finance in 2026 Introduction: federal reset, state assertiveness The regulatory landscape entering 2026 reflects a divergence between federal and state initiatives. One year into the current Trump administration, fed - eral policy has prioritised innovation and deregula - tion – specifically encouraging artificial intelligence (AI) technology development and digital asset growth. In December 2025, the administration issued an Execu - tive Order directing federal agencies to evaluate state AI laws and prepare legislative recommendations for federal preemption of state AI regulations deemed inconsistent with national innovation policy. As for banking and financial services regulation, enforce - ment intensity has significantly diminished, although the underlying statutes remain in force. New York, by contrast, has not moderated its regulato - ry assertiveness. In November 2025, the state enacted the first-of-its-kind Algorithmic Pricing Disclosure Act (NY Disclosure Act). This development, together with ongoing supervisory signals from the New York State Department of Financial Services (NYDFS), demon - strates its willingness to address emerging technol - ogy risks through targeted regulation and enforcement rather than waiting for federal action. This state-level assertiveness creates compliance obligations that extend beyond federal requirements, particularly for companies deploying consumer-facing AI systems. For fintech companies operating in or through New York, 2026 presents a question: how should AI strat - egy, regulatory strategy and embedded finance archi - tecture align in an environment where federal inno - vation policy and state-level requirements may not align? The Algorithmic Pricing Disclosure Act The NY Disclosure Act requires that where a price is set by an algorithm using a consumer’s personal data, the entity must provide a clear disclosure: “THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA”.

The NY Disclosure Act applies to entities domiciled or doing business in New York state and offering goods or services for personal, family or household purpos - es. It exempts: • financial institutions subject to the Gramm–Leach– Bliley Act (GLBA); • entities regulated under New York’s insurance law; and • certain subscription-based discount arrangements. The statute does not mandate bias testing, algorith - mic audits or governance documentation. It is nar - rowly focused on consumer transparency. Many fintech companies operate in or through New York, offering banking or payments services powered by sponsor bank partnerships and embedded finance structures. These arrangements have become foun - dational to modern fintech business models, enabling technology companies to offer financial products without obtaining direct charters or licensing. In these models, institutional responsibilities are distributed across multiple entities, creating nuanced questions about regulatory accountability: • A bank holds the charter and issues cards or books loans. • A fintech partner designs the product experience. • Pricing logic may reside in fintech controlled infra - structure. • Data flows across institutional boundaries. Where a bank qualifies as a GLBA financial institution, it falls within NY Disclosure Act’s exemption. However, a fintech that partners with the exempted bank may not. This creates a potential divergence between the exempted entity and the entity that controls pricing. Regulatory obligations often attach to the entity controlling the consumer interface, not merely to the underlying regulated institution. State banking regulators have already established this principle in the banking-as-a-service context: unchartered fin - tech platforms offering banking products through partner banks have been required by various state regulators to display conspicuous disclaimers stat - ing that the platform is a “fintech, not a bank”, even

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