If the first wave of intangible asset finance was built on patents and trademarks, and the second on recurring revenue contracts, the third wave is being built on data.
The recognition of data as a productive asset — formalised by the 2025 revision to the System of National Accounts — marks a watershed moment. For the first time, the global statistical framework treats data as capital, sitting alongside software, R&D, and mineral exploration rights. This is not merely an accounting change. It signals a fundamental shift in how economies measure value, and it opens the door to data-backed financing structures that were previously theoretical.
During my 25 years in financial technology — building and scaling platforms at IG Group that processed billions of data points daily — I witnessed first-hand how proprietary data becomes the most defensible competitive advantage a company can build. Now the capital markets are beginning to catch up, recognising that data assets deserve the same financial treatment as the physical assets that traditional lending was built on.
Proprietary data is emerging as a new class of collateral for lending. The market is early but the structural foundations — valuation methodologies, governance frameworks, and regulatory recognition — are developing rapidly. Companies that invest in data governance and valuation now will be first in line when the lending market matures.
Data presents unique characteristics that distinguish it from every other collateral class — including other intangible assets.
Non-rivalry. Unlike a patent or a building, data can be used by multiple parties simultaneously without diminishing its value. This is powerful for the data holder but creates challenges for lenders: if the borrower retains a copy of the data after pledging it as collateral, what exactly has the lender secured?
Non-depletion. Data does not wear out with use. In fact, many data assets appreciate with use — machine learning models trained on proprietary data become more valuable as the dataset grows. This inverts the traditional collateral depreciation model.
Regulatory constraints. Personal data is subject to GDPR, CCPA, and equivalent regulations that restrict transfer, processing, and monetisation. A dataset that includes personal information cannot simply be seized and sold by a lender in a default scenario without navigating a complex regulatory landscape.
Contextual value. A dataset that is immensely valuable to one company may be worthless to another. This context-dependency complicates both valuation and secondary market development.
The 2025 SNA revision recognises data as a produced non-financial asset, alongside software and databases. This statistical recognition does not directly create accounting standards, but it signals the direction of travel for IFRS and US GAAP standard-setters. Companies should expect formal accounting guidance on data asset recognition within the next 3-5 years.
Not all data is suitable for collateral. Lenders evaluate data assets against five criteria that determine financeability.
| Criterion | Description | High Score | Low Score |
|---|---|---|---|
| Identifiability | Can the dataset be precisely defined and bounded? | Structured database with clear schema | Unstructured data across multiple systems |
| Revenue attribution | Does the data generate identifiable income? | Active licensing agreements | Internal use only, no external revenue |
| Transferability | Can the data be transferred to a third party? | Non-personal, proprietary, clean IP | Personal data subject to GDPR consent |
| Durability | Will the data retain value over the loan term? | Market data with ongoing collection | One-time survey data with expiration |
| Governance | Is the data governed, documented, and auditable? | Data governance framework in place | Undocumented, ungoverned data lake |
Companies that score highly across all five criteria have a genuine asset that lenders can evaluate. Companies that score poorly on transferability or governance — the two most common weaknesses — face significant barriers regardless of how valuable the data is operationally.
Three structural approaches are developing for data-backed financing.
The most practical structure today. The borrower grants the lender a contingent licence to the data, which activates only upon default. The data remains with the borrower during the life of the loan, and the lender's security interest is in the licence right rather than the data itself.
This avoids the regulatory complications of transferring personal data and addresses the non-rivalry problem — the lender does not need to possess the data, only the right to access or license it in an enforcement scenario.
Companies that generate revenue from data — through data licensing, data-enhanced SaaS products, or advertising against data-enriched audiences — can securitise those revenue streams. The data itself does not need to be transferred; the revenue rights are assigned to an SPV that issues debt backed by the income stream.
A market intelligence company licenses its proprietary datasets to 200 enterprise customers, generating £15M in annual data licensing revenue with 90%+ renewal rates. The company securitises the licensing revenue stream through an SPV, raising £10M in senior notes at 8% coupon. The data remains with the company; the SPV holds the right to the revenue streams. Investors underwrite the credit quality of the customer base and the stickiness of the data subscriptions.
An emerging model where data is placed into a trust with an independent trustee who holds the data on behalf of the beneficiaries (the lender and the borrower). The trust structure provides governance, control, and enforcement mechanisms that address many of the challenges of using data as traditional collateral.
This approach is particularly relevant for datasets that combine contributions from multiple parties or where regulatory constraints require independent oversight of data handling.
Data valuation is the most significant barrier to data-backed financing. Without credible valuation, lenders cannot price the risk, and borrowers cannot demonstrate the collateral value.
The income approach — specifically, the contribution analysis method — is the most relevant for lending valuations. It isolates the revenue attributable to the data asset by modelling the business with and without the dataset, quantifying the data's economic contribution.
The market approach, using comparable data licensing transactions, is developing as more data marketplace transactions become public. Platforms like AWS Data Exchange, Snowflake Marketplace, and Bloomberg are creating reference points for data pricing, though comparability remains challenging given the heterogeneity of data assets.
Opagio's Valuator includes data asset valuation capabilities that produce the structured, methodology-consistent assessments lenders require. The Calculator helps companies estimate the contribution of their data assets to overall business value.
Data valuation for lending requires the income approach — specifically, demonstrating how the data asset generates identifiable, attributable revenue. Cost-based valuations provide a floor but are insufficient for lending purposes. Companies must build the revenue attribution evidence before approaching lenders.
No lender will advance against data assets without a demonstrable data governance framework. Governance is not a compliance checkbox — it is the infrastructure that makes data financeable.
Catalogue all data assets with metadata: source, schema, volume, update frequency, personal data flags, licensing restrictions. This inventory is the equivalent of a physical asset register for lending purposes.
Document how data was collected, processed, and enriched. Establish chain of custody. This is the data equivalent of chain of title for real property — lenders need to know the data was lawfully obtained and the company has the right to use and pledge it.
Implement ongoing data quality monitoring: completeness, accuracy, timeliness, consistency. Data quality directly affects value — degraded data is impaired collateral.
Role-based access, encryption at rest and in transit, audit logging. Security is both a governance requirement and a lender covenant — data breaches destroy collateral value.
GDPR data protection impact assessments, consent records, processing agreements. Regulatory non-compliance is an existential risk to data collateral — a regulator can order data deletion.
Companies that aggregate and enrich financial market data, credit data, or transaction data hold some of the most financeable data assets. The revenue models are established (data subscriptions), the customer bases are diversified (financial institutions), and the regulatory frameworks are understood.
Clinical trial data, real-world evidence datasets, and genomic data represent enormous value. However, regulatory constraints (HIPAA, EU Clinical Trials Regulation) create significant transferability challenges. Data trust structures may be the most appropriate approach for healthcare data collateral.
The explosive demand for high-quality AI training data has created a new category of data asset. Companies that hold curated, labelled, proprietary training data are sitting on assets of rapidly increasing value. The challenge is valuation — AI training data pricing is volatile and use-case dependent.
Manufacturing, logistics, and infrastructure companies generate vast quantities of operational data from IoT sensors. This data has demonstrated value for predictive maintenance, supply chain optimisation, and digital twin applications. Industrial data is often less encumbered by privacy regulations than consumer data, making it more suitable for collateral structures.
An industrial IoT company has deployed 50,000 sensors across manufacturing facilities, accumulating three years of operational data with demonstrated predictive maintenance value. The data is non-personal, proprietary, and generates £5M annually through data-enhanced service contracts. A lender advances £3M against the data asset, secured by a contingent licence that activates upon default. The loan is serviced from the data-enhanced service revenue.
The regulatory landscape for data as a financial asset is evolving rapidly.
The EU Data Act (effective September 2025) establishes rights and obligations around data sharing and access, creating a clearer framework for data as a transferable asset. The UK's data protection framework, diverging from EU GDPR post-Brexit, is developing more permissive approaches to legitimate business use of data.
In Singapore, the Infocomm Media Development Authority has launched a data valuation framework pilot, working with financial institutions to develop standardised approaches to data asset assessment. This is the most advanced government-backed initiative to make data financeable.
The IAS 38 framework currently does not provide specific guidance on data asset recognition, though the principles (identifiability, control, future economic benefit) can be applied to data — see also capitalising intangible assets on the balance sheet and the IAS 38 FAQ. The IASB's research project on intangible assets — initiated in 2024 — is expected to address data explicitly. The Opagio intangible finance academy covers data asset valuation in detail.
Invest in data governance first. Before any financing conversation, ensure your data assets are inventoried, classified, governed, and documented. This is the non-negotiable foundation.
Build revenue attribution. If your data generates value only internally, start building the evidence base: model the revenue impact, explore data licensing opportunities, and create the revenue streams that lenders can underwrite. Use the Questionnaire to systematically identify data assets you may be undervaluing.
Get a data valuation. Commission a structured data asset valuation using income and cost approaches. Even if you are not yet seeking financing, the valuation establishes a baseline and identifies the most financeable components of your data portfolio.
Consider the structure. Data licence pledge structures are the most practical today. Revenue securitisation works if you have established data licensing income. Data trust structures may be appropriate for complex, multi-party datasets.
Engage early. The data-backed financing market is nascent, and lenders are still developing their evaluation frameworks. Companies that engage early with specialist lenders — bringing governance documentation, valuations, and structural proposals — will shape the market and secure the best terms.
Data-backed financing is no longer theoretical — it is emerging. The companies that will benefit first are those that treat data as a strategic asset today: governed, valued, and documented with the rigour that capital markets require. Start with a comprehensive data asset assessment and valuation to understand what you have and what it is worth to lenders.
Ivan Gowan is founder and CEO of Opagio. With 25 years in financial technology, including a decade leading technology and operations at IG Group (FTSE 250), Ivan brings deep operational experience in building data-intensive platforms that serve global financial markets. Meet the team.
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The frontier of structured finance is lending against the intangible assets that constitute AI company value: proprietary models, training datasets, and customer contracts. The question is no longer whether this is possible, but how to structure it and price the risk appropriately.
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