Written by PEER DATA
Introduction
Imagine a financial data provider licensing proprietary market sentiment scores to a fintech firm. The contract permits use in a traditional analytics platform, but the buyer later trains a generative AI model that outputs competing trading signals. Does this require special licensing terms, higher fees, or new safeguards?
Artificial intelligence (AI), encompassing machine learning (ML) and generative AI, has powered finance since the early 2010s. However, its unique traits such as training on vast datasets, creating novel outputs, incorporating user prompts, and complicating data retention set it apart from traditional technology.
As 2025 sees a surge in AI driven financial applications, from fraud detection to synthetic forecasts, both data providers and buyers must navigate these differences to protect intellectual property (IP) while fostering innovation. Drawing on ongoing cases like The New York Times Company v. OpenAI where courts have rejected some claims, but the suit continues as of today, with OpenAI defending AI training as fair use, this essay offers balanced guidance for licensing financial data. It also highlights tools such as PEER DATA’s Ledger (DBOR™) platform to ensure compliance and value for all parties.
In today’s digital era, data has emerged as one of the most valuable assets for a business. It can generate recurring revenue, reduce operational costs, and even serve as collateral for financing.
The Evolution of AI and Its Roots in Traditional Tech
AI is not new to finance. Machine learning has driven applications such as credit scoring and algorithmic trading since the early2010s, with neural networks documented in academic studies from 2010 to 2015.Generative AI, such as large language models that produce reports or forecasts, has surged more recently but still shares roots with traditional technology.
In traditional applications such as databases or analytics platforms like Aladdin, data is licensed for defined uses, including generating static reports or dashboards. Licenses specify access and scope and often require data purging after the contract ends, ensuring predictable outcomes and minimal IP risk.
AI diverges due to its dynamic capabilities. ML trains models to predict patterns, while generative AI creates novel content such as synthetic market insights. Both rely on user prompts and embedded data retention, which complicates licensing. The October 2025 BIS report on The Use of Artificial Intelligence for Policy Purposes highlights AI’s growing role in finance, from risk modeling to personalized advisory, and urges providers and buyers to address these nuances. For financial data stakeholders, understanding where AI aligns with or deviates from traditional technology is critical to crafting fair and effective licenses.
Licensing for AI vs. Traditional Technology: Key Differences
Licensing data for traditional technology is relatively straightforward. Buyers access datasets such as stock histories or economic indicators for specific purposes like portfolio reporting, with static outputs and clear retention rules, including purging data after the license term ends. Sellers set terms limiting redistribution and charge based on access or user count. IP risks remain low because outputs do not transform the original data.
AI licensing, which covers both ML and generative AI, introduces additional complexity.
Training requires ML and generative AI systems to ingest large datasets to learn patterns, such as training trading algorithms or large language models. Sellers must license reproduction rights broadly, while buyers need assurances that those rights clearly permit training activities.
Generative outputs differ from traditional analytics. ML produces analytical results such as risk scores, while generative AI creates novel content including market reports. This increases the risk of replicating proprietary compilations like ESG indexes. The ongoing New York Times v. OpenAI case illustrates how outputs that resemble original works can raise infringement concerns, even as courts continue to debate fair use.
Third party inputs also add complexity. Generative AI relies on user driven prompts, such as customized forecasts, which introduce variability absent from fixed ML models. Buyers need flexible terms, while sellers require safeguards against IP dilution.
Record retention further complicates licensing. Traditional licenses typically mandate data purging, but AI training embeds data within models, making deletion impractical. Sellers may require audit rights, while buyers need retention allowances to maintain model functionality.
Sellers may adjust pricing or terms for higher value AI use cases, such as generative trading signals or outputs that risk substituting for the seller’s products. These considerations align with discussions in the U.S. Copyright Office’s January 2025 report, Copyright and Artificial Intelligence, Part 2: Copyrightability. Buyers should negotiate clear AI permissions to avoid disputes like those in Getty Images v. Stability AI, where the UK High Court rejected Getty’s secondary copyright claims in November2025. When AI use mirrors traditional analytics, such as non-generative ML, standard licensing terms often remain sufficient.
Key Considerations for AI Data Licensing in Finance
Both buyers and sellers of financial data must address AI specific risks and opportunities.
IP protection remains a central concern. Sellers must safeguard copyrighted compilations such as sentiment scores and indexes from generative AI outputs that closely resemble their IP, as alleged in Warner Bros. Entertainment v. Midjourney, filed in September 2025. Buyers need licenses that explicitly permit training and outputs while avoiding replication of protected elements.
Compliance and privacy also require attention. Sensitive data, including transaction logs containing personal information, triggers GDPR and CCPA obligations. Sellers should enforce data minimization clauses, while buyers must ensure compliance during AI training, particularly in cross border contexts.
Market impact is another key issue. Sellers may require terms preventing AI outputs from competing directly with their products, such as free AI derived signals that substitute for paid services. Buyers should negotiate noncompetitive use clauses that preserve commercial value.
Record retention policies should be clearly defined. Sellers may prefer audit-based approaches rather than deletion requirements to protect IP, while buyers need practical retention rights to maintain AI integrity.
Traceability is increasingly important. Tools like PEERDATA’s DBOR™ track data lineage using distributed ledger backed immutability, allowing sellers to detect misuse and buyers to demonstrate compliance in real time.
Pricing models should reflect AI use cases. Sellers may adopt tiered pricing, charging more for generative AI with market impact and standard rates for analytical ML. Buyers should seek pricing that aligns with their actual scope of use.
Strategies for Financial Data Providers and Buyers
Both parties can adopt practical strategies to navigate AIlicensing effectively.
Tailored contracts are essential. Sellers should assess whether AI specific clauses are needed based on the products they license, including training permissions, output restrictions, retention policies, and audit rights. Buyers should clearly define their model objectives and ensure agreements cover training, outputs, and retention needs.
Technology solutions can support compliance. Observability platforms like PEER DATA’s DBOR™ enable monitoring of data usage, helping sellers identify potential misuse and allowing buyers to demonstrate adherence to license terms.
Regulatory alignment is also critical. Contracts should reflect U.S. fair use principles and EU AI Act transparency requirements, including prohibitions effective February 2025 and key obligations beginning in August 2025, to support global compliance.
Collaboration across the industry can help standardize approaches. Engagement with organizations such as the BIS or OECD, based on2025 guidance, may reduce friction and uncertainty in licensing negotiations.
Education remains important. Training legal, product, and commercial teams on distinctions between ML and generative AI, as well as data retention challenges, enables more informed and efficient negotiations.
Conclusion
Data licensing for AI in finance, encompassing both ML and generative AI, differs from traditional technology due to training practices, novel outputs, user inputs, and retention challenges. At the same time, alignment remains possible in purely analytical use cases. Sellers and buyers must develop tailored terms, leverage tools such as PEER DATA’s DBOR™, and align with evolving regulations like the EU AI Act to balance protection and innovation.
As discussions around U.S. and EU economic policy and innovation frameworks continue into 2026, collaborative standards may further simplify licensing. With the right approach, AI can become a shared opportunity for financial data stakeholders. Contact PEER DATA today for a DBOR™ demo to streamline your AI licensing processes.